|
107 | 107 | for ax, model in zip(axs, models): |
108 | 108 | t_train = model.fit_transform(X_train_scaled, y_train) |
109 | 109 | t_test = model.transform(X_test_scaled) |
110 | | - |
111 | | - ax.scatter(t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train) |
| 110 | + |
| 111 | + ax.scatter( |
| 112 | + t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train |
| 113 | + ) |
112 | 114 | ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_test, cmap=cm_bright, c=y_test) |
113 | | - |
114 | 115 |
|
115 | 116 | ax.set_title(models[model]) |
116 | 117 | plt.tight_layout() |
|
168 | 169 | eps=models[model]["eps"], |
169 | 170 | grid_resolution=resolution, |
170 | 171 | ) |
171 | | - ax.scatter(t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train) |
| 172 | + ax.scatter( |
| 173 | + t_train[:, 0], t_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train |
| 174 | + ) |
172 | 175 | ax.scatter(t_test[:, 0], t_test[:, 1], alpha=alpha_test, cmap=cm_bright, c=y_test) |
173 | 176 | ax.set_title(models[model]["title"]) |
174 | 177 |
|
|
242 | 245 | eps=models[model].get("eps", 1), |
243 | 246 | grid_resolution=resolution, |
244 | 247 | ) |
245 | | - |
246 | | - ax.scatter(t_kpcovc_train[:, 0], t_kpcovc_train[:, 1], alpha=alpha_train, cmap=cm_bright, c=y_train) |
247 | | - |
| 248 | + |
| 249 | + ax.scatter( |
| 250 | + t_kpcovc_train[:, 0], |
| 251 | + t_kpcovc_train[:, 1], |
| 252 | + alpha=alpha_train, |
| 253 | + cmap=cm_bright, |
| 254 | + c=y_train, |
| 255 | + ) |
| 256 | + |
248 | 257 | ax.scatter( |
249 | 258 | t_kpcovc_test[:, 0], |
250 | 259 | t_kpcovc_test[:, 1], |
251 | 260 | cmap=cm_bright, |
252 | 261 | alpha=alpha_test, |
253 | 262 | c=y_test, |
254 | 263 | ) |
255 | | - |
| 264 | + |
256 | 265 | ax.text( |
257 | 266 | 0.70, |
258 | 267 | 0.03, |
|
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