Delete pages/LLM_Trial_1.py

pages/LLM_Trial_1.py

@@ -1,1088 +0,0 @@
-
-import os
-import random
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-import seaborn as sns
-from matplotlib.pyplot import subplots
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-from sklearn.metrics import mean_poisson_deviance, mean_gamma_deviance, make_scorer
-from scipy.stats import ks_2samp
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-from mpl_toolkits.mplot3d import Axes3D
-from sklearn.linear_model import TweedieRegressor
-import shap
-from sklearn.mixture import GaussianMixture
-from joblib import dump
-from joblib import load
-import streamlit as st
-
-import warnings
-warnings.filterwarnings('ignore')
-
-
-DEFAULT_RANDOM_SEED = 0 # Set a random seed for reproducibility throughout Python, NumPy, and TensorFlow operations
-random.seed(DEFAULT_RANDOM_SEED)
-os.environ['PYTHONHASHSEED'] = str(DEFAULT_RANDOM_SEED)
-np.random.seed(DEFAULT_RANDOM_SEED)
-
-# Title
-st.title("Large Language Model GPT-5.1: Synthetic Data Generation Analysis")
-
-
-def compare_real_vs_synthetic(real_df, synthetic_df, columns=None, kind='hist', bins=30, figsize=(15, 10)):
-    """
-    Compare distributions between real and synthetic datasets.
-
-    Parameters:
-    - real_df: pd.DataFrame, the original dataset
-    - synthetic_df: pd.DataFrame, the synthetic dataset
-    - columns: list of column names to compare; if None, all columns are used
-    - kind: str, type of plot: 'hist', 'kde', or 'box'
-    - bins: int, number of bins for histograms
-    - figsize: tuple, size of the plot figure
-
-    Returns:
-    - None (displays plots)
-    """
-    if columns is None:
-        columns = [col for col in real_df.columns if real_df[col].dtype != 'object']
-
-    n_cols = 2
-    n_rows = (len(columns) + 1) // n_cols
-
-    fig= plt.figure(figsize=figsize)
-
-    for idx, col in enumerate(columns, 1):
-        plt.subplot(n_rows, n_cols, idx)
-
-        if kind == 'hist':
-            sns.histplot(real_df[col], color='blue', label='Real', kde=False, stat='density', bins=bins, alpha=0.6)
-            sns.histplot(synthetic_df[col], color='red', label='Synthetic', kde=False, stat='density', bins=bins, alpha=0.6)
-
-        elif kind == 'kde':
-            sns.kdeplot(real_df[col], color='blue', label='Real')
-            sns.kdeplot(synthetic_df[col], color='red', label='Synthetic')
-
-        elif kind == 'box':
-            sns.boxplot(data=[real_df[col], synthetic_df[col]], palette=['blue', 'red'])
-            plt.xticks([0, 1], ['Real', 'Synthetic'])
-
-        else:
-            raise ValueError("Unsupported plot kind. Choose from 'hist', 'kde', or 'box'.")
-
-        plt.title(f"Comparison for '{col}'")
-        plt.legend()
-
-    plt.tight_layout()
-    st.pyplot(fig)
-
-
-def run_glm_frequency_analysis(
-    X_train, X_test, model=None, clip_exposure=False, random_state=0, label="Model", var=None):
-    """
-    Run GLM Poisson regression frequency analysis (ClaimNb ~ Features | Exposure).
-
-    Parameters:
-    - X_train: pd.DataFrame with ['Exposure', 'ClaimNb', ...]
-    - X_test: pd.DataFrame with ['Exposure', 'ClaimNb', ...]
-    - model: sklearn regressor, default is TweedieRegressor(power=1, link='log')
-    - clip_exposure: bool, if True, caps Exposure at 1 in training set
-    - random_state: int, for reproducibility
-    - label: str, label for printing/logging
-
-    Returns:
-    - trained_model: fitted model
-    - results: dict with CV scores, deviance on train/test, and predictions
-    """
-
-    np.random.seed(0)
-
-    # Optionally clip exposure in training data
-    if clip_exposure:
-        X_train = X_train.copy()
-        X_train['Exposure'] = np.where(X_train['Exposure'] > 1, 1, X_train['Exposure'])
-
-    # Filter for Exposure > 0
-    mask_tr = X_train['Exposure'] > 0
-    mask_te = X_test['Exposure'] > 0
-    X_train_f = X_train[mask_tr].copy()
-    X_test_f = X_test[mask_te].copy()
-
-    y_train = X_train_f['ClaimNb']
-    y_test = X_test_f['ClaimNb']
-    exposure_train = X_train_f['Exposure']
-    exposure_test = X_test_f['Exposure']
-
-    X_train_ = X_train_f.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-    X_test_ = X_test_f.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-
-    # Set model if not passed
-    if model is None:
-        model = TweedieRegressor(power=1, link='log')
-
-    # Cross-validation
-    cv = KFold(n_splits=5)
-    mpd_scores = []
-
-    for fold_idx, (train_idx, val_idx) in enumerate(cv.split(X_train_)):
-        X_tr, X_val = X_train_.iloc[train_idx], X_train_.iloc[val_idx]
-        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]
-        w_tr, w_val = exposure_train.iloc[train_idx], exposure_train.iloc[val_idx]
-
-        model.fit(X_tr, y_tr / w_tr, sample_weight=w_tr)
-        y_pred = model.predict(X_val)
-
-        score = mean_poisson_deviance(y_val / w_val, y_pred)
-        #st.write(f"Fold {fold_idx + 1} Poisson Deviance Score: {score:.4f}")
-        mpd_scores.append(score)
-
-    #st.write(f"Average cross-validation Poisson Deviance Score: {np.mean(mpd_scores):.4f}")
-    #st.write(f"Standard Deviation of CV Scores: {np.std(mpd_scores):.4f}")
-
-    # Final fit on full training set
-    model.fit(X_train_, y_train / exposure_train, sample_weight=exposure_train)
-
-    pred_train = model.predict(X_train_)
-    pred_test = model.predict(X_test_)
-
-    mpd_train = mean_poisson_deviance(y_train / exposure_train, pred_train)
-    mpd_test = mean_poisson_deviance(y_test / exposure_test, pred_test)
-
-    st.write(f"Train Poisson {var} Deviance: {mpd_train:.4f}")
-    st.write(f"Test Poisson {var} Deviance: {mpd_test:.4f}")
-
-    return model, {
-        "cv_scores": mpd_scores,
-        "mpd_train": mpd_train,
-        "mpd_test": mpd_test,
-        "train_predictions": pred_train,
-        "test_predictions": pred_test
-    }
-
-
-def run_glm_cost_analysis(X_train, X_test, is_sampled=False, verbose=True, var=None):
-    """
-    Perform GLM Cost Analysis using Tweedie Regressor (power=2, link='log').
-
-    Parameters:
-    - X_train: Training DataFrame (must include 'ClaimAmount', 'ClaimNb', 'Exposure')
-    - X_test: Testing DataFrame
-    - is_sampled: If True, cap 'Exposure' at 1 for training data
-    - verbose: If True, print CV results and scores
-
-    Returns:
-    - Dictionary containing train/test gamma deviance and predictions
-    """
-
-    np.random.seed(0)
-
-    # Cap exposure if sampled
-    if is_sampled:
-        X_train = X_train.copy()
-        X_train['Exposure'] = np.where(X_train['Exposure'] > 1, 1, X_train['Exposure'])
-
-    X_train_co = X_train.copy()
-    X_test_co = X_test.copy()
-
-    # Compute average cost per claim (Acost)
-    X_train_co['Acost'] = np.where(X_train_co['ClaimNb'] != 0,
-                                   X_train_co['ClaimAmount'] / X_train_co['ClaimNb'], 0)
-    X_test_co['Acost'] = np.where(X_test_co['ClaimNb'] != 0,
-                                  X_test_co['ClaimAmount'] / X_test_co['ClaimNb'], 0)
-
-    # Filter rows with non-zero claim amounts
-    X_train_cost = X_train_co[X_train_co['ClaimAmount'] != 0].copy()
-    X_test_cost = X_test_co[X_test_co['ClaimAmount'] != 0].copy()
-
-    # Target and weights
-    y_train = X_train_cost['Acost']
-    claim_tr = X_train_cost['ClaimNb']
-    y_test = X_test_cost['Acost']
-    claim_te = X_test_cost['ClaimNb']
-
-    # Features
-    drop_cols = ['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb']
-    X_train_ = X_train_cost.drop(columns=drop_cols)
-    X_test_ = X_test_cost.drop(columns=drop_cols)
-
-    # Initialize model
-    glm_cl = TweedieRegressor(power=2, link='log')
-
-    # Cross-validation
-    cv = KFold(n_splits=5, shuffle=True, random_state=0)
-    mgd_scores = []
-
-    for fold_idx, (train_idx, val_idx) in enumerate(cv.split(X_train_)):
-        X_tr, X_val = X_train_.iloc[train_idx], X_train_.iloc[val_idx]
-        y_tr, y_val = y_train.iloc[train_idx], y_train.iloc[val_idx]
-        w_tr, w_val = claim_tr.iloc[train_idx], claim_tr.iloc[val_idx]
-
-        glm_cl.fit(X_tr, y_tr, sample_weight=w_tr)
-        y_pred_val = glm_cl.predict(X_val)
-        score = mean_gamma_deviance(y_val, y_pred_val)
-        mgd_scores.append(score)
-
-        #if verbose:
-        #    print(f"Fold {fold_idx + 1} Gamma Deviance Score: {score:.4f}")
-
-    #if verbose:
-    #    print("Average cross-validation Gamma Deviance Score:", np.mean(mgd_scores))
-    #    print("Standard Deviation of CV Scores:", np.std(mgd_scores))
-
-    # Train on full data
-    glm_cl.fit(X_train_, y_train, sample_weight=claim_tr)
-
-    # Predictions
-    y_pred_train = glm_cl.predict(X_train_)
-    y_pred_test = glm_cl.predict(X_test_)
-
-    # Deviance on train and test
-    mgd_train = mean_gamma_deviance(y_train, y_pred_train)
-    mgd_test = mean_gamma_deviance(y_test, y_pred_test)
-
-    if verbose:
-        st.write(f"Train Gamma {var} Deviance: {mgd_train:.4f}")
-        st.write(f"Test Gamma {var} Deviance: {mgd_test:.4f}")
-
-    return {
-        "cv_scores": mgd_scores,
-        'mgd_train': mgd_train,
-        'mgd_test': mgd_test,
-        'y_pred_train': y_pred_train,
-        'y_pred_test': y_pred_test
-    }
-
-
-def plot_glm_shap_importance(
-    X_train, X_test, y_train, sample_weight,
-    power: int, title: str, max_display: int = 10, figsize: tuple = (5, 5), seed: int = 0):
-    """
-    Compute and plot SHAP feature importance for GLMs using SHAP LinearExplainer.
-
-    Parameters:
-        X_train (pd.DataFrame): Training features
-        X_test (pd.DataFrame): Test features
-        y_train (pd.Series or np.array): Training target
-        sample_weight (pd.Series or np.array): Sample weights
-        power (int): Tweedie power (1 = Poisson for frequency, 2 = Gamma for severity)
-        title (str): Title for the plot
-        max_display (int): Max number of features to display
-        figsize (tuple): Size of the figure
-        seed (int): Random seed for reproducibility
-    """
-     
-    np.random.seed(seed)
-
-    model = TweedieRegressor(power=power, link='log')
-    model.fit(X_train, y_train, sample_weight=sample_weight)
-
-    masker = shap.maskers.Independent(X_train)
-    explainer = shap.LinearExplainer(model, masker=masker)
-    shap_values = explainer.shap_values(X_test)
-
-    plt.figure(figsize=figsize)
-    shap.summary_plot(
-        shap_values, features=X_test,
-        feature_names=X_test.columns,
-        plot_type='bar',
-        max_display=max_display,
-        show=False
-    )
-    plt.title(title, fontsize=12)
-    plt.tight_layout()
-    fig = plt.gcf()
-    st.pyplot(fig)
-
-
-# ### Upload datasets
-
-#-------------------
-# DATASETS
-#-------------------
-df1=pd.read_csv('./data/ausprivauto0405.csv')
-df2=pd.read_csv('./data/swmotorcycle.csv')
-df1_synth=pd.read_csv('./LLM/synthetic_nonlife_53320_D1_80.csv')
-#df1_synth = df1_synth.drop(columns=["Unnamed: 0"])
-df2_synth=pd.read_csv('./LLM/synthetic_nonlife_51638_D2_80.csv')
-#df2_synth = df2_synth.drop(columns=["Unnamed: 0"])
-
-
-
-# ### dataset 1 and data handling
-
-st.header('Dataset 1: ausprivauto0405')
-
-df1_duplicated_rows=df1[df1.duplicated()]
-df1=df1.drop_duplicates()
-df1_duplicated_col=df1.columns[df1.columns.duplicated()]
-
-
-# ### Encoding
-
-df1_encod=df1.copy()
-# VehAge
-VehAge_group = {'old cars':'1','young cars':'2','oldest cars':'3','youngest cars':'4'}
-df1_encod['VehAge'] = df1_encod['VehAge'].map(VehAge_group)
-df1_encod['VehAge']= df1_encod['VehAge'].astype(int)
-# DrivAge
-DrivAge_group = {'young people':'1','older work. people':'2','oldest people':'3','working people':'4','old people':'5','youngest people':'6'}
-df1_encod['DrivAge'] = df1_encod['DrivAge'].map(DrivAge_group)
-df1_encod['DrivAge']= df1_encod['DrivAge'].astype(int)
-# VehBody
-VehBody_group = {'Hatchback':'1','Utility':'2','Station wagon':'3','Hardtop':'4','Panel van':'5','Sedan':'6','Truck':'7',\
-                'Coupe':'8', 'Minibus':'9', 'Motorized caravan':'10', 'Bus':'11', 'Convertible':'12','Roadster':'13'}
-df1_encod['VehBody'] = df1_encod['VehBody'].map(VehBody_group)
-df1_encod['VehBody']= df1_encod['VehBody'].astype(int)
-# Gender
-Gender_group = {'Female':'0','Male':'1'}
-df1_encod['Gender'] = df1_encod['Gender'].map(Gender_group)
-df1_encod['Gender']= df1_encod['Gender'].astype(int)
-
-
-
-
-# ### Split dataset
-# Split the dataset into train/test split
-X_train, X_test = train_test_split(df1_encod, test_size=0.2, random_state=0)
-st.markdown(f"**Train shape:** {X_train.shape}  \n**Test shape:** {X_test.shape}")
-
-
-# ### Use Generate Samples Dataframe
-df1_synth_encod=df1_synth.copy()
-# VehAge
-VehAge_group = {'old cars':'1','young cars':'2','oldest cars':'3','youngest cars':'4'}
-df1_synth_encod['VehAge'] = df1_synth_encod['VehAge'].map(VehAge_group)
-df1_synth_encod['VehAge']= df1_synth_encod['VehAge'].astype(int)
-# DrivAge
-DrivAge_group = {'young people':'1','older work. people':'2','oldest people':'3','working people':'4','old people':'5','youngest people':'6'}
-df1_synth_encod['DrivAge'] = df1_synth_encod['DrivAge'].map(DrivAge_group)
-df1_synth_encod['DrivAge']= df1_synth_encod['DrivAge'].astype(int)
-# VehBody
-VehBody_group = {'Hatchback':'1','Utility':'2','Station wagon':'3','Hardtop':'4','Panel van':'5','Sedan':'6','Truck':'7',\
-                'Coupe':'8', 'Minibus':'9', 'Motorized caravan':'10', 'Bus':'11', 'Convertible':'12','Roadster':'13'}
-df1_synth_encod['VehBody'] = df1_synth_encod['VehBody'].map(VehBody_group)
-df1_synth_encod['VehBody']= df1_synth_encod['VehBody'].astype(int)
-# Gender
-Gender_group = {'Female':'0','Male':'1'}
-df1_synth_encod['Gender'] = df1_synth_encod['Gender'].map(Gender_group)
-df1_synth_encod['Gender']= df1_synth_encod['Gender'].astype(int)
-
-
-new_samples_df=df1_synth_encod.copy()
-
-# Check consistency
-st.subheader(f"Check consistency")
-# Find inconsistencies
-inconsistent_records = new_samples_df[
-    ~(((new_samples_df["ClaimNb"] == 0) & (new_samples_df["ClaimOcc"] == 0) & (new_samples_df["ClaimAmount"] == 0)) |
-      ((new_samples_df["ClaimNb"] > 0) & (new_samples_df["ClaimOcc"] > 0) & (new_samples_df["ClaimAmount"] > 0)))
-]
-
-st.write(f"Number of inconsistent records on synthetic data: {len(inconsistent_records)}")
-st.write(inconsistent_records.head())  # Show a few inconsistent rows
-st.write('Helps assess basic data fidelity by checking structural or logical violations.') 
-
-
-# ### Visual Comparison
-
-# Compare selected variables using histograms
-st.subheader(f"Univariate distribution comparison: real vs synthetic")
-st.write('Shows how well each individual feature is mimicked by the synthetic data.')
-
-compare_real_vs_synthetic(
-    real_df=X_train,
-    synthetic_df=df1_synth,
-    columns=['Exposure','VehBody','VehValue','ClaimOcc','ClaimNb', 'ClaimAmount', 'DrivAge', 'VehAge','Gender'],
-    kind='hist'
-)
-
-
-st.subheader(f"Correlation matrix comparison: real vs synthetic")
-st.write('Evaluates preservation of feature-to-feature relationships.')
-
-# Compute correlation matrices
-corr_matrix_X_train = X_train.corr()
-corr_matrix_new_samples = new_samples_df.corr()
-
-# Set figure size
-fig=plt.figure(figsize=(30,15))
-
-# a subplot grid
-# Parameters (1, 2, 1) implies 1 row, 2 columns, and this plot is the 1st plot.
-plt.subplot(1, 2, 1) # Subplot 1
-sns.heatmap(corr_matrix_X_train, square=True, annot=True, cmap='coolwarm', fmt='.2f',annot_kws={"size": 15})
-plt.title('Correlation Heatmap of X_train', size=15)
-plt.yticks(rotation=0,fontsize=15)
-plt.xticks(rotation=90,fontsize=15)
-
-# another subplot for the second heatmap
-plt.subplot(1, 2, 2) # Subplot 2
-sns.heatmap(corr_matrix_new_samples, square=True, annot=True, cmap='coolwarm', fmt='.2f',annot_kws={"size": 15})
-plt.title('Correlation Heatmap of New Samples', size=15)
-plt.yticks(rotation=0,fontsize=15)
-plt.xticks(rotation=90,fontsize=15)
-
-# Display the plot
-plt.tight_layout()
-st.pyplot(fig)
-
-# ### Statistical Analysis
-# Kolmogorov-Smirnov test
-st.subheader("Kolmogorov–Smirnov Test Results")
-st.write('Quantifies the statistical distance between real and synthetic distributions.')
-
-results = []
-
-for column in X_train.columns:
-    original = X_train[column].values
-    generated = new_samples_df[column].values
-    statistic, p_value = ks_2samp(original, generated)
-
-    results.append({
-        "Feature": column,
-        "KS Statistic": statistic,
-        "P-value": p_value
-    })
-
-results_df = pd.DataFrame(results)
-
-def color_pval(val):
-    color = "red" if val < 0.05 else "green"
-    return f"color: {color};"
-
-styled_df = results_df.style.applymap(color_pval, subset=["P-value"]) \
-                            .format({"KS Statistic": "{:.4f}", "P-value": "{:.4f}"})
-                            
-st.markdown("""
-**Legend:**  
-- <span style='color:green;'>Green P-value</span>: distributions are **similar** (p ≥ 0.05)  
-- <span style='color:red;'>Red P-value</span>: distributions are **significantly different** (p < 0.05)
-""", unsafe_allow_html=True)
-st.dataframe(styled_df)
-
-
-# ### PCA Analysis
-
-st.subheader('PCA comparison')
-st.write('Assesses similarity in global variance structure and major latent components.')
-# Load the saved models
-img = mpimg.imread('./LLM/pca_d1_80.png')
-fig=plt.figure(figsize=(10, 8))
-plt.imshow(img)
-plt.axis('off')
-st.pyplot(fig)
-
-
-
-# ### UMAP Analysis
-
-st.subheader('UMAP comparison')
-st.write('Examines nonlinear manifold structure and clustering behavior.')
-img = mpimg.imread('./LLM/umap_d1_80.png')
-fig=plt.figure(figsize=(10, 8))
-plt.imshow(img)
-plt.axis('off')
-st.pyplot(fig)
-
-
-# ### GLM Frequency Analysis
-st.subheader('Frequency GLM Analysis')
-st.write('Tests how well synthetic data preserves predictive relationships for claim frequency.')
-# Baseline frequency model
-results_frequency_1 = run_glm_frequency_analysis(X_train, X_test, label="Baseline", var='Real')
-# Using synthetic sample data with exposure clipping
-results_frequency_2 = run_glm_frequency_analysis(new_samples_df, X_test, clip_exposure=True, label="Synthetic Clipped",var= 'Synthetic')
-
-
-# ### GLM Cost Analysis
-st.subheader('Severity GLM Analysis')
-st.write('Evaluates whether severity-related predictors behave similarly on real and synthetic data.')
-results_cost_1 = run_glm_cost_analysis(X_train, X_test,var='Real')
-results_cost_2 = run_glm_cost_analysis(new_samples_df, X_test, is_sampled=True,var='Synthetic')
-
-
-# ### Feature Importance Analysis
-# --- SHAP Feature Importance for Frequency ---
-st.subheader('SHAP Feature Importance for Frequency Model')
-st.write('Shows whether drivers of frequency predictions remain consistent across datasets.')
-# Prepare data for frequency model SHAP
-X_train_freq = X_train.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-y_train_freq = X_train['ClaimNb']
-sample_weight_freq = X_train['Exposure']
-
-X_test_freq = X_test.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-
-# Filter out rows with Exposure = 0 for frequency model training and SHAP explanation
-mask_train_freq = sample_weight_freq > 0
-X_train_freq_filtered = X_train_freq[mask_train_freq]
-y_train_freq_filtered = y_train_freq[mask_train_freq]
-sample_weight_freq_filtered = sample_weight_freq[mask_train_freq]
-
-# Ensure X_test_freq also only contains rows where Exposure > 0
-mask_test_freq = X_test['Exposure'] > 0
-X_test_freq_filtered = X_test_freq[mask_test_freq]
-
-
-# Plot SHAP for Frequency
-plot_glm_shap_importance(
-    X_train=X_train_freq_filtered,
-    X_test=X_test_freq_filtered,
-    y_train=y_train_freq_filtered / sample_weight_freq_filtered, # Target is rate (ClaimNb / Exposure)
-    sample_weight=sample_weight_freq_filtered,
-    power=1, # Power=1 for Poisson (frequency)
-    title="SHAP Feature Importance for Frequency Model (Real Data)",
-    max_display=10
-)
-
-# --- SHAP Feature Importance for Frequency (Synthetic Data) ---
-# Prepare data for frequency model SHAP using synthetic data
-X_train_freq_synth = new_samples_df.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-y_train_freq_synth = new_samples_df['ClaimNb']
-sample_weight_freq_synth = new_samples_df['Exposure']
-
-# X_test_freq is the same as before (real test data)
-X_test_freq = X_test.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-
-# Filter out rows with Exposure = 0 for frequency model training and SHAP explanation
-mask_train_freq_synth = sample_weight_freq_synth > 0
-X_train_freq_synth_filtered = X_train_freq_synth[mask_train_freq_synth]
-y_train_freq_synth_filtered = y_train_freq_synth[mask_train_freq_synth]
-sample_weight_freq_synth_filtered = sample_weight_freq_synth[mask_train_freq_synth]
-
-# Ensure X_test_freq also only contains rows where Exposure > 0
-mask_test_freq = X_test['Exposure'] > 0
-X_test_freq_filtered = X_test_freq[mask_test_freq]
-
-# Plot SHAP for Frequency (Synthetic Data)
-plot_glm_shap_importance(
-    X_train=X_train_freq_synth_filtered,
-    X_test=X_test_freq_filtered,
-    y_train=y_train_freq_synth_filtered / sample_weight_freq_synth_filtered, # Target is rate
-    sample_weight=sample_weight_freq_synth_filtered,
-    power=1, # Power=1 for Poisson (frequency)
-    title="SHAP Feature Importance for Frequency Model (Synthetic Data)",
-    max_display=10
-)
-
-# --- SHAP Feature Importance for Severity ---
-st.subheader('SHAP Feature Importance for Severity Model')
-st.write('Assesses stability of model explanations for severity outcomes.')
-
-# Prepare data for severity model SHAP
-X_train_cost_prep = X_train[X_train['ClaimAmount'] != 0].copy()
-X_test_cost_prep = X_test[X_test['ClaimAmount'] != 0].copy()
-
-X_train_sev = X_train_cost_prep.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-y_train_sev = X_train_cost_prep['ClaimAmount'] / X_train_cost_prep['ClaimNb']
-sample_weight_sev = X_train_cost_prep['ClaimNb'] # Number of claims is the weight for severity
-
-X_test_sev = X_test_cost_prep.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-
-# Plot SHAP for Severity
-plot_glm_shap_importance(
-    X_train=X_train_sev,
-    X_test=X_test_sev,
-    y_train=y_train_sev,
-    sample_weight=sample_weight_sev,
-    power=2, # Power=2 for Gamma (severity)
-    title="SHAP Feature Importance for Severity Model (Real Data)",
-    max_display=10
-)
-
-
-# --- SHAP Feature Importance for Severity (Synthetic Data) ---
-# Prepare data for severity model SHAP using synthetic data
-X_train_cost_prep_synth = new_samples_df[new_samples_df['ClaimAmount'] != 0].copy()
-X_test_cost_prep_synth = X_test[X_test['ClaimAmount'] != 0].copy() # Keep using real test data for explanation
-
-X_train_sev_synth = X_train_cost_prep_synth.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-y_train_sev_synth = X_train_cost_prep_synth['ClaimAmount'] / X_train_cost_prep_synth['ClaimNb']
-sample_weight_sev_synth = X_train_cost_prep_synth['ClaimNb'] # Number of claims is the weight for severity
-
-X_test_sev_synth = X_test_cost_prep_synth.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-
-
-# Plot SHAP for Severity (Synthetic Data)
-plot_glm_shap_importance(
-    X_train=X_train_sev_synth,
-    X_test=X_test_sev_synth,
-    y_train=y_train_sev_synth,
-    sample_weight=sample_weight_sev_synth,
-    power=2, # Power=2 for Gamma (severity)
-    title="SHAP Feature Importance for Severity Model (Synthetic Data)",
-    max_display=10
-)
-
-
-# ### dataset 2 and data handling
-st.header('Dataset 2: swmotorcycle')
-
-df2_duplicated_rows=df2[df2.duplicated()]
-df2=df2.drop_duplicates()
-df2_duplicated_col=df2.columns[df2.columns.duplicated()]
-
-
-# add ClaimOcc feature
-df_2 = df2.copy()
-df_2['ClaimOcc'] = np.where(df_2['ClaimNb'] > 0, 1, 0)
-# Feature transformation
-df_2['Exposure'] = df_2['Exposure'].clip(upper=1)
-df_2['VehAge'] = df_2['VehAge'].clip(upper=20)
-
-
-# ### Encoding
-df2_encod=df_2.copy()
-# RiskClass
-RiskClass_group = {'EV ratio 13-15':'1','EV ratio 20-24':'2','EV ratio 9-12':'3','EV ratio <5':'4','EV ratio 6-8':'5',\
-                   'EV ratio 16-19':'6','EV ratio >25':'7'}
-df2_encod['RiskClass'] = df2_encod['RiskClass'].map(RiskClass_group)
-df2_encod['RiskClass']= df2_encod['RiskClass'].astype(int)
-# BonusClass
-BonusClass_group = {'BM1':'1','BM2':'2','BM3':'3','BM4':'4','BM5':'5','BM6':'6','BM7':'7'}
-df2_encod['BonusClass'] = df2_encod['BonusClass'].map(BonusClass_group)
-df2_encod['BonusClass']= df2_encod['BonusClass'].astype(int)
-# Area
-Area_group = {"Central parts of Sweden's three largest cities":'1','Lesser towns except Gotland; Northern towns':'2',\
-              'Small towns; countryside except Gotland; Northern towns':'3','Suburbs; middle-sized cities':'4',\
-              'Northern countryside':'5','Northern towns':'6',"Gotland (Sweden's largest island)":'7'}
-df2_encod['Area'] = df2_encod['Area'].map(Area_group)
-df2_encod['Area']= df2_encod['Area'].astype(int)
-# Gender
-Gender_group = {'Female':'0','Male':'1'}
-df2_encod['Gender'] = df2_encod['Gender'].map(Gender_group)
-df2_encod['Gender']= df2_encod['Gender'].astype(int)
-
-
-
-
-# ### Split dataset
-# Split the dataset into train/test split
-X_train, X_test = train_test_split(df2_encod, test_size=0.2, random_state=0)
-st.markdown(f"**Train shape:** {X_train.shape}  \n**Test shape:** {X_test.shape}")
-
-
-# ### Use Generate Samples Dataframe
-df2_synth_encod=df2_synth.copy()
-# RiskClass
-RiskClass_group = {'EV ratio 13-15':'1','EV ratio 20-24':'2','EV ratio 9-12':'3','EV ratio <5':'4','EV ratio 6-8':'5',\
-                   'EV ratio 16-19':'6','EV ratio >25':'7'}
-df2_synth_encod['RiskClass'] = df2_synth_encod['RiskClass'].map(RiskClass_group)
-df2_synth_encod['RiskClass']= df2_synth_encod['RiskClass'].astype(int)
-# BonusClass
-BonusClass_group = {'BM1':'1','BM2':'2','BM3':'3','BM4':'4','BM5':'5','BM6':'6','BM7':'7'}
-df2_synth_encod['BonusClass'] = df2_synth_encod['BonusClass'].map(BonusClass_group)
-df2_synth_encod['BonusClass']= df2_synth_encod['BonusClass'].astype(int)
-# Area
-Area_group = {"Central parts of Sweden's three largest cities":'1','Lesser towns except Gotland; Northern towns':'2',\
-              'Small towns; countryside except Gotland; Northern towns':'3','Suburbs; middle-sized cities':'4',\
-              'Northern countryside':'5','Northern towns':'6',"Gotland (Sweden's largest island)":'7'}
-df2_synth_encod['Area'] = df2_synth_encod['Area'].map(Area_group)
-df2_synth_encod['Area']= df2_synth_encod['Area'].astype(int)
-# Gender
-Gender_group = {'Female':'0','Male':'1'}
-df2_synth_encod['Gender'] = df2_synth_encod['Gender'].map(Gender_group)
-df2_synth_encod['Gender']= df2_synth_encod['Gender'].astype(int)
-
-new_samples_df=df2_synth_encod.copy()
-
-# Check consistency
-st.subheader(f"Check consistency")
-# Find inconsistencies
-inconsistent_records = new_samples_df[
-    ~(((new_samples_df["ClaimNb"] == 0) & (new_samples_df["ClaimOcc"] == 0) & (new_samples_df["ClaimAmount"] == 0)) |
-      ((new_samples_df["ClaimNb"] > 0) & (new_samples_df["ClaimOcc"] > 0) & (new_samples_df["ClaimAmount"] > 0)))
-]
-
-st.write(f"Number of inconsistent records on synthetic data: {len(inconsistent_records)}")
-st.write(inconsistent_records.head())  # Show a few inconsistent rows
-st.write('Helps assess basic data fidelity by checking structural or logical violations.')
-
-
-# ### Visual Comparison
-st.subheader('Univariate distribution comparison: real vs synthetic')
-st.write('Shows how well each individual feature is mimicked by the synthetic data.')
-
-# Compare selected variables using histograms
-compare_real_vs_synthetic(
-    real_df=X_train,
-    synthetic_df=df2_synth,
-    columns=['Exposure','VehAge','ClaimOcc','ClaimNb', 'ClaimAmount', 'RiskClass', 'Area','BonusClass','Gender'],
-    kind='hist'
-)
-
-st.subheader('Correlation matrix comparison: real vs synthetic')
-st.write('Evaluates preservation of feature-to-feature relationships.')
-
-# Compute correlation matrices
-corr_matrix_X_train = X_train.corr()
-corr_matrix_new_samples = new_samples_df.corr()
-
-# Set figure size
-fig=plt.figure(figsize=(30,15))
-
-# a subplot grid
-# Parameters (1, 2, 1) implies 1 row, 2 columns, and this plot is the 1st plot.
-plt.subplot(1, 2, 1) # Subplot 1
-sns.heatmap(corr_matrix_X_train, square=True, annot=True, cmap='coolwarm', fmt='.2f',annot_kws={"size": 15})
-plt.title('Correlation Heatmap of X_train', size=15)
-plt.yticks(rotation=0,fontsize=15)
-plt.xticks(rotation=90,fontsize=15)
-
-# another subplot for the second heatmap
-plt.subplot(1, 2, 2) # Subplot 2
-sns.heatmap(corr_matrix_new_samples, square=True, annot=True, cmap='coolwarm', fmt='.2f',annot_kws={"size": 15})
-plt.title('Correlation Heatmap of New Samples', size=15)
-plt.yticks(rotation=0,fontsize=15)
-plt.xticks(rotation=90,fontsize=15)
-
-# Display the plot
-plt.tight_layout()
-st.pyplot(fig)
-
-
-# ### Statistical Analysis
-# Kolmogorov-Smirnov test
-st.subheader('Kolmogorov–Smirnov Test Results')
-st.write('Quantifies the statistical distance between real and synthetic distributions.')
-
-
-results = []
-
-for column in X_train.columns:
-    original = X_train[column].values
-    generated = new_samples_df[column].values
-    statistic, p_value = ks_2samp(original, generated)
-
-    results.append({
-        "Feature": column,
-        "KS Statistic": statistic,
-        "P-value": p_value
-    })
-
-results_df = pd.DataFrame(results)
-
-def color_pval(val):
-    color = "red" if val < 0.05 else "green"
-    return f"color: {color};"
-
-styled_df = results_df.style.applymap(color_pval, subset=["P-value"]) \
-                            .format({"KS Statistic": "{:.4f}", "P-value": "{:.4f}"})
-                            
-st.markdown("""
-**Legend:**  
-- <span style='color:green;'>Green P-value</span>: distributions are **similar** (p ≥ 0.05)  
-- <span style='color:red;'>Red P-value</span>: distributions are **significantly different** (p < 0.05)
-""", unsafe_allow_html=True)
-st.dataframe(styled_df)
-
-
-# ### PCA Analysis
-st.subheader('PCA comparison')
-st.write('Assesses similarity in global variance structure and major latent components.')
-
-img = mpimg.imread('./LLM/pca_d2_80.png')
-fig=plt.figure(figsize=(10, 8))
-plt.imshow(img)
-plt.axis('off')
-st.pyplot(fig)
-
-
-# ### UMAP Analysis
-st.subheader('UMAP comparison')
-st.write('Examines nonlinear manifold structure and clustering behavior.')
-img = mpimg.imread('./LLM/umap_d2_80.png')
-fig=plt.figure(figsize=(10, 8))
-plt.imshow(img)
-plt.axis('off')
-st.pyplot(fig)
-
-
-# ### GLM Frequency Analysis
-st.subheader('Frequency GLM Analysis')
-st.write('Tests how well synthetic data preserves predictive relationships for claim frequency.')
-# Baseline frequency model
-results_frequency_3 = run_glm_frequency_analysis(X_train, X_test, label="Baseline", var='Real')
-# Using synthetic sample data with exposure clipping
-results_frequency_4 = run_glm_frequency_analysis(new_samples_df, X_test, clip_exposure=True, label="Synthetic Clipped", var='Synthetic')
-
-
-# ### GLM Cost Analysis
-st.subheader('Severity GLM Analysis')
-st.write('Evaluates whether severity-related predictors behave similarly on real and synthetic data.')
-results_cost_3 = run_glm_cost_analysis(X_train, X_test, var='Real')
-results_cost_4 = run_glm_cost_analysis(new_samples_df, X_test, is_sampled=True, var= 'Synthetic')
-
-
-# ### Feature Importance Analysis
-
-# --- SHAP Feature Importance for Frequency ---
-st.subheader('SHAP Feature Importance for Frequency Model')
-st.write('Shows whether drivers of frequency predictions remain consistent across datasets.')
-
-# Prepare data for frequency model SHAP
-X_train_freq = X_train.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-y_train_freq = X_train['ClaimNb']
-sample_weight_freq = X_train['Exposure']
-
-X_test_freq = X_test.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-
-# Filter out rows with Exposure = 0 for frequency model training and SHAP explanation
-mask_train_freq = sample_weight_freq > 0
-X_train_freq_filtered = X_train_freq[mask_train_freq]
-y_train_freq_filtered = y_train_freq[mask_train_freq]
-sample_weight_freq_filtered = sample_weight_freq[mask_train_freq]
-
-# Ensure X_test_freq also only contains rows where Exposure > 0
-mask_test_freq = X_test['Exposure'] > 0
-X_test_freq_filtered = X_test_freq[mask_test_freq]
-
-
-# Plot SHAP for Frequency
-plot_glm_shap_importance(
-    X_train=X_train_freq_filtered,
-    X_test=X_test_freq_filtered,
-    y_train=y_train_freq_filtered / sample_weight_freq_filtered, # Target is rate (ClaimNb / Exposure)
-    sample_weight=sample_weight_freq_filtered,
-    power=1, # Power=1 for Poisson (frequency)
-    title="SHAP Feature Importance for Frequency Model (Real Data)",
-    max_display=10
-)
-
-# --- SHAP Feature Importance for Frequency (Synthetic Data) ---
-# Prepare data for frequency model SHAP using synthetic data
-X_train_freq_synth = new_samples_df.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-y_train_freq_synth = new_samples_df['ClaimNb']
-sample_weight_freq_synth = new_samples_df['Exposure']
-
-# X_test_freq is the same as before (real test data)
-X_test_freq = X_test.drop(['Exposure', 'ClaimNb', 'ClaimAmount'], axis=1, errors='ignore')
-
-# Filter out rows with Exposure = 0 for frequency model training and SHAP explanation
-mask_train_freq_synth = sample_weight_freq_synth > 0
-X_train_freq_synth_filtered = X_train_freq_synth[mask_train_freq_synth]
-y_train_freq_synth_filtered = y_train_freq_synth[mask_train_freq_synth]
-sample_weight_freq_synth_filtered = sample_weight_freq_synth[mask_train_freq_synth]
-
-# Ensure X_test_freq also only contains rows where Exposure > 0
-mask_test_freq = X_test['Exposure'] > 0
-X_test_freq_filtered = X_test_freq[mask_test_freq]
-
-# Plot SHAP for Frequency (Synthetic Data)
-plot_glm_shap_importance(
-    X_train=X_train_freq_synth_filtered,
-    X_test=X_test_freq_filtered,
-    y_train=y_train_freq_synth_filtered / sample_weight_freq_synth_filtered, # Target is rate
-    sample_weight=sample_weight_freq_synth_filtered,
-    power=1, # Power=1 for Poisson (frequency)
-    title="SHAP Feature Importance for Frequency Model (Synthetic Data)",
-    max_display=10
-)
-
-# --- SHAP Feature Importance for Severity ---
-st.subheader('SHAP Feature Importance for Severity Model')
-st.write('Assesses stability of model explanations for severity outcomes')
-
-# Prepare data for severity model SHAP
-X_train_cost_prep = X_train[X_train['ClaimAmount'] != 0].copy()
-X_test_cost_prep = X_test[X_test['ClaimAmount'] != 0].copy()
-
-X_train_sev = X_train_cost_prep.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-y_train_sev = X_train_cost_prep['ClaimAmount'] / X_train_cost_prep['ClaimNb']
-sample_weight_sev = X_train_cost_prep['ClaimNb'] # Number of claims is the weight for severity
-
-X_test_sev = X_test_cost_prep.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-
-# Plot SHAP for Severity
-plot_glm_shap_importance(
-    X_train=X_train_sev,
-    X_test=X_test_sev,
-    y_train=y_train_sev,
-    sample_weight=sample_weight_sev,
-    power=2, # Power=2 for Gamma (severity)
-    title="SHAP Feature Importance for Severity Model (Real Data)",
-    max_display=10
-)
-
-# --- SHAP Feature Importance for Severity (Synthetic Data) ---
-# Prepare data for severity model SHAP using synthetic data
-X_train_cost_prep_synth = new_samples_df[new_samples_df['ClaimAmount'] != 0].copy()
-X_test_cost_prep_synth = X_test[X_test['ClaimAmount'] != 0].copy() # Keep using real test data for explanation
-
-X_train_sev_synth = X_train_cost_prep_synth.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-y_train_sev_synth = X_train_cost_prep_synth['ClaimAmount'] / X_train_cost_prep_synth['ClaimNb']
-sample_weight_sev_synth = X_train_cost_prep_synth['ClaimNb'] # Number of claims is the weight for severity
-
-X_test_sev_synth = X_test_cost_prep_synth.drop(columns=['Acost', 'Exposure', 'ClaimAmount', 'ClaimNb'], errors='ignore')
-
-
-# Plot SHAP for Severity (Synthetic Data)
-plot_glm_shap_importance(
-    X_train=X_train_sev_synth,
-    X_test=X_test_sev_synth,
-    y_train=y_train_sev_synth,
-    sample_weight=sample_weight_sev_synth,
-    power=2, # Power=2 for Gamma (severity)
-    title="SHAP Feature Importance for Severity Model (Synthetic Data)",
-    max_display=10
-)
-
-
-# ### Results
-st.subheader('Overall results')
-# The dictionary dataset 1
-metrics_dict_1 = results_frequency_1[1]
-mpd_train_1 = metrics_dict_1['mpd_train']
-mpd_test_1 = metrics_dict_1['mpd_test']
-
-
-# The dictionary synthetic dataset 1
-metrics_dict_2 = results_frequency_2[1]
-mpd_train_2 = metrics_dict_2['mpd_train']
-mpd_test_2 = metrics_dict_2['mpd_test']
-
-
-
-# The dictionary dataset 2
-metrics_dict_3 = results_frequency_3[1]
-mpd_train_3 = metrics_dict_3['mpd_train']
-mpd_test_3 = metrics_dict_3['mpd_test']
-
-
-
-# The dictionary synthetic dataset 2
-metrics_dict_4 = results_frequency_4[1]
-mpd_train_4 = metrics_dict_4['mpd_train']
-mpd_test_4 = metrics_dict_4['mpd_test']
-
-
-
-# The dictionary dataset 1
-mgd_train_1 = results_cost_1['mgd_train']
-mgd_test_1 = results_cost_1['mgd_test']
-
-
-
-# The dictionary synthetic dataset 1
-mgd_train_2 = results_cost_2['mgd_train']
-mgd_test_2 = results_cost_2['mgd_test']
-
-
-
-# The dictionary dataset 2
-mgd_train_3 = results_cost_3['mgd_train']
-mgd_test_3 = results_cost_3['mgd_test']
-
-
-
-# The dictionary synthetic dataset 2
-mgd_train_4 = results_cost_4['mgd_train']
-mgd_test_4 = results_cost_4['mgd_test']
-
-
-
-# Create the DataFrame
-results_df1 = {
-    'mpd_train': mpd_train_1,
-    'mpd_test': mpd_test_1,
-    'mgd_train': mgd_train_1,
-    'mgd_test': mgd_test_1,
-}
-results_df2 = {
-    'mpd_train': mpd_train_2,
-    'mpd_test': mpd_test_2,
-    'mgd_train': mgd_train_2,
-    'mgd_test': mgd_test_2,
-}
-results_df3 = {
-    'mpd_train': mpd_train_3,
-    'mpd_test': mpd_test_3,
-    'mgd_train': mgd_train_3,
-    'mgd_test': mgd_test_3,
-}
-results_df4 = {
-    'mpd_train': mpd_train_4,
-    'mpd_test': mpd_test_4,
-    'mgd_train': mgd_train_4,
-    'mgd_test': mgd_test_4,
-}
-d1=pd.DataFrame(results_df1, index=['dataset 1'])
-d2=pd.DataFrame(results_df2, index=['synthetic dataset 1'])
-d3=pd.DataFrame(results_df3, index=['dataset 2'])
-d4=pd.DataFrame(results_df4, index=['synthetic dataset 2'])
-df_tot= pd.concat([d1,d2,d3,d4])
-st.dataframe(df_tot)
-
-
-# barplot comparison
-fig, ax = plt.subplots(figsize=(9, 5))
-df_tot.plot(kind='bar', ax=ax)
-ax.set_title('Comparison of MPD and MGD Metrics')
-ax.set_ylabel('Value')
-ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
-ax.legend(title='Metric')
-for container in ax.containers:
-    labels = ax.bar_label(container, fmt='%.2f', label_type='edge', padding=2)
-    for label in labels:
-        label.set_fontsize(8)
-
-plt.tight_layout()
-st.pyplot(fig)
-
-
-# MPD: Train vs Test Comparison
-fig, axes = plt.subplots(1, 2, figsize=(15, 6))
-
-# --- MPD Comparison ---
-mpd_data = df_tot[['mpd_train', 'mpd_test']]
-mpd_data.plot(kind='bar', ax=axes[0], color=['#2ecc71', '#e74c3c'])
-
-axes[0].set_title('Mean Poisson Deviance: Train vs Test', fontsize=16, fontweight='bold')
-axes[0].set_ylabel('MPD Value', fontsize=14)   
-axes[0].set_xlabel('Dataset', fontsize=14)     
-axes[0].legend(['Train', 'Test'], fontsize=10)
-
-# Larger tick labels
-axes[0].tick_params(axis='x', labelsize=12, rotation=45)
-axes[0].tick_params(axis='y', labelsize=12)
-
-axes[0].grid(axis='y', alpha=0.3)
-for container in axes[0].containers:
-    axes[0].bar_label(container, fmt='%.3f', fontsize=15)
-
-# --- MGD Comparison ---
-mgd_data = df_tot[['mgd_train', 'mgd_test']]
-mgd_data.plot(kind='bar', ax=axes[1], color=['#3498db', '#f39c12'])
-
-axes[1].set_title('Mean Gamma Deviance: Train vs Test', fontsize=16, fontweight='bold')
-axes[1].set_ylabel('MGD Value', fontsize=14)   
-axes[1].set_xlabel('Dataset', fontsize=14)     
-axes[1].legend(['Train', 'Test'], fontsize=10)
-
-# Larger tick labels
-axes[1].tick_params(axis='x', labelsize=12, rotation=45)
-axes[1].tick_params(axis='y', labelsize=12)
-
-axes[1].grid(axis='y', alpha=0.3)
-for container in axes[1].containers:
-    axes[1].bar_label(container, fmt='%.3f', fontsize=15)
-
-plt.tight_layout()
-st.pyplot(fig)
-
-# Create a heatmap
-fig, ax = plt.subplots(figsize=(10, 6))
-
-sns.heatmap(df_tot, annot=True, fmt='.3f', cmap='RdYlGn_r', 
-            linewidths=0.5, ax=ax, cbar_kws={'label': 'Deviance Value'})
-ax.set_title('Performance Heatmap: All Metrics Across Datasets', fontsize=15, fontweight='bold', pad=20)
-ax.set_xlabel('Metrics')
-ax.set_ylabel('Datasets')
-
-plt.tight_layout()
-st.pyplot(fig)
-