|
80 | 80 | est = make_pipeline( |
81 | 81 | QuantileTransformer(), |
82 | 82 | MLPRegressor( |
83 | | - hidden_layer_sizes=(50, 50), learning_rate_init=0.01, early_stopping=True |
| 83 | + hidden_layer_sizes=(30, 15), |
| 84 | + learning_rate_init=0.01, |
| 85 | + early_stopping=True, |
| 86 | + random_state=0, |
84 | 87 | ), |
85 | 88 | ) |
86 | 89 | est.fit(X_train, y_train) |
|
145 | 148 |
|
146 | 149 | print("Training HistGradientBoostingRegressor...") |
147 | 150 | tic = time() |
148 | | -est = HistGradientBoostingRegressor() |
| 151 | +est = HistGradientBoostingRegressor(random_state=0) |
149 | 152 | est.fit(X_train, y_train) |
150 | 153 | print(f"done in {time() - tic:.3f}s") |
151 | 154 | print(f"Test R2 score: {est.score(X_test, y_test):.2f}") |
|
233 | 236 | X_train, |
234 | 237 | features, |
235 | 238 | kind="average", |
236 | | - n_jobs=3, |
237 | | - grid_resolution=20, |
| 239 | + n_jobs=2, |
| 240 | + grid_resolution=10, |
238 | 241 | ax=ax, |
239 | 242 | ) |
240 | 243 | print(f"done in {time() - tic:.3f}s") |
|
265 | 268 |
|
266 | 269 | features = ("AveOccup", "HouseAge") |
267 | 270 | pdp = partial_dependence( |
268 | | - est, X_train, features=features, kind="average", grid_resolution=20 |
| 271 | + est, X_train, features=features, kind="average", grid_resolution=10 |
269 | 272 | ) |
270 | 273 | XX, YY = np.meshgrid(pdp["values"][0], pdp["values"][1]) |
271 | 274 | Z = pdp.average[0].T |
272 | 275 | ax = Axes3D(fig) |
273 | 276 | fig.add_axes(ax) |
| 277 | + |
274 | 278 | surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor="k") |
275 | 279 | ax.set_xlabel(features[0]) |
276 | 280 | ax.set_ylabel(features[1]) |
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