report.py

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

REPORT_DIR = f"cleaning_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
os.makedirs(REPORT_DIR, exist_ok=True)


def save_plot(fig, filename):
    fig.savefig(os.path.join(REPORT_DIR, filename), dpi=400, bbox_inches='tight')
    plt.close(fig)


def plot_heatmap(df, title):
    plt.figure(figsize=(12, 8))
    sns.heatmap(df.isnull(), cbar=False, cmap='Reds')
    plt.title(title)
    plt.tight_layout()
    save_plot(plt.gcf(), f'{title.lower().replace(" ", "_")}.png')


def plot_valid_data_percentage(original_df, cleaned_df, primary_key_column):
    # Get columns present in both DataFrames, excluding the primary key
    columns_to_plot = [col for col in original_df.columns if col in cleaned_df.columns and col != primary_key_column]

    original_valid = (original_df[columns_to_plot].notna().sum() / len(original_df)) * 100
    cleaned_valid = (cleaned_df[columns_to_plot].notna().sum() / len(cleaned_df)) * 100

    combined_data = pd.concat([original_valid, cleaned_valid], axis=1, keys=['Original', 'Cleaned']).fillna(0)

    plt.figure(figsize=(15, 8))

    x = range(len(combined_data))
    width = 0.35

    plt.bar(x, combined_data['Original'], width, label='Before Cleaning', alpha=0.8)
    plt.bar([i + width for i in x], combined_data['Cleaned'], width, label='After Cleaning', alpha=0.8)

    plt.xlabel('Columns')
    plt.ylabel('Percentage of Valid Data')
    plt.title('Percentage of Valid Data Before and After Cleaning')
    plt.xticks([i + width / 2 for i in x], combined_data.index, rotation=90)
    plt.legend()

    for i, v in enumerate(combined_data['Original']):
        plt.text(i, v, f'{v:.1f}%', ha='center', va='bottom', fontsize=6)
    for i, v in enumerate(combined_data['Cleaned']):
        plt.text(i + width, v, f'{v:.1f}%', ha='center', va='bottom', fontsize=6)

    plt.tight_layout()
    plt.savefig(os.path.join(REPORT_DIR, 'valid_data_percentage.png'))
    plt.close()

    # Print information about removed columns
    removed_columns = set(original_df.columns) - set(cleaned_df.columns)
    if removed_columns:
        print(f"The following columns were removed during the cleaning process: {', '.join(removed_columns)}")
    else:
        print("No columns were removed during the cleaning process.")


def plot_column_schemas(df):
    schemas = df.dtypes.astype(str).value_counts()
    fig, ax = plt.subplots(figsize=(10, 6))

    colors = plt.cm.rainbow(np.linspace(0, 1, len(schemas)))

    bars = ax.bar(schemas.index, schemas.values, color=colors)

    ax.set_title('Column Data Types')
    ax.set_xlabel('Data Type')
    ax.set_ylabel('Count')

    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width() / 2., height,
                f'{height}',
                ha='center', va='bottom')

    save_plot(fig, 'column_schemas.png')


def plot_nonconforming_cells(nonconforming_cells):
    if isinstance(nonconforming_cells, dict):
        fig, ax = plt.subplots(figsize=(12, 6))

        colors = plt.cm.rainbow(np.linspace(0, 1, len(nonconforming_cells)))

        bars = ax.bar(list(nonconforming_cells.keys()), list(nonconforming_cells.values()), color=colors)

        ax.set_title('Nonconforming Cells by Column')
        ax.set_xlabel('Columns')
        ax.set_ylabel('Number of Nonconforming Cells')
        plt.xticks(rotation=90)

        for bar in bars:
            height = bar.get_height()
            ax.text(bar.get_x() + bar.get_width() / 2., height,
                    f'{height:,}',
                    ha='center', va='bottom')

        save_plot(fig, 'nonconforming_cells.png')
    else:
        print(f"Expected nonconforming_cells to be a dictionary, but got {type(nonconforming_cells)}.")


def plot_column_distributions(original_df, cleaned_df, primary_key_column):
    numeric_columns = [col for col in original_df.select_dtypes(include=[np.number]).columns if
                       col != primary_key_column]
    num_columns = len(numeric_columns)

    if num_columns == 0:
        print("No numeric columns found for distribution plots.")
        return

    fig, axes = plt.subplots(nrows=(num_columns + 2) // 3, ncols=3, figsize=(18, 5 * ((num_columns + 2) // 3)))
    axes = axes.flatten() if num_columns > 1 else [axes]

    for i, column in enumerate(numeric_columns):
        if column in cleaned_df.columns:
            sns.histplot(original_df[column].dropna(), ax=axes[i], kde=True, color='blue', label='Before Cleaning',
                         alpha=0.5)
            sns.histplot(cleaned_df[column].dropna(), ax=axes[i], kde=True, color='orange', label='After Cleaning',
                         alpha=0.5)
            axes[i].set_title(f'{column} - Distribution Before & After Cleaning')
            axes[i].legend()

    for j in range(i + 1, len(axes)):
        fig.delaxes(axes[j])

    plt.tight_layout()
    save_plot(fig, 'distributions_before_after_cleaning.png')


def plot_boxplot_with_outliers(df, primary_key_column):
    numeric_columns = [col for col in df.select_dtypes(include=[np.number]).columns if col != primary_key_column]
    num_columns = len(numeric_columns)

    if num_columns == 0:
        print("No numeric columns found for boxplot.")
        return

    fig, axes = plt.subplots(nrows=(num_columns + 2) // 3, ncols=3, figsize=(15, 5 * ((num_columns + 2) // 3)))
    axes = axes.flatten() if num_columns > 1 else [axes]

    for i, column in enumerate(numeric_columns):
        sns.boxplot(x=df[column], ax=axes[i])
        axes[i].set_title(f'Boxplot of {column} with Outliers')

    for j in range(i + 1, len(axes)):
        fig.delaxes(axes[j])

    plt.tight_layout()
    save_plot(fig, 'boxplots_with_outliers.png')


def plot_correlation_heatmap(df, primary_key_column):
    numeric_df = df.select_dtypes(include=[np.number])
    numeric_df = numeric_df.drop(columns=[primary_key_column], errors='ignore')

    correlation_matrix = numeric_df.corr()

    fig, ax = plt.subplots(figsize=(15, 10))
    sns.heatmap(correlation_matrix, annot=True, fmt=".2f", cmap='coolwarm', ax=ax, cbar_kws={'label': 'Correlation'})
    ax.set_title('Correlation Heatmap')
    save_plot(fig, 'correlation_heatmap.png')


def plot_process_times(process_times):
    process_times_minutes = {k: v / 60 for k, v in process_times.items()}

    main_processes = {k: v for k, v in process_times_minutes.items() if not k.startswith("Clean column:")}
    column_processes = {k: v for k, v in process_times_minutes.items() if k.startswith("Clean column:")}

    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10))

    bars1 = ax1.bar(main_processes.keys(), main_processes.values())
    ax1.set_title('Main Process Times')
    ax1.set_ylabel('Time (minutes)')
    ax1.tick_params(axis='x', rotation=45)

    bars2 = ax2.bar(column_processes.keys(), column_processes.values())
    ax2.set_title('Column Cleaning Times')
    ax2.set_ylabel('Time (minutes)')
    ax2.tick_params(axis='x', rotation=90)

    for ax, bars in zip([ax1, ax2], [bars1, bars2]):
        for bar in bars:
            height = bar.get_height()
            ax.text(bar.get_x() + bar.get_width() / 2., height,
                    f'{height:.4f}', ha='center', va='bottom')

    total_time = sum(process_times_minutes.values())
    fig.suptitle(f'Process Times (Total: {total_time:.2f} minutes)', fontsize=16)

    plt.tight_layout()
    save_plot(fig, 'process_times.png')


def create_full_report(original_df, cleaned_df, nonconforming_cells_before, process_times, removed_columns,
                       removed_rows, primary_key_column):
    os.makedirs(REPORT_DIR, exist_ok=True)

    sns.set_style("whitegrid")
    plt.rcParams['figure.dpi'] = 400

    print("Plotting valid data percentages...")
    plot_valid_data_percentage(original_df, cleaned_df, primary_key_column)

    print("Plotting column schemas...")
    plot_column_schemas(cleaned_df)

    print("Plotting nonconforming cells before cleaning...")
    plot_nonconforming_cells(nonconforming_cells_before)

    print("Plotting column distributions...")
    plot_column_distributions(original_df, cleaned_df, primary_key_column)

    print("Plotting process times...")
    plot_process_times(process_times)

    print("Plotting heatmaps...")
    plot_heatmap(original_df, "Missing Values Before Cleaning")

    print("Plotting correlation heatmap...")
    plot_correlation_heatmap(cleaned_df, primary_key_column)

    print("Plotting boxplots with outliers...")
    plot_boxplot_with_outliers(cleaned_df, primary_key_column)

    print(f"All visualization reports saved in directory: {REPORT_DIR}")