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265 | 265 | "X_num = X[:, -4:].astype(np.float32, copy=False)\n", |
266 | 266 | "xmin, xmax = X_num.min(axis=0), X_num.max(axis=0)\n", |
267 | 267 | "rng = (-1., 1.)\n", |
268 | | - "X_num_scaled = (X_num - xmin) / (xmax - xmin) * (rng[1] - rng[0]) + rng[0]\n", |
269 | | - "X_num_scaled_train = X_num_scaled[:idx, :]\n", |
270 | | - "X_num_scaled_test = X_num_scaled[idx+1:, :]" |
| 268 | + "X_num_scaled = (X_num - xmin) / (xmax - xmin) * (rng[1] - rng[0]) + rng[0]" |
271 | 269 | ] |
272 | 270 | }, |
273 | 271 | { |
|
284 | 282 | "outputs": [], |
285 | 283 | "source": [ |
286 | 284 | "X_cat = X[:, :-4].copy()\n", |
287 | | - "ohe = OneHotEncoder(categories='auto')\n", |
288 | | - "ohe.fit(X_cat)\n", |
| 285 | + "ohe = OneHotEncoder(categories='auto', sparse=False).fit(X_cat)\n", |
289 | 286 | "X_cat_ohe = ohe.transform(X_cat)" |
290 | 287 | ] |
291 | 288 | }, |
|
310 | 307 | } |
311 | 308 | ], |
312 | 309 | "source": [ |
313 | | - "X = np.c_[X_cat_ohe.todense(), X_num_scaled].astype(np.float32, copy=False)\n", |
| 310 | + "X = np.c_[X_cat_ohe, X_num_scaled].astype(np.float32, copy=False)\n", |
314 | 311 | "X_train, X_test = X[:idx, :], X[idx+1:, :]\n", |
315 | 312 | "print(X_train.shape, X_test.shape)" |
316 | 313 | ] |
|
601 | 598 | " print('\\nNumerical:')\n", |
602 | 599 | " delta_num = X_cf_ord[0, -4:] - X_orig_ord[0, -4:]\n", |
603 | 600 | " n_keys = len(list(cat_vars_ord.keys()))\n", |
604 | | - " for i in range(delta_num.shape[1]):\n", |
605 | | - " if np.abs(delta_num[0, i]) > eps:\n", |
| 601 | + " for i in range(delta_num.shape[0]):\n", |
| 602 | + " if np.abs(delta_num[i]) > eps:\n", |
606 | 603 | " print('{}: {:.2f} --> {:.2f}'.format(feature_names[i+n_keys],\n", |
607 | 604 | " X_orig_ord[0,i+n_keys],\n", |
608 | 605 | " X_cf_ord[0,i+n_keys]))" |
|
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