|
87 | 87 | " DataRowMetadata,\n", |
88 | 88 | " DataRowMetadataField,\n", |
89 | 89 | " DeleteDataRowMetadata,\n", |
90 | | - " DataRowMetadataKind\n", |
91 | 90 | ")\n", |
92 | 91 | "from sklearn.random_projection import GaussianRandomProjection\n", |
| 92 | + "import tensorflow as tf\n", |
93 | 93 | "import seaborn as sns\n", |
94 | | - "from datetime import datetime\n", |
95 | | - "from pprint import pprint\n", |
96 | 94 | "import tensorflow_hub as hub\n", |
| 95 | + "from datetime import datetime\n", |
97 | 96 | "from tqdm.notebook import tqdm\n", |
98 | 97 | "import requests\n", |
99 | | - "import tensorflow as tf\n", |
100 | 98 | "from pprint import pprint" |
101 | 99 | ] |
102 | 100 | }, |
|
154 | 152 | "outputs": [], |
155 | 153 | "source": [ |
156 | 154 | "# dictionary access with id\n", |
157 | | - "pprint(mdo.all_fields_id_index, indent=2)" |
| 155 | + "pprint(mdo.fields_by_id, indent=2)" |
158 | 156 | ] |
159 | 157 | }, |
160 | 158 | { |
|
167 | 165 | "outputs": [], |
168 | 166 | "source": [ |
169 | 167 | "# access by name\n", |
170 | | - "split_field = mdo.reserved_name_index[\"split\"]" |
171 | | - ] |
172 | | - }, |
173 | | - { |
174 | | - "cell_type": "code", |
175 | | - "execution_count": null, |
176 | | - "id": "uOS2QlHmqAIs", |
177 | | - "metadata": { |
178 | | - "id": "uOS2QlHmqAIs" |
179 | | - }, |
180 | | - "outputs": [], |
181 | | - "source": [ |
182 | | - "split_field.options" |
| 168 | + "split_field = mdo.reserved_by_name[\"split\"]\n", |
| 169 | + "train_field = mdo.reserved_by_name[\"split\"][\"train\"]" |
183 | 170 | ] |
184 | 171 | }, |
185 | 172 | { |
|
191 | 178 | }, |
192 | 179 | "outputs": [], |
193 | 180 | "source": [ |
194 | | - "tag_field = mdo.reserved_name_index[\"tag\"]" |
| 181 | + "tag_field = mdo.reserved_by_name[\"tag\"]" |
195 | 182 | ] |
196 | 183 | }, |
197 | 184 | { |
|
286 | 273 | "outputs": [], |
287 | 274 | "source": [ |
288 | 275 | "field = DataRowMetadataField(\n", |
289 | | - " schema_id=mdo.reserved_name_index[\"captureDateTime\"].id, # specify the schema id\n", |
| 276 | + " schema_id=mdo.reserved_by_name[\"captureDateTime\"].id, # specify the schema id\n", |
290 | 277 | " value=datetime.now(), # typed inputs\n", |
291 | 278 | ")\n", |
292 | 279 | "# Completed object ready for upload\n", |
|
356 | 343 | " # assign datarows a split\n", |
357 | 344 | " rnd = random.random()\n", |
358 | 345 | " if rnd < test:\n", |
359 | | - " split = \"cko8scbz70005h2dkastwhgqt\"\n", |
| 346 | + " split = mdo.reserved_by_name[\"split\"][\"test\"]\n", |
360 | 347 | " elif rnd < valid:\n", |
361 | | - " split = \"cko8sc2yr0004h2dk69aj5x63\"\n", |
| 348 | + " split = mdo.reserved_by_name[\"split\"][\"valid\"]\n", |
362 | 349 | " else:\n", |
363 | | - " split = \"cko8sbscr0003h2dk04w86hof\"\n", |
| 350 | + " split = mdo.reserved_by_name[\"split\"][\"train\"]\n", |
364 | 351 | " \n", |
365 | 352 | " embeddings.append(list(model(processor(response.content), training=False)[0].numpy()))\n", |
366 | 353 | " dt = datetime.utcnow() \n", |
|
371 | 358 | " data_row_id=datarow.uid,\n", |
372 | 359 | " fields=[\n", |
373 | 360 | " DataRowMetadataField(\n", |
374 | | - " schema_id=mdo.reserved_name_index[\"captureDateTime\"].id,\n", |
| 361 | + " schema_id=mdo.reserved_by_name[\"captureDateTime\"].uid,\n", |
375 | 362 | " value=dt,\n", |
376 | 363 | " ),\n", |
377 | 364 | " DataRowMetadataField(\n", |
378 | | - " schema_id=mdo.reserved_name_index[\"split\"].id,\n", |
379 | | - " value=split\n", |
| 365 | + " schema_id=split.parent,\n", |
| 366 | + " value=split.uid\n", |
380 | 367 | " ),\n", |
381 | 368 | " DataRowMetadataField(\n", |
382 | | - " schema_id=mdo.reserved_name_index[\"tag\"].id,\n", |
| 369 | + " schema_id=mdo.reserved_by_name[\"tag\"].uid,\n", |
383 | 370 | " value=message\n", |
384 | 371 | " ),\n", |
385 | 372 | " ]\n", |
|
438 | 425 | "for md, embd in zip(uploads, projected):\n", |
439 | 426 | " md.fields.append(\n", |
440 | 427 | " DataRowMetadataField(\n", |
441 | | - " schema_id=mdo.reserved_name_index[\"embedding\"].id,\n", |
| 428 | + " schema_id=mdo.reserved_by_name[\"embedding\"].uid,\n", |
442 | 429 | " value=embd.tolist(), # convert from numpy to list\n", |
443 | 430 | " ),\n", |
444 | 431 | " )" |
|
568 | 555 | "fields = []\n", |
569 | 556 | "# iterate through the fields you want to delete\n", |
570 | 557 | "for field in md.fields:\n", |
571 | | - " schema = mdo.all_fields_id_index[field.schema_id]\n", |
572 | 558 | " fields.append(field.schema_id)\n", |
573 | 559 | "\n", |
574 | 560 | "deletes = DeleteDataRowMetadata(\n", |
|
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