Build-Random-Forest-From-scratch/visualization.py at main · codewithdark-git/Build-Random-Forest-From-scratch · GitHub

History

442 lines (360 loc) · 14.2 KB

Raw

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

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

"""

Visualization utilities for Random Forest experiments.

Includes functions for plotting accuracy curves, confusion matrices,

feature importance, and training time comparisons.

"""

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.metrics import confusion_matrix

import os

import config

# Set plotting style

try:

plt.style.use(config.PLOT_STYLE)

except:

plt.style.use('default')

sns.set_palette("husl")

def plot_accuracy_vs_n_estimators(

n_estimators_list: list,

train_accuracies: list,

test_accuracies: list,

title: str = "Accuracy vs Number of Trees",

filename: str = None

) -> None:

"""

Plot accuracy as a function of number of trees.

Parameters:

-----------

n_estimators_list : list

List of n_estimators values

train_accuracies : list

Training accuracies for each n_estimators

test_accuracies : list

Test accuracies for each n_estimators

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

plt.figure(figsize=config.FIGURE_SIZE, dpi=config.DPI)

plt.plot(n_estimators_list, train_accuracies, 'o-',

label='Training Accuracy', linewidth=2, markersize=8)

plt.plot(n_estimators_list, test_accuracies, 's-',

label='Test Accuracy', linewidth=2, markersize=8)

plt.xlabel('Number of Trees (n_estimators)', fontsize=12, fontweight='bold')

plt.ylabel('Accuracy', fontsize=12, fontweight='bold')

plt.title(title, fontsize=14, fontweight='bold')

plt.legend(fontsize=11)

plt.grid(True, alpha=0.3)

# Add value annotations

for i, (n_est, acc) in enumerate(zip(n_estimators_list, test_accuracies)):

plt.annotate(f'{acc:.3f}',

xy=(n_est, acc),

xytext=(5, 5),

textcoords='offset points',

fontsize=9,

alpha=0.7)

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def plot_training_time_comparison(

n_estimators_list: list,

training_times: list,

title: str = "Training Time vs Number of Trees",

filename: str = None

) -> None:

"""

Plot training time as a function of number of trees.

Parameters:

-----------

n_estimators_list : list

List of n_estimators values

training_times : list

Training times for each n_estimators

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

plt.figure(figsize=config.FIGURE_SIZE, dpi=config.DPI)

plt.plot(n_estimators_list, training_times, 'o-',

linewidth=2, markersize=8, color='coral')

plt.xlabel('Number of Trees (n_estimators)', fontsize=12, fontweight='bold')

plt.ylabel('Training Time (seconds)', fontsize=12, fontweight='bold')

plt.title(title, fontsize=14, fontweight='bold')

plt.grid(True, alpha=0.3)

# Add value annotations

for i, (n_est, time) in enumerate(zip(n_estimators_list, training_times)):

plt.annotate(f'{time:.2f}s',

xy=(n_est, time),

xytext=(5, 5),

textcoords='offset points',

fontsize=9,

alpha=0.7)

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def plot_tree_vs_forest_comparison(

metrics_dict: dict,

title: str = "Decision Tree vs Random Forest Comparison",

filename: str = None

) -> None:

"""

Plot comparison between Decision Tree and Random Forest.

Parameters:

-----------

metrics_dict : dict

Dictionary with 'tree' and 'forest' keys, each containing metrics

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

fig, axes = plt.subplots(1, 2, figsize=(14, 6), dpi=config.DPI)

# Accuracy comparison

models = ['Decision Tree', 'Random Forest']

train_accs = [metrics_dict['tree']['train_accuracy'],

metrics_dict['forest']['train_accuracy']]

test_accs = [metrics_dict['tree']['test_accuracy'],

metrics_dict['forest']['test_accuracy']]

x = np.arange(len(models))

width = 0.35

axes[0].bar(x - width/2, train_accs, width, label='Training', alpha=0.8)

axes[0].bar(x + width/2, test_accs, width, label='Test', alpha=0.8)

axes[0].set_ylabel('Accuracy', fontsize=12, fontweight='bold')

axes[0].set_title('Accuracy Comparison', fontsize=12, fontweight='bold')

axes[0].set_xticks(x)

axes[0].set_xticklabels(models)

axes[0].legend()

axes[0].grid(True, alpha=0.3, axis='y')

# Add value labels

for i, (train, test) in enumerate(zip(train_accs, test_accs)):

axes[0].text(i - width/2, train + 0.01, f'{train:.3f}',

ha='center', va='bottom', fontsize=9)

axes[0].text(i + width/2, test + 0.01, f'{test:.3f}',

ha='center', va='bottom', fontsize=9)

# Training time comparison

times = [metrics_dict['tree']['training_time'],

metrics_dict['forest']['training_time']]

axes[1].bar(models, times, alpha=0.8, color=['skyblue', 'lightcoral'])

axes[1].set_ylabel('Training Time (seconds)', fontsize=12, fontweight='bold')

axes[1].set_title('Training Time Comparison', fontsize=12, fontweight='bold')

axes[1].grid(True, alpha=0.3, axis='y')

# Add value labels

for i, time in enumerate(times):

axes[1].text(i, time + max(times)*0.02, f'{time:.3f}s',

ha='center', va='bottom', fontsize=9)

plt.suptitle(title, fontsize=14, fontweight='bold', y=1.02)

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def plot_confusion_matrix(

y_true: np.ndarray,

y_pred: np.ndarray,

class_names: list = None,

title: str = "Confusion Matrix",

filename: str = None

) -> None:

"""

Plot confusion matrix.

Parameters:

-----------

y_true : array

True labels

y_pred : array

Predicted labels

class_names : list, optional

Names of classes

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

cm = confusion_matrix(y_true, y_pred)

plt.figure(figsize=(8, 6), dpi=config.DPI)

sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',

xticklabels=class_names, yticklabels=class_names,

cbar_kws={'label': 'Count'})

plt.xlabel('Predicted Label', fontsize=12, fontweight='bold')

plt.ylabel('True Label', fontsize=12, fontweight='bold')

plt.title(title, fontsize=14, fontweight='bold')

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def plot_feature_importance(

importances: np.ndarray,

feature_names: list = None,

top_n: int = 10,

title: str = "Feature Importance",

filename: str = None

) -> None:

"""

Plot feature importance.

Parameters:

-----------

importances : array

Feature importance values

feature_names : list, optional

Names of features

top_n : int

Number of top features to show

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

if feature_names is None:

feature_names = [f'Feature {i}' for i in range(len(importances))]

# Sort by importance

indices = np.argsort(importances)[::-1][:top_n]

top_importances = importances[indices]

top_features = [feature_names[i] for i in indices]

plt.figure(figsize=(10, 6), dpi=config.DPI)

colors = plt.cm.viridis(np.linspace(0, 1, len(top_importances)))

plt.barh(range(len(top_importances)), top_importances, color=colors, alpha=0.8)

plt.yticks(range(len(top_importances)), top_features)

plt.xlabel('Importance', fontsize=12, fontweight='bold')

plt.title(title, fontsize=14, fontweight='bold')

plt.gca().invert_yaxis()

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

# Add value labels

for i, imp in enumerate(top_importances):

plt.text(imp + max(top_importances)*0.01, i, f'{imp:.4f}',

va='center', fontsize=9)

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def plot_all_metrics(

metrics_dict: dict,

title: str = "Model Performance Metrics",

filename: str = None

) -> None:

"""

Plot all metrics (accuracy, precision, recall, F1) for comparison.

Parameters:

-----------

metrics_dict : dict

Dictionary with model names as keys and metrics as values

title : str

Plot title

filename : str, optional

If provided, save plot to this filename

"""

metric_names = ['accuracy', 'precision', 'recall', 'f1']

models = list(metrics_dict.keys())

fig, ax = plt.subplots(figsize=(12, 6), dpi=config.DPI)

x = np.arange(len(metric_names))

width = 0.35

for i, model in enumerate(models):

values = [metrics_dict[model].get(metric, 0) for metric in metric_names]

offset = (i - len(models)/2 + 0.5) * width

ax.bar(x + offset, values, width, label=model, alpha=0.8)

ax.set_ylabel('Score', fontsize=12, fontweight='bold')

ax.set_title(title, fontsize=14, fontweight='bold')

ax.set_xticks(x)

ax.set_xticklabels([m.capitalize() for m in metric_names])

ax.legend()

ax.grid(True, alpha=0.3, axis='y')

ax.set_ylim([0, 1.1])

plt.tight_layout()

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Plot saved to {filepath}")

plt.show()

def create_summary_plot(

experiment_results: dict,

dataset_name: str,

filename: str = None

) -> None:

"""

Create a comprehensive summary plot with multiple subplots.

Parameters:

-----------

experiment_results : dict

Dictionary containing all experiment results

dataset_name : str

Name of the dataset

filename : str, optional

If provided, save plot to this filename

"""

fig = plt.figure(figsize=(16, 10), dpi=config.DPI)

gs = fig.add_gridspec(2, 2, hspace=0.3, wspace=0.3)

# Plot 1: Accuracy vs n_estimators

ax1 = fig.add_subplot(gs[0, 0])

n_est = experiment_results['n_estimators_list']

# Handle new key names (prefer sklearn for summary)

if 'sklearn_test_accuracies' in experiment_results:

test_acc = experiment_results['sklearn_test_accuracies']

else:

test_acc = experiment_results.get('test_accuracies', [])

ax1.plot(n_est, test_acc, 'o-', linewidth=2, markersize=8)

ax1.set_xlabel('Number of Trees', fontweight='bold')

ax1.set_ylabel('Test Accuracy', fontweight='bold')

ax1.set_title('Accuracy vs Number of Trees', fontweight='bold')

ax1.grid(True, alpha=0.3)

# Plot 2: Training time vs n_estimators

ax2 = fig.add_subplot(gs[0, 1])

if 'sklearn_training_times' in experiment_results:

times = experiment_results['sklearn_training_times']

else:

times = experiment_results.get('training_times', [])

ax2.plot(n_est, times, 's-', linewidth=2, markersize=8, color='coral')

ax2.set_xlabel('Number of Trees', fontweight='bold')

ax2.set_ylabel('Training Time (s)', fontweight='bold')

ax2.set_title('Training Time vs Number of Trees', fontweight='bold')

ax2.grid(True, alpha=0.3)

# Plot 3: Tree vs Forest comparison

ax3 = fig.add_subplot(gs[1, 0])

models = ['Decision Tree', 'Random Forest']

# Handle new key names

if 'sklearn_tree_metrics' in experiment_results:

tree_metrics = experiment_results['sklearn_tree_metrics']

forest_metrics = experiment_results['sklearn_forest_metrics']

else:

tree_metrics = experiment_results.get('tree_metrics', {'test_accuracy': 0})

forest_metrics = experiment_results.get('forest_metrics', {'test_accuracy': 0})

tree_acc = tree_metrics.get('test_accuracy', 0)

forest_acc = forest_metrics.get('test_accuracy', 0)

ax3.bar(models, [tree_acc, forest_acc], alpha=0.8, color=['skyblue', 'lightcoral'])

ax3.set_ylabel('Test Accuracy', fontweight='bold')

ax3.set_title('Model Comparison', fontweight='bold')

ax3.grid(True, alpha=0.3, axis='y')

for i, acc in enumerate([tree_acc, forest_acc]):

ax3.text(i, acc + 0.01, f'{acc:.3f}', ha='center', va='bottom')

# Plot 4: Feature importance (if available)

ax4 = fig.add_subplot(gs[1, 1])

if 'feature_importances' in experiment_results:

importances = experiment_results['feature_importances']

top_n = min(10, len(importances))

indices = np.argsort(importances)[::-1][:top_n]

ax4.barh(range(top_n), importances[indices], alpha=0.8)

ax4.set_yticks(range(top_n))

ax4.set_yticklabels([f'F{i}' for i in indices])

ax4.set_xlabel('Importance', fontweight='bold')

ax4.set_title('Top 10 Feature Importances', fontweight='bold')

ax4.invert_yaxis()

ax4.grid(True, alpha=0.3, axis='x')

plt.suptitle(f'Random Forest Experiments - {dataset_name}',

fontsize=16, fontweight='bold', y=0.995)

if filename:

filepath = os.path.join(config.PLOTS_DIR, filename)

plt.savefig(filepath, dpi=config.DPI, bbox_inches='tight')

print(f"Summary plot saved to {filepath}")

plt.show()

Build-Random-Forest-From-scratch/visualization.py at main · codewithdark-git/Build-Random-Forest-From-scratch · GitHub

Expand file tree

Breadcrumbs

visualization.py

Latest commit

History

Breadcrumbs

visualization.py

File metadata and controls