IIS_2023_1/romanova_adelina_lab_3/main.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn
from sklearn.preprocessing import (LabelEncoder,
                                   StandardScaler,
                                   MinMaxScaler,
                                   RobustScaler)
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, learning_curve, ShuffleSplit
from sklearn.model_selection import cross_val_predict as cvp
from sklearn import metrics
from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score, confusion_matrix, explained_variance_score

from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier


def str_features_to_numeric(data):
    # Преобразовывает все строковые признаки в числовые.

    # Определение категориальных признаков
    categorical_columns = []
    numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
    features = data.columns.values.tolist()
    for col in features:
        if data[col].dtype in numerics: continue
        categorical_columns.append(col)

    # Кодирование категориальных признаков
    for col in categorical_columns:
        if col in data.columns:
            le = LabelEncoder()
            le.fit(list(data[col].astype(str).values))
            data[col] = le.transform(list(data[col].astype(str).values))

    return data


def fe_creation(df):
    # Feature engineering (FE)
    df['age2'] = df['age']//10
    df['trestbps2'] = df['trestbps']//10
    df['chol2'] = df['chol']//60
    df['thalch2'] = df['thalch']//40
    df['oldpeak2'] = df['oldpeak']//0.4
    for i in ['sex', 'age2', 'fbs', 'restecg', 'exang']:
        for j in ['cp','trestbps2', 'chol2', 'thalch2', 'oldpeak2', 'slope']:
            df[i + "_" + j] = df[i].astype('str') + "_" + df[j].astype('str')
    return df


def acc_d(y_meas, y_pred):
    # Относительная погрешность между прогнозируемыми значениями y_pred и измеренными значениями y_meas
    return mean_absolute_error(y_meas, y_pred)*len(y_meas)/sum(abs(y_meas))


def acc_rmse(y_meas, y_pred):
    # Среднеквадратичная ошибка между прогнозируемыми значениями y_pred и измеренными значениями y_meas
    return (mean_squared_error(y_meas, y_pred))**0.5


def plot_cm(train_target, train_target_pred, valid_target, valid_target_pred, title):
    # Построение матриц ошибок

    def cm_calc(y_true, y_pred):
        cm = confusion_matrix(y_true, y_pred, labels=np.unique(y_true))
        cm_sum = np.sum(cm, axis=1, keepdims=True)
        cm_perc = cm / cm_sum.astype(float) * 100
        annot = np.empty_like(cm).astype(str)
        nrows, ncols = cm.shape
        for i in range(nrows):
            for j in range(ncols):
                c = cm[i, j]
                p = cm_perc[i, j]
                if i == j:
                    s = cm_sum[i]
                    annot[i, j] = '%.1f%%\n%d/%d' % (p, c, s)
                elif c == 0:
                    annot[i, j] = ''
                else:
                    annot[i, j] = '%.1f%%\n%d' % (p, c)
        cm = pd.DataFrame(cm, index=np.unique(y_true), columns=np.unique(y_true))
        cm.index.name = 'Actual'
        cm.columns.name = 'Predicted'
        return cm, annot


    # Построение матриц ошибок
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6), sharex=True)

    # Обучающие данные
    ax = axes[0]
    ax.set_title("for training data")
    cm0, annot0 = cm_calc(train_target, train_target_pred)
    sns.heatmap(cm0, cmap= "YlGnBu", annot=annot0, fmt='', ax=ax)

    # Тестовые данные
    ax = axes[1]
    ax.set_title("for test (validation) data")
    cm1, annot1 = cm_calc(valid_target, valid_target_pred)
    sns.heatmap(cm1, cmap= "YlGnBu", annot=annot1, fmt='', ax=ax)

    fig.suptitle(f'CONFUSION MATRICES for {title}')
    plt.savefig(f'CONFUSION MATRICES for {title}.png')
    plt.show()


def acc_metrics_calc(num, acc_all, model, train, valid, train_target, valid_target, title):
    # Этап выбора моделей
    # Расчет точности модели по различным показателям

    ytrain = model.predict(train).astype(int)
    yvalid = model.predict(valid).astype(int)
    print('train_target = ', train_target[:5].values)
    print('ytrain = ', ytrain[:5])
    print('valid_target =', valid_target[:5].values)
    print('yvalid =', yvalid[:5])

    num_acc = 0
    for x in metrics_now:
        if x == 1:
            #критерий точности score
            acc_train = round(metrics.accuracy_score(train_target, ytrain), 2)
            acc_valid = round(metrics.accuracy_score(valid_target, yvalid), 2)
        elif x == 2:
            # rmse критерий
            acc_train = round(acc_rmse(train_target, ytrain), 2)
            acc_valid = round(acc_rmse(valid_target, yvalid), 2)
        elif x == 3:
            # критерий относительной погрешности
            acc_train = round(acc_d(train_target, ytrain) * 100, 2)
            acc_valid = round(acc_d(valid_target, yvalid) * 100, 2)

        print('acc of', metrics_all[x], 'for train =', acc_train)
        print('acc of', metrics_all[x], 'for valid =', acc_valid)
        acc_all[num_acc].append(acc_train) #train
        acc_all[num_acc+1].append(acc_valid) #valid
        num_acc += 2

    #  Построение матриц
    plot_cm(train_target, ytrain, valid_target, yvalid, title)

    return acc_all


def plot_feature_importance(feature_importances, feature_names, model_name):
    import matplotlib.pyplot as plt
    import seaborn as sns

    # Создание цветовой палитры
    colors = sns.color_palette('viridis', len(feature_importances))

    # Сортировка индексов важностей признаков
    indices = feature_importances.argsort()[::-1]

    # Создание стильного барплота
    plt.figure(figsize=(12, 8))
    ax = sns.barplot(x=feature_importances[indices], y=feature_names[indices], palette=colors)

    # Добавление декораций
    plt.xlabel('Важность признака', fontsize=14)
    plt.ylabel('Признаки', fontsize=14)
    plt.title(f'Важность признаков в модели {model_name}', fontsize=16)
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)

    # Добавление цветовой шкалы и ее описания
    cbar = plt.colorbar(plt.cm.ScalarMappable(cmap='viridis'), ax=ax)
    cbar.set_label('Уровень важности', rotation=270, labelpad=15, fontsize=12)

    # Добавление сетки для лучшей читаемости
    plt.grid(axis='x', linestyle='--', alpha=0.6)

    # Сохранение графика в файл
    plt.savefig('feature_importance_plot.png', bbox_inches='tight')

    # Отображение графика
    plt.savefig(f'feature_importances_{model_name}.png')
    plt.show()


def plot_learning_curve(estimator, title, X, y, cv=None, axes=None, ylim=None,
                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5), random_state=0):
    fig, axes = plt.subplots(2, 1, figsize=(20, 10))

    if axes is None:
        _, axes = plt.subplots(1, 2, figsize=(20, 5))

    axes[0].set_title(title)
    if ylim is not None:
        axes[0].set_ylim(*ylim)
    axes[0].set_xlabel("Training examples")
    axes[0].set_ylabel("Score")

    cv_train = ShuffleSplit(n_splits=cv_n_split, test_size=test_train_split_part, random_state=random_state)

    train_sizes, train_scores, test_scores, fit_times, _ = \
        learning_curve(estimator=estimator, X=X, y=y, cv=cv,
                       train_sizes=train_sizes,
                       return_times=True)

    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    fit_times_mean = np.mean(fit_times, axis=1)
    fit_times_std = np.std(fit_times, axis=1)

    # Plot learning curve
    axes[0].grid()
    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1,
                         color="g")
    axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r",
                 label="Training score")
    axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g",
                 label="Cross-validation score")
    axes[0].legend(loc="best")

    # Plot n_samples vs fit_times
    axes[1].grid()
    axes[1].plot(train_sizes, fit_times_mean, 'o-')
    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,
                         fit_times_mean + fit_times_std, alpha=0.1)
    axes[1].set_xlabel("Training examples")
    axes[1].set_ylabel("fit_times")
    axes[1].set_title("Scalability of the model")

    plt.savefig(f'{title}.png')

    plt.show()
    return


if __name__ == "__main__":
    # Загрузка данных
    # Преобразование данных и предобработка
    # Обучение моделей Decision Tree Classifier и Random Forest Classifier
    # Расчет метрик и построение графиков
    cv_n_split = 5
    random_state = 42
    test_train_split_part = 0.25

    metrics_all = {1: 'acc', 2 : 'rmse', 3 : 're'}
    metrics_now = [1, 2, 3]

    data = pd.read_csv("..//heart_disease_uci.csv")
    data['target'] = data['num']
    data = data.drop(columns=['id', 'dataset', 'ca', 'thal', 'num'])

    data = data[(data['chol'] <= 420) & (data['oldpeak'] >=0) & (data['oldpeak'] <=4)].reset_index(drop=True)
    data = data.dropna().reset_index(drop=True)
    print(data.info())

    data = str_features_to_numeric(data)
    data = data[data['target'].isin([0, 1])]  # приволим столбец с целевыми значениями к бинарному виду

    data = fe_creation(data)
    data = str_features_to_numeric(data)

    dataset = data.copy()  # original data
    target_name = 'target'
    target = data.pop(target_name)

    # Model standartization
    # The standard score of a sample x is calculated as:
    # z = (x - мат.ож.) / (стандартное отклонение)
    scaler = StandardScaler()
    data = pd.DataFrame(scaler.fit_transform(data), columns = data.columns)

    train, valid, train_target, valid_target = train_test_split(data, target, test_size=test_train_split_part, random_state=random_state)

    # list of accuracy of all model - amount of metrics_now * 2 (train & valid datasets)
    num_models = 6
    acc_train = []
    acc_valid = []
    acc_all = np.empty((len(metrics_now)*2, 0)).tolist()
    acc_all

    acc_all_pred = np.empty((len(metrics_now), 0)).tolist()
    acc_all_pred

    cv_train = ShuffleSplit(n_splits=cv_n_split, test_size=test_train_split_part, random_state=random_state)

    decision_tree = DecisionTreeClassifier()
    param_grid = {'min_samples_leaf': [i for i in range(2,12)]}
    decision_tree_CV = GridSearchCV(decision_tree, param_grid=param_grid, cv=cv_train, verbose=False)
    decision_tree_CV.fit(train, train_target)
    print(decision_tree_CV.best_params_)

    acc_all = acc_metrics_calc(0, acc_all, decision_tree_CV, train, valid, train_target, valid_target, title="Decision Tree Classifier")
    plot_learning_curve(decision_tree_CV, "Decision Tree", train, train_target, cv=cv_train)

    feature_importances_dt = decision_tree_CV.best_estimator_.feature_importances_
    plot_feature_importance(feature_importances_dt, data.columns, "Decision Tree")