visualizer.py

# �� Copilot �ͦ�
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import json
from typing import Dict, List

# �]�w����r��
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei', 'SimHei', 'Arial Unicode MS']
plt.rcParams['axes.unicode_minus'] = False

class RentalDataVisualizer:
    """���θ�Ƶ�ı�ƾ�"""
    
    def __init__(self, df: pd.DataFrame = None, analysis_results: Dict = None):
        """

        ��l�Ƶ�ı�ƾ�

        

        Args:

            df: ���DataFrame

            analysis_results: ���R���G�r��

        """
        self.df = df
        self.analysis_results = analysis_results
        self.colors = px.colors.qualitative.Set3
        
    def load_data(self, data_path: str):
        """���J���"""
        try:
            if data_path.endswith('.csv'):
                self.df = pd.read_csv(data_path, encoding='utf-8-sig')
            else:
                raise ValueError("�д���CSV�榡������ɮ�")
            print(f"���\���J {len(self.df)} ����ƥΩ��ı��")
        except Exception as e:
            print(f"���J��Ʈɵo�Ϳ��~: {e}")
    
    def load_analysis_results(self, results_path: str):
        """���J���R���G"""
        try:
            with open(results_path, 'r', encoding='utf-8') as f:
                self.analysis_results = json.load(f)
            print("���R���G���J���\")

        except Exception as e:

            print(f"���J���R���G�ɵo�Ϳ��~: {e}")

    

    def plot_price_distribution(self, save_path: str = "output/price_distribution.png"):

        """ø�s����������"""

        if self.df is None or 'price' not in self.df.columns:

            print("�L�kø�s���������ϡG�ʤָ��")

            return

            

        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

        

        # �����

        ax1.hist(self.df['price'], bins=20, alpha=0.7, color='skyblue', edgecolor='black')

        ax1.set_xlabel('���� (��)')

        ax1.set_ylabel('����ƶq')

        ax1.set_title('�������������')

        ax1.grid(True, alpha=0.3)

        

        # �c�ι�

        ax2.boxplot(self.df['price'], vert=True, patch_artist=True,

                   boxprops=dict(facecolor='lightgreen', alpha=0.7))

        ax2.set_ylabel('���� (��)')

        ax2.set_title('���������c�ι�')

        ax2.grid(True, alpha=0.3)

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"���������Ϥw�x�s: {save_path}")

    

    def plot_price_ranges(self, save_path: str = "output/price_ranges.png"):

        """ø�s�����϶�������"""

        if not self.analysis_results or 'price_distribution' not in self.analysis_results:

            print("�L�kø�s�����϶��ϡG�ʤ֤��R���G")

            return

            

        dist_data = self.analysis_results['price_distribution']

        

        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

        

        # ������

        bars = ax1.bar(dist_data['ranges'], dist_data['counts'], 

                      color=self.colors[:len(dist_data['ranges'])], alpha=0.8)

        ax1.set_xlabel('�����϶�')

        ax1.set_ylabel('����ƶq')

        ax1.set_title('�U�����϶�����ƶq')

        ax1.tick_params(axis='x', rotation=45)

        

        # �b�����W��ܼƭ�

        for bar, count in zip(bars, dist_data['counts']):

            height = bar.get_height()

            ax1.text(bar.get_x() + bar.get_width()/2., height + 0.5,

                    f'{count}', ha='center', va='bottom')

        

        # ����

        ax2.pie(dist_data['percentages'], labels=dist_data['ranges'], autopct='%1.1f%%',

               colors=self.colors[:len(dist_data['ranges'])], startangle=90)

        ax2.set_title('�����϶���Ҥ���')

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"�����϶��Ϥw�x�s: {save_path}")

    

    def plot_area_analysis(self, save_path: str = "output/area_analysis.png"):

        """ø�s�W�Ƥ��R��"""

        if self.df is None or 'area' not in self.df.columns:

            print("�L�kø�s�W�Ƥ��R�ϡG�ʤָ��")

            return

            

        # �����ŭ�

        area_data = self.df['area'].dropna()

        

        if len(area_data) == 0:

            print("�L�kø�s�W�Ƥ��R�ϡG�S�����Ī��W�Ƹ��")

            return

            

        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

        

        # ���I�� - �W�ƻP�������Y

        if 'price' in self.df.columns:

            valid_data = self.df.dropna(subset=['area', 'price'])

            if len(valid_data) > 0:

                ax1.scatter(valid_data['area'], valid_data['price'], 

                           alpha=0.6, color='coral', s=50)

                ax1.set_xlabel('�W��')

                ax1.set_ylabel('���� (��)')

                ax1.set_title('�W�ƻP�������Y')

                ax1.grid(True, alpha=0.3)

                

                # �K�[�Ͷսu

                z = np.polyfit(valid_data['area'], valid_data['price'], 1)

                p = np.poly1d(z)

                ax1.plot(valid_data['area'], p(valid_data['area']), "r--", alpha=0.8)

        

        # �W�Ƥ��������

        ax2.hist(area_data, bins=15, alpha=0.7, color='lightgreen', edgecolor='black')

        ax2.set_xlabel('�W��')

        ax2.set_ylabel('����ƶq')

        ax2.set_title('�W�Ƥ���')

        ax2.grid(True, alpha=0.3)

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"�W�Ƥ��R�Ϥw�x�s: {save_path}")

    

    def plot_price_per_ping(self, save_path: str = "output/price_per_ping.png"):

        """ø�s�C�W�������R��"""

        if self.df is None or 'price_per_ping' not in self.df.columns:

            print("�L�kø�s�C�W�����ϡG�ʤָ��")

            return

            

        price_per_ping_data = self.df['price_per_ping'].dropna()

        

        if len(price_per_ping_data) == 0:

            print("�L�kø�s�C�W�����ϡG�S�����Ī��C�W�������")

            return

            

        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

        

        # �C�W��������

        ax1.hist(price_per_ping_data, bins=20, alpha=0.7, color='gold', edgecolor='black')

        ax1.set_xlabel('�C�W���� (��/�W)')

        ax1.set_ylabel('����ƶq')

        ax1.set_title('�C�W��������')

        ax1.grid(True, alpha=0.3)

        

        # �c�ι�

        ax2.boxplot(price_per_ping_data, vert=True, patch_artist=True,

                   boxprops=dict(facecolor='orange', alpha=0.7))

        ax2.set_ylabel('�C�W���� (��/�W)')

        ax2.set_title('�C�W�����c�ι�')

        ax2.grid(True, alpha=0.3)

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"�C�W�����Ϥw�x�s: {save_path}")

    

    def plot_keywords_analysis(self, save_path: str = "output/keywords_analysis.png"):

        """ø�s����r���R��"""

        if not self.analysis_results or 'description_analysis' not in self.analysis_results:

            print("�L�kø�s����r���R�ϡG�ʤ֤��R���G")

            return

            

        desc_analysis = self.analysis_results['description_analysis']

        if 'keywords_frequency' not in desc_analysis:

            print("�L�kø�s����r���R�ϡG�ʤ�����r���")

            return

            

        keywords_data = desc_analysis['keywords_frequency']

        

        # �L�o�X���ƾڪ�����r

        filtered_keywords = {k: v for k, v in keywords_data.items() if v > 0}

        

        if not filtered_keywords:

            print("�S������������r���")

            return

            

        keywords = list(filtered_keywords.keys())

        frequencies = list(filtered_keywords.values())

        

        plt.figure(figsize=(12, 8))

        bars = plt.barh(keywords, frequencies, color=self.colors[:len(keywords)])

        plt.xlabel('�X�{����')

        plt.ylabel('����r')

        plt.title('����y�z����r�W�v���R')

        plt.grid(True, alpha=0.3, axis='x')

        

        # �b�����W��ܼƭ�

        for bar, freq in zip(bars, frequencies):

            width = bar.get_width()

            plt.text(width + 0.1, bar.get_y() + bar.get_height()/2.,

                    f'{freq}', ha='left', va='center')

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"����r���R�Ϥw�x�s: {save_path}")

    

    def create_interactive_dashboard(self, save_path: str = "output/dashboard.html"):

        """�Ыؤ��ʦ������O"""

        if self.df is None:

            print("�L�k�Ыػ����O�G�ʤָ��")

            return

            

        # �Ыؤl��

        fig = make_subplots(

            rows=2, cols=2,

            subplot_titles=('��������', '�W��vs����', '�����϶�����', '�C�W��������'),

            specs=[[{"secondary_y": False}, {"secondary_y": False}],

                   [{"type": "bar"}, {"secondary_y": False}]]

        )

        

        # 1. �������������

        fig.add_trace(

            go.Histogram(x=self.df['price'], name='��������', nbinsx=20,

                        marker_color='skyblue', opacity=0.7),

            row=1, col=1

        )

        

        # 2. �W��vs�������I��

        if 'area' in self.df.columns:

            valid_data = self.df.dropna(subset=['area', 'price'])

            if len(valid_data) > 0:

                fig.add_trace(

                    go.Scatter(x=valid_data['area'], y=valid_data['price'],

                             mode='markers', name='�W��vs����',

                             marker=dict(color='coral', size=8, opacity=0.6)),

                    row=1, col=2

                )

        

        # 3. �����϶�����

        if self.analysis_results and 'price_distribution' in self.analysis_results:

            dist_data = self.analysis_results['price_distribution']

            fig.add_trace(

                go.Bar(x=dist_data['ranges'], y=dist_data['counts'],

                      name='�����϶�', marker_color='lightgreen'),

                row=2, col=1

            )

        

        # 4. �C�W��������

        if 'price_per_ping' in self.df.columns:

            price_per_ping_data = self.df['price_per_ping'].dropna()

            if len(price_per_ping_data) > 0:

                fig.add_trace(

                    go.Histogram(x=price_per_ping_data, name='�C�W����', nbinsx=15,

                               marker_color='gold', opacity=0.7),

                    row=2, col=2

                )

        

        # ��s����

        fig.update_layout(

            title_text="���������s�ϯ��Υ������R�����O",

            title_x=0.5,

            height=800,

            showlegend=False

        )

        

        # ��s�b����

        fig.update_xaxes(title_text="���� (��)", row=1, col=1)

        fig.update_yaxes(title_text="����ƶq", row=1, col=1)

        fig.update_xaxes(title_text="�W��", row=1, col=2)

        fig.update_yaxes(title_text="���� (��)", row=1, col=2)

        fig.update_xaxes(title_text="�����϶�", row=2, col=1)

        fig.update_yaxes(title_text="����ƶq", row=2, col=1)

        fig.update_xaxes(title_text="�C�W���� (��/�W)", row=2, col=2)

        fig.update_yaxes(title_text="����ƶq", row=2, col=2)

        

        # �x�s���ʦ��Ϫ�

        fig.write_html(save_path)

        print(f"���ʦ������O�w�x�s: {save_path}")

    

    def generate_all_visualizations(self):

        """�ͦ��Ҧ���ı�ƹϪ�"""

        print("�}�l�ͦ���ı�ƹϪ�...")

        

        # �R�A�Ϫ�

        self.plot_price_distribution()

        self.plot_price_ranges()

        self.plot_area_analysis()

        self.plot_price_per_ping()

        self.plot_keywords_analysis()

        

        # ���ʦ������O

        self.create_interactive_dashboard()

        

        print("�Ҧ���ı�ƹϪ��ͦ������I")

    

    def create_summary_report(self, save_path: str = "output/summary_report.png"):

        """�ЫغK�n���i��"""

        if not self.analysis_results or 'basic_stats' not in self.analysis_results:

            print("�L�k�ЫغK�n���i�G�ʤ֤��R���G")

            return

            

        fig, ax = plt.subplots(figsize=(12, 8))

        ax.axis('off')

        

        # ���D

        fig.suptitle('���������s�ϯ��Υ������R�K�n���i', fontsize=20, fontweight='bold', y=0.95)

        

        # �򥻲έp��T

        stats = self.analysis_results['basic_stats']

        

        # �Ыؤ�r���e

        report_text = f"""
        
? �������p
? �`�����: {stats['total_properties']} ��
? ��ƽd��: 2�СB��h�B�q��j��

? �����έp
? ��������: {stats['price_stats']['mean']:,} ��
? ����Ư���: {stats['price_stats']['median']:,} ��
? �̧C����: {stats['price_stats']['min']:,} ��
? �̰�����: {stats['price_stats']['max']:,} ��
? �зǮt: {stats['price_stats']['std']:,} ��

? �����S�x
? �Ĥ@�|�����: {stats['price_stats']['q25']:,} ��
? �ĤT�|�����: {stats['price_stats']['q75']:,} ��
        """

        

        # �K�[���n�έp�]�p�G�����ܡ^

        if 'area_stats' in stats and stats['area_stats']:

            area_stats = stats['area_stats']

            report_text += f"""
? �W�Ʋέp
? �����W��: {area_stats['mean']} �W
? ����ƩW��: {area_stats['median']} �W
? �̤p�W��: {area_stats['min']} �W
? �̤j�W��: {area_stats['max']} �W
            """

        

        # �K�[�C�W�����έp�]�p�G�����ܡ^

        if 'price_per_ping_stats' in stats and stats['price_per_ping_stats']:

            pp_stats = stats['price_per_ping_stats']

            report_text += f"""
? �C�W�����έp
? �����C�W����: {pp_stats['mean']:,} ��/�W
? ����ƨC�W����: {pp_stats['median']:,} ��/�W
? �̧C�C�W����: {pp_stats['min']:,} ��/�W
? �̰��C�W����: {pp_stats['max']:,} ��/�W
            """

        

        # �K�[�}��]�p�G�����ܡ^

        if 'insights' in self.analysis_results:

            report_text += "\n\n? ���n�}��\n"

            for i, insight in enumerate(self.analysis_results['insights'], 1):

                report_text += f"? {insight}\n"

        

        # ��ܤ�r

        ax.text(0.05, 0.95, report_text, transform=ax.transAxes, fontsize=12,

               verticalalignment='top', fontfamily='monospace',

               bbox=dict(boxstyle="round,pad=0.5", facecolor="lightblue", alpha=0.8))

        

        plt.tight_layout()

        plt.savefig(save_path, dpi=300, bbox_inches='tight')

        plt.close()

        print(f"�K�n���i�w�x�s: {save_path}")



if __name__ == "__main__":

    # ���յ�ı�ƾ�

    visualizer = RentalDataVisualizer()

    

    # ���J���

    visualizer.load_data("output/rental_data.csv")

    visualizer.load_analysis_results("output/analysis_results.json")

    

    # �ͦ��Ҧ���ı�ƹϪ�

    visualizer.generate_all_visualizations()

    

    # �ЫغK�n���i

    visualizer.create_summary_report()