花瓣轮廓：
分类与聚类

使用各种模型进行鸢尾花分类和聚类

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1. | 介绍 👋

🤔 数据集问题

鸢尾花分类项目是使用简单数据集实现机器学习模型的实际演示。数据集本身包含有关花瓣和萼片大小的信息，包括鸢尾属物种。通过分析鸢尾花的属性，例如花瓣和萼片测量值，旨在利用基本的机器学习技术来准确地对不同品种的鸢尾进行分类。本笔记本展示了对机器学习（监督和无监督）如何应用于现实场景（即使数据集不复杂）的基本理解。此外，该项目还展示了模型创建、训练、评估、生成预测结果以及在新数据上测试最佳模型的分步过程。

📌 笔记本目标

👨‍💻 机器学习模型

🧾 数据集描述

Variable Name	Description	Sample Data
Id	ID for observations (unique ID)	1; 2; ...
SepalLengthCm	Length of the sepal (in cm)	5.1; 4.9; ...
SepalWidthCm	Width of the sepal (in cm)	3.5; 3.0; ...
PetalLengthCm	Length of the petal (in cm)	1.4; 1.3; ...
PetalWidthCm	Width of the petal (in cm)	0.2; 0.4; ...
Species	Species name	Iris-setosa; Iris-versicolor; ...

---

2. | 安装和导入库 📚

# --- Installing Libraries ---
!pip install ydata-profiling
!pip install pywaffle
!pip install highlight-text# --- Importing Libraries ---
import numpy as np
import pandas as pd
import ydata_profiling
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
import warnings
import os
import yellowbrick
import joblibfrom ydata_profiling import ProfileReport
from highlight_text import fig_text
from random import sample
from numpy.random import uniform
from math import isnan
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler 
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier, NearestNeighbors
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.cluster import KMeans
from sklearn.metrics import classification_report, accuracy_score, davies_bouldin_score, silhouette_score, calinski_harabasz_score
from yellowbrick.classifier import PrecisionRecallCurve, ROCAUC, ConfusionMatrix
from yellowbrick.model_selection import LearningCurve, FeatureImportances
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
from yellowbrick.contrib.wrapper import wrap
from yellowbrick.style import set_palette
from pywaffle import Waffle

3. | 读取数据集 👓

# --- Importing Dataset ---
df = pd.read_csv("/kaggle/input/iris/Iris.csv")# --- Reading Train Dataset ---
print(clr.start+".: Imported Dataset :."+clr.end)
print(clr.color+"*" * 23)
df.head().style.background_gradient(cmap="Purples").hide()

4. | 初始数据集探索 🔍

# --- 数据集报告 ---
ProfileReport(df, title="Iris Dataset Report", minimal=True, progress_bar=False, samples=None, correlations=None, interactions=None, explorative=True, dark_mode=True, notebook={"iframe":{"height": "600px"}}, html={"style":{"primary_color": color_line}}, missing_diagrams={"heatmap": False, "dendrogram": False}).to_notebook_iframe()

# --- 相关图变量 ---
suptitle = dict(x=0.1, y=0.92, fontsize=13, weight="heavy", ha="left", va="bottom", fontname=font_main)
title = dict(x=0.1, y=0.89, fontsize=8, weight="normal", ha="left", va="bottom", fontname=font_alt)
xy_label = dict(size=6)
highlight_textprops = [{"weight":"bold", "color": colors[0]}]# --- 相关图数据框 ---
df_corr = df.drop(columns=["Species"])# --- 相关图（热图）---
mask = np.triu(np.ones_like(df_corr.corr(), dtype=bool))
fig, ax = plt.subplots(figsize=(7, 6))
sns.heatmap(df_corr.corr(), mask=mask, annot=True, cmap=color_map, linewidths=0.2, cbar=False, annot_kws={"size": 7}, rasterized=True)
yticks, ylabels = plt.yticks()
xticks, xlabels = plt.xticks()
ax.set_xticklabels(xlabels, rotation=0, **xy_label)
ax.set_yticklabels(ylabels, **xy_label)
ax.grid(False)
fig_text(s="Variables Correlation Map", **suptitle)
fig_text(s="Most features in the dataset are <strongly correlated> to each other.", highlight_textprops=highlight_textprops, **title)
plt.tight_layout(rect=[0, 0.04, 1, 1.01])
plt.gcf().text(0.84, 0.03, "kaggle.com/caesarmario", style="italic", fontsize=5)
plt.show();

5. | EDA 📈

5.1 | 数值变量配对图

# --- EDA 1: Variables ---
suptitle = dict(x=0.05, y=1.07, fontsize=20, weight="heavy", ha="left", va="bottom", fontname=font_main)
title = dict(x=0.05, y=1.05, fontsize=12, weight="normal", ha="left", va="bottom", fontname=font_alt)
highlight_textprops = [{"weight":"bold", "color": colors[0]}, {"weight":"bold", "color": colors[0]}]# --- Display EDA 1 ---
with sns.axes_style("white"):eda1=sns.pairplot(df, hue="Species", diag_kind="kde", palette=color_eda, markers=["o", "s", "D"], vars=column_list2)
eda1_handles, eda1_labels = eda1._legend_data.values(), eda1._legend_data.keys()
eda1._legend.remove()
eda1.fig.legend(handles=eda1_handles, labels=eda1_labels, loc="upper center", ncol=3, bbox_to_anchor=(0.45, 1.03))
fig_text(s="Pairplot of Numerical Variables", **suptitle)
fig_text(s="<Iris-setosa> can be easily identified since it is <completely linearly seperated>.", highlight_textprops=highlight_textprops, **title)
plt.gcf().text(0.73, -0.01, "kaggle.com/caesarmario", style="italic", fontsize=8)
plt.show();

5.2 | 萼片长度和萼片宽度联合图

# --- EDA 2: Variables ---
suptitle = dict(x=0.1, y=1.05, fontsize=16, weight="heavy", ha="left", va="bottom", fontname=font_main)
title = dict(x=0.1, y=1.02, fontsize=8, weight="normal", ha="left", va="bottom", fontname=font_alt)
highlight_textprops = [{"weight":"bold", "color": colors[0]}, {"weight":"bold", "color": colors[0]}]# --- Display EDA 2 ---
with sns.axes_style("white"):eda2=sns.jointplot(x="SepalLengthCm", y="SepalWidthCm", data=df, hue="Species", palette=color_eda)
eda2.fig.set_size_inches((7, 5))
fig_text(s="Sepal Length and Sepal Width Jointplot", **suptitle)
fig_text(s="<Iris-setosa> is more likely to have a <short sepal length but a broader sepal breadth>.", highlight_textprops=highlight_textprops, **title)
plt.gcf().text(0.77, -0.02, "kaggle.com/caesarmario", style="italic", fontsize=8)
plt.show();

5.3 | 数值变量分布

# --- EDA 3 Variables ---
tick_params_prompt = dict(labelsize=8, length=5, width=1.5, bottom="on", color=color_line)
plot_style = dict(edgecolor=scatter_color_edge, s=3, alpha=0.7)
tick_params = dict(length=3, width=1, color=color_line)
xy_label = dict(fontsize=9, weight="bold")
highlight_textprops = [{"weight": "bold", "color": colors[0]}, {"weight": "bold", "color": colors[0]}]
suptitle = dict(x=0.12, y=0.932, fontsize=18, weight="heavy", ha="left", va="bottom", fontname=font_main)
title = dict(x=0.12, y=0.92, fontsize=10, weight="normal", ha="left", va="bottom", fontname=font_alt)# --- EDA 3 Function ---
def boxplot_figure(iris_type):df_eda3 = df[column_list2][df.Species==iris_type]if iris_type == "Iris-setosa": ax_num = 0elif iris_type == "Iris-versicolor": ax_num = 1elif iris_type == "Iris-virginica": ax_num = 2fig = sns.boxplot(x="value", y="variable", data=pd.melt(df_eda3), ax=axs[ax_num], palette=colors, boxprops=dict(alpha=0.9), linewidth=0.75)fig = sns.swarmplot(x="value", y="variable", data=pd.melt(df_eda3), ax=axs[ax_num], palette=colors, linewidth=0.5, size=3.5, **plot_style)fig.set_title(f"{iris_type}\n", fontweight="heavy", fontsize="11")fig.set_ylabel("Variables\n", **xy_label)if iris_type in ["Iris-setosa", "Iris-versicolor"]:fig.set_xlabel("", **xy_label)else:fig.set_xlabel("\nValues (in cm)", **xy_label)fig.grid(axis="y", alpha=0, zorder=2)fig.grid(axis="x", which="major", alpha=0.3, color=color_grid, linestyle="dotted", zorder=1)for spine in fig.spines.values(): spine.set_color("None")for spine in ["bottom", "left"]:fig.spines[spine].set_visible(True)fig.spines[spine].set_color(color_line)fig.tick_params(axis="both", which="major", left="on", **tick_params_prompt)fig.tick_params(axis="x", which="minor", **tick_params_prompt)plt.gcf().text(0.76, 0.06, "kaggle.com/caesarmario", style="italic", fontsize=8)# --- Display EDA 3 ---
fig, axs = plt.subplots(3, 1, figsize=(10, 16), sharex=True, sharey=True)
for iris_type in list(df["Species"].unique()): boxplot_figure(iris_type)
fig_text(s="Numerical Variables Distributions", **suptitle)
fig_text(s="<Iris-setosa> is having smaller features and less distributed while <Iris-virginica is the opposite>.", highlight_textprops=highlight_textprops, **title)
plt.show();

6. | 数据预处理 ⚙️

6.1 | 删除变量，特征分离和分裂🪓

# --- 从数据框中删除“Id” ---
df = df.drop("Id", axis=1)# --- 分离“物种”列 ---
x = df.drop(["Species"], axis=1)
y = df["Species"]# --- 分割数据集 ---
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

6.2 | 处理流程 🪠

# --- Pipeline ---
pipeline = Pipeline([("scaling", StandardScaler())
])# --- Apply Pipeline to Dataframe --- 
x_train_process = pipeline.fit_transform(x_train)
x_test_process = pipeline.fit_transform(x_test)

7. | 监督模型实施 🛠️

# --- 功能：模型拟合和性能评估 ---
def fit_ml_models(algo, algo_param, algo_name):# --- 算法流程 ---algo = Pipeline([("algo", algo)])# --- 应用网格搜索 ---model = GridSearchCV(algo, param_grid=algo_param, cv=10, n_jobs=-1, verbose=1)# --- 配件型号 ---print(clr.start+f".:. Fitting {algo_name} .:."+clr.end)fit_model = model.fit(x_train_process, y_train)# --- 模型最佳参数 ---best_params = model.best_params_print("\n>> Best Parameters: "+clr.start+f"{best_params}"+clr.end)# --- 最佳和最终参数 ---best_model = model.best_estimator_best_estimator = model.best_estimator_._final_estimatorbest_score = round(model.best_score_, 4)print(">> Best Score: "+clr.start+"{:.3f}".format(best_score)+clr.end)# --- 创建训练和测试预测 ---y_pred_train = model.predict(x_train_process)y_pred_test = model.predict(x_test_process)# --- 训练和测试准确率分数 ---acc_score_train = round(accuracy_score(y_pred_train, y_train)*100, 3)acc_score_test = round(accuracy_score(y_pred_test, y_test)*100, 3)print("\n"+clr.start+f".:. Train and Test Accuracy Score for {algo_name} .:."+clr.end)print("\t>> Train Accuracy: "+clr.start+"{:.2f}%".format(acc_score_train)+clr.end)print("\t>> Test Accuracy: "+clr.start+"{:.2f}%".format(acc_score_test)+clr.end)# --- 分类报告 ---print("\n"+clr.start+f".:. Classification Report for {algo_name} .:."+clr.end)print(classification_report(y_test, y_pred_test))# --- 图片设定 ---xy_label = dict(fontweight="bold", fontsize=12)grid_style = dict(color=color_grid, linestyle="dotted", zorder=1)title_style = dict(fontsize=14, fontweight="bold")tick_params = dict(length=3, width=1, color=color_line)bar_style = dict(zorder=3, edgecolor="black", linewidth=0.5, alpha=0.85)set_palette(color_yb)fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 14))# --- 混淆矩阵 ---conf_matrix = ConfusionMatrix(best_estimator, ax=ax1, cmap="BuPu")conf_matrix.fit(x_train_process, y_train)conf_matrix.score(x_test_process, y_test)conf_matrix.finalize()conf_matrix.ax.set_title("Confusion Matrix\n", **title_style)conf_matrix.ax.tick_params(axis="both", labelsize=10, bottom="on", left="on", **tick_params)for spine in conf_matrix.ax.spines.values(): spine.set_color(color_line)conf_matrix.ax.set_xlabel("\nPredicted Class", **xy_label)conf_matrix.ax.set_ylabel("True Class\n", **xy_label)conf_matrix.ax.xaxis.set_ticklabels(iris_list, rotation=0)conf_matrix.ax.yaxis.set_ticklabels(iris_list[::-1])# --- ROC AUC ---logrocauc = ROCAUC(best_estimator, classes=iris_list, ax=ax2, colors=color_yb)logrocauc.fit(x_train_process, y_train)logrocauc.score(x_test_process, y_test)logrocauc.finalize()logrocauc.ax.set_title("ROC AUC Curve\n", **title_style)logrocauc.ax.tick_params(axis="both", labelsize=10, bottom="on", left="on", **tick_params)logrocauc.ax.grid(axis="both", alpha=0.4, **grid_style)for spine in logrocauc.ax.spines.values(): spine.set_color("None")for spine in ["bottom", "left"]:logrocauc.ax.spines[spine].set_visible(True)logrocauc.ax.spines[spine].set_color(color_line)logrocauc.ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.12), ncol=2, borderpad=2, frameon=False, fontsize=10)logrocauc.ax.set_xlabel("\nFalse Positive Rate", **xy_label)logrocauc.ax.set_ylabel("True Positive Rate\n", **xy_label)# --- Learning Curve ---lcurve = LearningCurve(best_estimator, scoring="f1_weighted", ax=ax3, colors=color_yb)lcurve.fit(x_train_process, y_train)lcurve.finalize()lcurve.ax.set_title("Learning Curve\n", **title_style)lcurve.ax.tick_params(axis="both", labelsize=10, bottom="on", left="on", **tick_params)lcurve.ax.grid(axis="both", alpha=0.4, **grid_style)for spine in lcurve.ax.spines.values(): spine.set_color("None")for spine in ["bottom", "left"]:lcurve.ax.spines[spine].set_visible(True)lcurve.ax.spines[spine].set_color(color_line)lcurve.ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.12), ncol=2, borderpad=2, frameon=False, fontsize=10)lcurve.ax.set_xlabel("\nTraining Instances", **xy_label)lcurve.ax.set_ylabel("Scores\n", **xy_label)# --- 特征重要性或精确召回曲线 ---try:feat_importance = FeatureImportances(best_estimator, labels=column_list2, ax=ax4, colors=color_yb_importance)feat_importance.fit(x_train_process, y_train)feat_importance.finalize()feat_importance.ax.set_title("Feature Importances\n", **title_style)feat_importance.ax.tick_params(axis="both", labelsize=10, bottom="on", left="on", **tick_params)feat_importance.ax.grid(axis="x", alpha=0.4, **grid_style)feat_importance.ax.grid(axis="y", alpha=0, **grid_style)for spine in feat_importance.ax.spines.values(): spine.set_color("None")for spine in ["bottom"]:feat_importance.ax.spines[spine].set_visible(True)feat_importance.ax.spines[spine].set_color(color_line)feat_importance.ax.set_xlabel("\nRelative Importance", **xy_label)feat_importance.ax.set_ylabel("Features\n", **xy_label)except:prec_curve = PrecisionRecallCurve(best_estimator, ax=ax4, ap_score=True, iso_f1_curves=True)prec_curve.fit(x_train_process, y_train)prec_curve.score(x_test_process, y_test)prec_curve.finalize()prec_curve.ax.set_title("Precision-Recall Curve\n", **title_style)prec_curve.ax.tick_params(axis="both", labelsize=10, bottom="on", left="on", **tick_params)for spine in prec_curve.ax.spines.values(): spine.set_color("None")for spine in ["bottom", "left"]:prec_curve.ax.spines[spine].set_visible(True)prec_curve.ax.spines[spine].set_color(color_line)prec_curve.ax.legend(loc="upper center", bbox_to_anchor=(0.5, -0.12), ncol=2, borderpad=2, frameon=False, fontsize=10)prec_curve.ax.set_xlabel("\nRecall", **xy_label)prec_curve.ax.set_ylabel("Precision\n", **xy_label)plt.suptitle(f"\n{algo_name} Performance Evaluation Report\n", fontsize=18, fontweight="bold")plt.gcf().text(0.88, 0.02, "kaggle.com/caesarmario", style="italic", fontsize=10)plt.tight_layout();return acc_score_train, acc_score_test, best_score

7.1 | Logistic Regression 逻辑回归

算法code如下

# --- 逻辑回归参数 ---
parameter_lr = {"algo__solver": ["liblinear", "newton-cg"], "algo__C": [0.001, 0.01, 0.1, 0.5, 1]
}# --- 逻辑回归算法 ---
algo_lr = LogisticRegression(penalty="l2", random_state=42)# --- 应用逻辑回归 ---
acc_score_train_lr, acc_score_test_lr, best_score_lr = fit_ml_models(algo_lr, parameter_lr, "Logistic Regression")

7.2 | Gaussian Naive Bayes 高斯朴素贝叶斯

算法code如下

# --- Gaussian NB Parameters ---
parameter_gnb = {"algo__var_smoothing": [1e-2, 1e-3, 1e-4, 1e-5, 1e-6]}# --- Gaussian NB Algorithm ---
algo_gnb = GaussianNB()# --- Applying Gaussian NB ---
acc_score_train_gnb, acc_score_test_gnb, best_score_gnb = fit_ml_models(algo_gnb, parameter_gnb, "Gaussian Naive Bayes")

7.3 | Support Vector Machine (SVM) 支持向量机

算法code如下

# --- SVM Parameters ---
parameter_svc = [{"algo__kernel": ["rbf"], "algo__gamma": np.arange(0.1, 1, 0.1), "algo__C": np.arange(0.1, 1, 0.1)}, {"algo__kernel": ["linear"], "algo__C": np.arange(0.1, 1, 0.1)}, {"algo__kernel": ["poly"], "algo__degree" : np.arange(1, 10, 1), "algo__C": np.arange(0.1, 1, 0.1)}
]# --- SVM Algorithm ---
algo_svc = SVC(random_state=1, probability=True)# --- Applying SVM ---
acc_score_train_svc, acc_score_test_svc, best_score_svc = fit_ml_models(algo_svc, parameter_svc, "Support Vector Machine (SVM)")

7.4 | K-Nearest Neighbour (KNN) K最近邻

算法code如下

# --- KNN Parameters ---
parameter_knn = {"algo__n_neighbors": np.arange(2, 15, 1), "algo__leaf_size": np.arange(1, 11, 1)
}# --- KNN Algorithm ---
algo_knn = KNeighborsClassifier()# --- Applying KNN ---
acc_score_train_knn, acc_score_test_knn, best_score_knn = fit_ml_models(algo_knn, parameter_knn, "K-Nearest Neighbour (KNN)")

7.5 | Decision Tree 决策树

算法code如下

# --- Decision Tree Parameters ---
parameter_dt = {"algo__max_depth": np.arange(1, 11, 1)}# --- Decision Tree Algorithm ---
algo_dt = DecisionTreeClassifier(random_state=42)# --- Applying Decision Tree ---
acc_score_train_dt, acc_score_test_dt, best_score_dt = fit_ml_models(algo_dt, parameter_dt, "Decision Tree")

7.6 | 模型对比 👀

# --- 创建精度比较表 ---
df_compare = pd.DataFrame({"Model": ["Logistic Regression", "K-Nearest Neighbour", "Support Vector Machine", "Gaussian NB", "Decision Tree"] , "Accuracy Train": [acc_score_train_lr, acc_score_train_knn, acc_score_train_svc, acc_score_train_gnb, acc_score_train_dt], "Accuracy Test": [acc_score_test_lr, acc_score_test_knn, acc_score_test_svc, acc_score_test_gnb, acc_score_test_dt], "Best Score": [best_score_lr, best_score_knn, best_score_svc, best_score_gnb,best_score_dt]})# --- 创建比较表 ---
print(clr.start+f".:. Models Comparison .:."+clr.end)
print(clr.color+"*" * 26)
df_compare.sort_values(by="Best Score", ascending=False).style.apply(acc_train_vs_test, axis=1).hide()

Model	Accuracy Train	Accuracy Test	Best Score
K-Nearest Neighbour	95.833000	96.667000	0.958300
Support Vector Machine	96.667000	96.667000	0.958300
Logistic Regression	95.833000	96.667000	0.941700
Gaussian NB	95.000000	96.667000	0.941700
Decision Tree	95.833000	96.667000	0.941700

从上面训练和测试的准确率结果来看，所有模型都获得了满分，其中所有模型的得分都在0.94以上 。可以看到，得分最高的有两个模型，得分为0.95，分别是SVM和K-Nearest Neighbor。此外，所有模型的训练和测试准确率均超过 90%，这意味着所有模型都非常擅长对鸢尾花类型进行分类。然而，一些模型会出现一些欠拟合，因为测试精度高于测试，但 SVM 除外，它具有良好的拟合效果，因为训练和测试精度具有相同的值。


从ROC AUC曲线来看，所有模型的AUC值都接近1，这意味着所有模型都能很好地预测鸢尾花类型的分类。同样，混淆矩阵表明所有模型的预测结果是相同的。从所有模型的F1分数来看，所有模型都擅长区分Iris类型。所有模型的精度值也具有相同的结果，所有模型都可以100%准确地定义Setosa和Virginica，而只能90%准确度定义Versicolor。此外，所有模型中学习曲线的训练和验证分数都表明所有模型都遭受低方差和高偏差，因为线条保持靠近。换句话说，模型需要更好地概括数据。从上面的分析可以看出，SVM 是最好的模型，因为 SVM 得分很高，并且不会出现欠拟合。


令人惊讶的是，从 SVM 和其他模型特征重要性图中，萼片宽度是对鸢尾花类型进行分类的最重要特征。同时，花瓣长度、花瓣宽度和萼片长度等其他变量在鸢尾花类型分类中具有负值，这意味着某些模型假设这些变量可能对分类产生不利影响或没有影响。

8. | Miscellaneous 🧪

8.1 | 创建输出 📤

# --- 完整的流程：预处理器和 SVM ---
svm_pipeline = Pipeline([("pipeline", pipeline), ("algo", SVC(C=0.4, kernel="linear", random_state=1, probability=True))
])# --- Save Complete Pipeline (joblib and pickle) ---
file_name = "pipeline_iris_svm_caesarmario"
for ext in ["joblib", "pkl"]:joblib.dump(svm_pipeline, f"pipeline/{file_name}.{ext}")

# --- 用于创建测试输出数据框的数据框 ---
svm_pipeline.fit(x_train, y_train)
y_pred_svm = svm_pipeline.predict(x_test)
pred_target = pd.DataFrame(y_pred_svm, columns=["pred_target"])x_test_output = x_test.reset_index()
actual_target = y_test.to_frame(name="actual_target").reset_index()# --- 组合并创建测试输出数据框 ---
df_test_output = pd.concat([x_test_output, actual_target, pred_target], axis=1).drop("index", axis=1)# # --- 显示示例测试输出数据框 ---
print(clr.start+".: Sample Test Dataframe :."+clr.end)
print(clr.color+"*" * 28)
df_test_output.sample(n=6, random_state=24).style.apply(act_vs_pred, axis=1).hide()

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	actual_target	pred_target
6.50	3.20	5.10	2.00	Iris-virginica	Iris-virginica
5.50	3.50	1.30	0.20	Iris-setosa	Iris-setosa
6.70	2.50	5.80	1.80	Iris-virginica	Iris-virginica
6.20	2.20	4.50	1.50	Iris-versicolor	Iris-virginica
5.60	2.90	3.60	1.30	Iris-versicolor	Iris-versicolor
6.80	3.20	5.90	2.30	Iris-virginica	Iris-virginica

# --- Export to CSV and JSON Files ---
output_name = "test_data_iris_caesarmario"
df_test_output.to_csv(f"test_data/{output_name}.csv", index=False, sep=",", encoding="utf-8")
df_test_output.to_json(f"test_data/{output_name}.json", orient="index")

8.2 | 预测案例 🧐

# --- 创建预测案例数据框（100 行） ---
df_pred_case = create_prediction_case(x_train, 100)# --- 显示数据框 ---
print(clr.start+".: Prediction Case Dataframe :."+clr.end)
print(clr.color+"*" * 32)
df_pred_case.sample(n=6, random_state=24).style.background_gradient(cmap="RdPu").hide()

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
4.800000	2.200000	1.500000	1.600000
4.800000	3.700000	1.800000	0.700000
4.400000	2.400000	2.400000	0.900000
5.300000	4.200000	3.500000	0.100000
7.400000	3.800000	5.900000	2.200000
7.000000	3.500000	6.200000	0.200000

# --- 使用最佳模型创建预测 ---
y_pred_case = svm_pipeline.predict(df_pred_case)# --- 将预测案例数据框与预测相结合 ---
pred_case_target = pd.DataFrame(y_pred_case, columns=["pred_target"])
df_pred_case = pd.concat([df_pred_case, pred_case_target], axis=1)# --- 展示最终数据集  ---
print(clr.start+".: Final Prediction Case Dataframe :."+clr.end)
print(clr.color+"*" * 38)
df_pred_case.sample(n=6, random_state=24).style.apply(coloring_target_col).hide()

SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	pred_target
4.800000	2.200000	1.500000	1.600000	Iris-versicolor
4.800000	3.700000	1.800000	0.700000	Iris-setosa
4.400000	2.400000	2.400000	0.900000	Iris-versicolor
5.300000	4.200000	3.500000	0.100000	Iris-setosa
7.400000	3.800000	5.900000	2.200000	Iris-virginica
7.000000	3.500000	6.200000	0.200000	Iris-versicolor

# --- Export to CSV and JSON Files ---
pred_output_name = "pred_case_heart_disease_caesarmario"
df_pred_case.to_csv(f"pred_case/{pred_output_name}.csv", index=False, sep=",", encoding="utf-8")
df_pred_case.to_json(f"pred_case/{pred_output_name}.json", orient="index")

9. | 无监督模型实施 👥

9.1 | 霍普金斯测试🧪

# --- 霍普金斯测试（代码由 Matevž Kunaver 编写） ---
def hopkins(X):d = X.shape[1]n = len(X)m = int(0.1 * n)nbrs = NearestNeighbors(n_neighbors=1).fit(X)rand_X = sample(range(0, n, 1), m)ujd = []wjd = []for j in range(0, m):u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)ujd.append(u_dist[0][1])w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)wjd.append(w_dist[0][1])H = sum(ujd) / (sum(ujd) + sum(wjd))if isnan(H):print (ujd, wjd)H = 0return H# --- Perform Hopkins Test ---
hopkins_value = hopkins(x)
hopkins_result = 'Result: '+clr.start+'{:.4f}'.format(hopkins_value)+clr.end
print(clr.start+'.: Hopkins Test :.'+clr.end)
print(clr.color+'*' * 19+clr.end)
print(hopkins_result)
if  0.7 < hopkins_value < 0.99:print('>> From the result above,'+clr.color+' it has a high tendency to cluster (contains meaningful clusters)'+clr.end)print('\n'+clr.color+'*' * 31+clr.end)print(clr.start+'.:. Conclusions: Accept H0 .:.'+clr.end)print(clr.color+'*' * 31+clr.end)
else:print('>> From the result above,'+clr.color+' it has no meaningful clusters'+clr.end)print('\n'+clr.color+'*' * 31+clr.end)print(clr.start+'.:. Conclusions: Reject H0 .:.'+clr.end)print(clr.color+'*' * 31+clr.end)

.: Hopkins Test :.
*******************
Result: 0.8547
>> From the result above, it has a high tendency to cluster (contains meaningful clusters)*******************************
.:. Conclusions: Accept H0 .:.
*******************************

9.2 | PCA 🔧

# --- 转换成数组 ---
x_pca = np.asarray(x)# --- 应用主成分分析---
pca = PCA(n_components=2, random_state=24)
x_pca = pca.fit_transform(x_pca)

9.3 | 应用 K-Means

K-means聚类是一种简单的无监督学习算法，用于解决聚类问题。它遵循一个简单的过程，将给定的数据集分类为多个由字母“k”定义的集群，这些集群是预先固定的。然后将聚类定位为点，并将所有观测值或数据点与最近的聚类相关联，进行计算、调整，然后使用新的调整重新开始该过程，直到达到所需的结果。

🖼 K-Means Clustering by Pranshu Sharma


但是，在实施 K 均值之前，需要 使用肘部分数计算最佳簇数。此外， Calinski-Harabasz 索引将用于确定理想的簇数.

# --- 定义 K 均值函数 ---
def kmeans(X):# --- Figures Settings ---set_palette(color_yb_clustering)title = dict(fontsize=14, fontweight="bold", fontname=font_main)text_style=dict(fontweight="bold", fontname=font_main)fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))# --- K-Means ---model = KMeans(random_state=4)# --- 肘部评分 ---elbow_score = KElbowVisualizer(model, k=(1, 11), ax=ax1)elbow_score.fit(X)elbow_score.finalize()elbow_score.ax.set_title('Distortion Score Elbow\n', **title)elbow_score.ax.tick_params(labelsize=7)for text in elbow_score.ax.legend_.texts: text.set_fontsize(9)for spine in elbow_score.ax.spines.values(): spine.set_color('None')elbow_score.ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), borderpad=2, frameon=False, fontsize=8)elbow_score.ax.grid(axis='y', alpha=0.5, color=color_grid, linestyle='dotted')elbow_score.ax.grid(axis='x', alpha=0)elbow_score.ax.set_xlabel('\nK Values', fontsize=9, **text_style)elbow_score.ax.set_ylabel('Distortion Scores\n', fontsize=9, **text_style)# --- 肘部评分（Calinski-Harabasz 指数） ---elbow_score_ch = KElbowVisualizer(model, k=(2, 8), metric='calinski_harabasz', timings=False, ax=ax2)elbow_score_ch.fit(X)elbow_score_ch.finalize()elbow_score_ch.ax.set_title('Calinski-Harabasz Score Elbow\n', **title)elbow_score_ch.ax.tick_params(labelsize=7)for text in elbow_score_ch.ax.legend_.texts: text.set_fontsize(9)for spine in elbow_score_ch.ax.spines.values(): spine.set_color('None')elbow_score_ch.ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), borderpad=2, frameon=False, fontsize=8)elbow_score_ch.ax.grid(axis='y', alpha=0.5, color=color_grid, linestyle='dotted')elbow_score_ch.ax.grid(axis='x', alpha=0)elbow_score_ch.ax.set_xlabel('\nK Values', fontsize=9, **text_style)elbow_score_ch.ax.set_ylabel('Calinski-Harabasz Score\n', fontsize=9, **text_style)plt.suptitle('Iris Clustering using K-Means', fontsize=16, **text_style)plt.gcf().text(0.9, 0.05, 'kaggle.com/caesarmario', style='italic', fontsize=7, fontname=font_alt)plt.tight_layout()plt.show();# --- Calling K-Means Functions ---
kmeans(x_pca);

# --- 应用 K-Means ---
kmeans = KMeans(n_clusters=3, random_state=4)
y_kmeans = kmeans.fit_predict(x_pca)# --- 定义 K-Means 可视化工具和绘图 ---
def visualizer(X, kmeans, y_kmeans):# --- Figures Settings ---labels = ['Virginica', 'Versicolor', 'Setosa', 'Centroids']title=dict(fontsize=12, fontweight='bold', fontname=font_main)text_style=dict(fontweight='bold', fontname=font_main)scatter_style=dict(linewidth=0.65, edgecolor=scatter_color_edge, alpha=0.9)legend_style=dict(borderpad=2, frameon=False, fontsize=8)fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))# --- 剪影图 ---s_viz = SilhouetteVisualizer(kmeans, ax=ax1, colors=cluster_colors)s_viz.fit(X)s_viz.finalize()s_viz.ax.set_title('Silhouette Plots of Clusters\n', **title)s_viz.ax.tick_params(labelsize=7)for text in s_viz.ax.legend_.texts:text.set_fontsize(9)for spine in s_viz.ax.spines.values():spine.set_color('None')s_viz.ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), **legend_style)s_viz.ax.grid(axis='x', alpha=0.5, color=color_grid, linestyle='dotted')s_viz.ax.grid(axis='y', alpha=0)s_viz.ax.set_xlabel('\nCoefficient Values', fontsize=9, **text_style)s_viz.ax.set_ylabel('Cluster Labels\n', fontsize=9, **text_style)# --- 聚类分布 ---y_kmeans_labels = list(set(y_kmeans.tolist()))for i in y_kmeans_labels:ax2.scatter(X[y_kmeans==i, 0], X[y_kmeans == i, 1], s=50, c=cluster_colors[i], **scatter_style)ax2.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=70, c='#42A2FC', label='Centroids', **scatter_style)for spine in ax2.spines.values():spine.set_color('None')ax2.set_title('Scatter Plot Clusters Distributions\n', **title)ax2.legend(labels, bbox_to_anchor=(0.85, -0.05), ncol=5, **legend_style)ax2.grid(axis='both', alpha=0.5, color=color_grid, linestyle='dotted')ax2.tick_params(left=False, right=False , labelleft=False , labelbottom=False, bottom=False)ax2.spines['bottom'].set_visible(True)ax2.spines['bottom'].set_color(color_line)# --- 华夫饼图 ---unique, counts = np.unique(y_kmeans, return_counts=True)df_waffle = dict(zip(unique, counts))df_waffle = {"Virginica" if k == 0 else "Versicolor" if k == 1 else "Setosa" if k == 2 else k:v for k,v in df_waffle.items()}total = sum(df_waffle.values())wfl_square = {key: value for key, value in df_waffle.items()}wfl_label = {key: round(value/total*100, 2) for key, value in df_waffle.items()}ax3=plt.subplot(2, 2, (3,4))ax3.set_title('Percentage of Each Clusters\n', **title)ax3.set_aspect(aspect='auto')Waffle.make_waffle(ax=ax3, rows=6, values=wfl_square, colors=cluster_colors, labels=[f"{i} - ({k}%)" for i, k in wfl_label.items()], legend={'loc': 'upper center', 'bbox_to_anchor': (0.5, -0.05), 'ncol': 4, 'borderpad': 2, 'frameon': False, 'fontsize':10})# --- Suptitle & WM ---plt.suptitle('Iris Clustering using K-Means Result\n', fontsize=15, **text_style)plt.gcf().text(0.9, 0.03, 'kaggle.com/caesarmario', style='italic', fontsize=7)plt.tight_layout()plt.show();# --- 调用 K-Means 函数 ---
visualizer(x_pca, kmeans, y_kmeans);

# --- 评估聚类质量函数 ---
def evaluate_clustering(X, y):db_index = round(davies_bouldin_score(X, y), 3)s_score = round(silhouette_score(X, y), 3)ch_index = round(calinski_harabasz_score(X, y), 3)print(clr.start+'.: Evaluate Clustering Quality :.'+clr.end)print(clr.color+'*' * 34+clr.end)print('.: Davies-Bouldin Index: '+clr.start, db_index)print(clr.end+'.: Silhouette Score: '+clr.start, s_score)print(clr.end+'.: Calinski Harabasz Index: '+clr.start, ch_index)return db_index, s_score, ch_index# ---评估 K-Means 聚类质量 ---
db_kmeans, ss_kmeans, ch_kmeans = evaluate_clustering(x_pca, y_kmeans)

9.4 | 聚类分析 🤔

# --- 将 K 均值预测添加到数据框 ----
df['cluster_result'] = y_kmeans+1
iris_type = {1: "Virginica", 2: "Versicolor", 3: "Setosa"
}
df["cluster_result"] = df["cluster_result"].replace(iris_type).astype(str)# --- 从当前数据帧计算总体平均值 ---
df_profile_overall = pd.DataFrame()
df_profile_overall['Overall'] = df.describe().loc[['mean']].T# --- 总结每个簇的平均值 --- 
df_cluster_summary = df.groupby('cluster_result').describe().T.reset_index().rename(columns={'level_0': 'Column Name', 'level_1': 'Metrics'})
df_cluster_summary = df_cluster_summary[df_cluster_summary['Metrics'] == 'mean'].set_index('Column Name')# --- 组合两个数据框 ---
print(clr.start+'.: Summarize of Each Clusters :.'+clr.end)
print(clr.color+'*' * 33)
df_profile = df_cluster_summary.join(df_profile_overall).reset_index()
df_profile.style.background_gradient(cmap='RdPu').hide()

Column Name	Metrics	Setosa	Versicolor	Virginica	Overall
SepalLengthCm	mean	5.006000	5.883607	6.853846	5.843333
SepalWidthCm	mean	3.418000	2.740984	3.076923	3.054000
PetalLengthCm	mean	1.464000	4.388525	5.715385	3.758667
PetalWidthCm	mean	0.244000	1.434426	2.053846	1.198667

10. | 结论和未来的改进 📝