88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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import sklearn
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from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge
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from sklearn.preprocessing import (LabelEncoder,
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StandardScaler,
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MinMaxScaler,
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RobustScaler)
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from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, learning_curve, ShuffleSplit
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def str_features_to_numeric(data):
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# Преобразовывает все строковые признаки в числовые.
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# Определение категориальных признаков
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categorical_columns = []
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numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
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features = data.columns.values.tolist()
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for col in features:
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if data[col].dtype in numerics: continue
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categorical_columns.append(col)
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# Кодирование категориальных признаков
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for col in categorical_columns:
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if col in data.columns:
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le = LabelEncoder()
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le.fit(list(data[col].astype(str).values))
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data[col] = le.transform(list(data[col].astype(str).values))
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return data
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if __name__ == "__main__":
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data = pd.read_csv("..//heart_disease_uci.csv")
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data['target'] = data['trestbps']
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data = data.drop(columns=['id', 'dataset', 'trestbps'])
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data_wo_null = data.dropna()
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print(len(data_wo_null))
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encoded_data_wo_null = str_features_to_numeric(data_wo_null)
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print(len(encoded_data_wo_null))
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# Model standartization
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# The standard score of a sample x is calculated as:
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# z = (x - мат.ож.) / (стандартное отклонение)
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scaler = StandardScaler()
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new_data = pd.DataFrame(scaler.fit_transform(encoded_data_wo_null), columns = encoded_data_wo_null.columns)
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dataset = data_wo_null.copy() # original data
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target_name = 'target'
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target = data_wo_null.pop(target_name)
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test_train_split_part = 0.2
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random_state = 42
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train, valid, train_target, valid_target = train_test_split(new_data, target,
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test_size=test_train_split_part,
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random_state=random_state)
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reg = LinearRegression().fit(train, train_target)
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print("---"*15, " LinearRegression ", "---"*15)
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print(f"Accuracy: {reg.score(valid, valid_target)}")
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print(f"коэффициенты: {reg.coef_}")
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print(f"Смещение относительно начала координат (bias): {reg.intercept_}")
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SGD_reg = SGDRegressor(max_iter=1000, tol=1e-3)
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SGD_reg.fit(train, train_target)
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print("---"*15, " SGDRegressor ", "---"*15)
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print(f"Accuracy: {SGD_reg.score(valid, valid_target)}")
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print(f"коэффициенты: {SGD_reg.coef_}")
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print(f"Смещение относительно начала координат (bias): {SGD_reg.intercept_}")
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Ridge_clf = Ridge(alpha=1.0)
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Ridge_clf.fit(train, train_target)
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print("---"*15, " Ridge ", "---"*15)
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print(f"Accuracy: {Ridge_clf.score(valid, valid_target)}")
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print(f"коэффициенты: {Ridge_clf.coef_}")
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print(f"Смещение относительно начала координат (bias): {Ridge_clf.intercept_}")
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