File size: 8,741 Bytes
b077775 b52804e b077775 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 | import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
plt.rcParams['figure.facecolor'] = '#1F2937'
plt.rcParams['axes.facecolor'] = '#0B0F19'
plt.rcParams['text.color'] = 'white'
plt.rcParams['axes.labelcolor'] = 'white'
plt.rcParams['xtick.color'] = 'white'
plt.rcParams['ytick.color'] = 'white'
def plot_feature_distributions(real_data, synthetic_data):
features = real_data.columns.to_list()
n_cols = 3
n_rows = (len(features) + n_cols - 1) // n_cols
fig, axes = plt.subplots(n_rows, n_cols, figsize=(18, 5 * n_rows))
for i, feature in enumerate(features):
row = i // n_cols
col = i % n_cols
sns.histplot(
real_data[feature],
bins=30,
color='skyblue',
stat='count',
element='step',
fill=True,
alpha=0.2,
ax=axes[row, col]
)
sns.histplot(
synthetic_data[feature],
bins=30,
color='indianred',
stat='count',
element='step',
fill=True,
alpha=0.2,
ax=axes[row, col]
)
axes[row, col].set_title(f'Distribution of {feature}')
axes[row, col].set_xlabel(feature)
axes[row, col].set_ylabel('Frequency')
axes[row, col].legend(['Real Data', 'Synthetic Data'])
for j in range(i + 1, n_rows * n_cols):
fig.delaxes(axes[j // n_cols, j % n_cols])
plt.tight_layout()
return fig
def get_metrics_df(y_true, y_real_pred, y_synth_pred):
metrics = {
'Model': ['Real Data Model', 'Synthetic Data Model'],
'Accuracy': [
accuracy_score(y_true, y_real_pred),
accuracy_score(y_true, y_synth_pred)
],
'Precision': [
precision_score(y_true, y_real_pred, average='weighted'),
precision_score(y_true, y_synth_pred, average='weighted')
],
'Recall': [
recall_score(y_true, y_real_pred, average='weighted'),
recall_score(y_true, y_synth_pred, average='weighted')
],
'F1-Score': [
f1_score(y_true, y_real_pred, average='weighted'),
f1_score(y_true, y_synth_pred, average='weighted')
]
}
return pd.DataFrame(metrics)
def plot_comparative_credit_score_distribution(
real_scores,
synth_scores,
bins=50,
title='Comparative Credit Score Distribution: Real vs Synthetic Models'
):
fig, axes = plt.subplots(1, 2, figsize=(16, 5), sharey=True)
sns.histplot(
real_scores,
bins=bins,
stat='count',
element='step',
fill=True,
alpha=0.2,
color='skyblue',
ax=axes[0]
)
axes[0].set_title('Real-Data Model Score Distribution')
axes[0].set_xlabel('Predicted Credit Score')
axes[0].set_ylabel('Frequency')
sns.histplot(
synth_scores,
bins=bins,
stat='count',
element='step',
fill=True,
alpha=0.2,
color='skyblue',
ax=axes[1]
)
axes[1].set_title('Synthetic-Data Model Score Distribution')
axes[1].set_xlabel('Predicted Credit Score')
axes[1].set_ylabel('Frequency')
plt.tight_layout()
return fig
def plot_comparison_table(
y_true, y_real_pred, y_synth_pred,
title='Model Comparison: Real Data vs Synthetic Data'
):
metrics_df = get_metrics_df(y_true, y_real_pred, y_synth_pred)
display_df = metrics_df.copy().round(4).set_index('Model')
fig, ax = plt.subplots(figsize=(18, 2))
ax.axis('off')
table = ax.table(
cellText=display_df.values,
rowLabels=display_df.index,
colLabels=display_df.columns,
cellLoc='center',
loc='center',
)
table.auto_set_font_size(False)
table.set_fontsize(16)
table.scale(1.2, 1.9)
for j in range(len(display_df.columns)):
table[(0, j)].set_facecolor('#1F77B4')
table[(0, j)].set_text_props(color='white', weight='bold')
table[(0, j)].set_edgecolor('white')
table[(0, j)].set_linewidth(1)
for i in range(1, len(display_df.index) + 1):
bg = '#0B0F19' if i % 2 else '#0B0F19'
table[(i, -1)].set_text_props(color='white', weight='bold')
table[(i, -1)].set_facecolor(bg)
table[(i, -1)].set_edgecolor('white')
table[(i, -1)].set_linewidth(1)
for j in range(len(display_df.columns)):
table[(i, j)].set_facecolor(bg)
table[(i, j)].set_text_props(color='white')
table[(i, j)].set_edgecolor('white')
table[(i, j)].set_linewidth(1)
plt.tight_layout()
return fig
def plot_comparative_confusion_matrices(
y_true,
y_pred_real,
y_pred_synth,
labels=None,
normalize=False,
cmap='Blues'
):
cm_real = confusion_matrix(y_true, y_pred_real, labels=labels)
cm_synth = confusion_matrix(y_true, y_pred_synth, labels=labels)
if normalize:
cm_real_plot = cm_real.astype(float) / cm_real.sum(axis=1, keepdims=True)
cm_synth_plot = cm_synth.astype(float) / cm_synth.sum(axis=1, keepdims=True)
fmt = '.2f'
else:
cm_real_plot = cm_real
cm_synth_plot = cm_synth
fmt = 'd'
fig, axes = plt.subplots(1, 2, figsize=(16, 6))
sns.heatmap(
cm_real_plot, annot=True, fmt=fmt, cmap=cmap,
xticklabels=labels, yticklabels=labels, ax=axes[0]
)
axes[0].set_title(f"Real Data Confusion Matrix")
axes[0].set_xlabel("Predicted")
axes[0].set_ylabel("Actual")
sns.heatmap(
cm_synth_plot, annot=True, fmt=fmt, cmap=cmap,
xticklabels=labels, yticklabels=labels, ax=axes[1]
)
axes[1].set_title(f"Synthetic Data Confusion Matrix")
axes[1].set_xlabel("Predicted")
axes[1].set_ylabel("Actual")
plt.tight_layout()
return fig
def plot_comparative_credit_score_distribution_by_actual_class(
y_true,
real_scores,
synth_scores,
color_map,
label_order=None,
bins=50,
):
fig, (ax_left, ax_right) = plt.subplots(1, 2, figsize=(16, 5), sharey=True)
y_true_arr = pd.Series(y_true).values
for label in label_order:
mask = (y_true_arr == label)
sns.histplot(
real_scores[mask],
bins=bins,
stat='count',
element='step',
fill=True,
alpha=0.2,
color=color_map.get(label, None),
label=label,
ax=ax_left
)
sns.histplot(
synth_scores[mask],
bins=bins,
stat='count',
element='step',
fill=True,
alpha=0.2,
color=color_map.get(label, None),
label=label,
ax=ax_right
)
ax_left.set_title('Real-Data Model: Actual Class Distribution')
ax_left.set_xlabel('Predicted Credit Score')
ax_left.set_ylabel('Frequency')
ax_left.legend(title='Actual Class')
ax_right.set_title('Synthetic-Data Model: Actual Class Distribution')
ax_right.set_xlabel('Predicted Credit Score')
ax_right.set_ylabel('Frequency')
ax_right.legend(title='Actual Class')
plt.tight_layout()
return fig
def plot_evaluation_table(summary_df, title="Synthetic Data Evaluation Summary"):
display_df = summary_df.copy().round(4)
fig, ax = plt.subplots(figsize=(18, 2))
ax.axis("off")
table = ax.table(
cellText = display_df.values,
rowLabels = display_df.index,
colLabels = display_df.columns,
cellLoc = "center",
loc = "center",
)
table.auto_set_font_size(False)
table.set_fontsize(16)
table.scale(1.2, 1.9)
for j in range(len(display_df.columns)):
table[(0, j)].set_facecolor("#1F77B4")
table[(0, j)].set_text_props(color="white", weight="bold")
table[(0, j)].set_edgecolor("white")
table[(0, j)].set_linewidth(1)
for i in range(1, len(display_df.index) + 1):
bg = "#0B0F19"
table[(i, -1)].set_text_props(color="white", weight="bold")
table[(i, -1)].set_facecolor(bg)
table[(i, -1)].set_edgecolor("white")
table[(i, -1)].set_linewidth(1)
for j in range(len(display_df.columns)):
table[(i, j)].set_facecolor(bg)
table[(i, j)].set_text_props(color="white")
table[(i, j)].set_edgecolor("white")
table[(i, j)].set_linewidth(1)
ax.set_title(title, color="white", fontsize=16, weight="bold", pad=12)
plt.tight_layout()
return fig |