Update tests.
Browse files- tests.ipynb +504 -73
tests.ipynb
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"nbformat": 4,
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"metadata": {
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"colab": {
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"display_name": "Python 3"
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"cells": [
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"cell_type": "code",
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"
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"!pip install -U datasets huggingface_hub fsspec"
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "OSWN0xQn6z8u",
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"outputId": "bb05c540-e196-49c3-cf7a-d2f4942abe52"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (2.14.4)\n",
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"Collecting datasets\n",
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"
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"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.32.4)\n",
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"Collecting huggingface_hub\n",
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"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (2025.3.2)\n",
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"Collecting fsspec\n",
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" Downloading fsspec-2025.5.1-py3-none-any.whl.metadata (11 kB)\n",
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"cell_type": "markdown",
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"source": [
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"### 📥 Step 1: Preprocess the Data\n",
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"\n",
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"Since the dataset is hosted on Hugging Face, you can load it directly using the `load_dataset` function."
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"metadata": {
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"cell_type": "code",
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"source": [
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"from datasets import load_dataset\n",
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"\n",
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"# Convert to a pandas DataFrame for easier manipulation\n",
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"df = pd.DataFrame(dataset[\"train\"])\n",
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"cells": [
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "OSWN0xQn6z8u",
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"outputId": "bb05c540-e196-49c3-cf7a-d2f4942abe52"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting datasets\n",
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" Using cached datasets-3.6.0-py3-none-any.whl.metadata (19 kB)\n",
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"Collecting huggingface_hub\n",
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" Using cached huggingface_hub-0.33.0-py3-none-any.whl.metadata (14 kB)\n",
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"Collecting fsspec\n",
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" Downloading fsspec-2025.5.1-py3-none-any.whl.metadata (11 kB)\n",
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"Collecting filelock (from datasets)\n",
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" Using cached filelock-3.18.0-py3-none-any.whl.metadata (2.9 kB)\n",
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"Collecting numpy>=1.17 (from datasets)\n",
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" Using cached numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl.metadata (62 kB)\n",
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"Collecting pyarrow>=15.0.0 (from datasets)\n",
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" Using cached pyarrow-20.0.0-cp313-cp313-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
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"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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"Collecting fsspec\n",
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" Using cached fsspec-2025.3.0-py3-none-any.whl.metadata (11 kB)\n",
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"Requirement already satisfied: packaging in ./.venv/lib/python3.13/site-packages (from datasets) (25.0)\n",
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"Collecting pyyaml>=5.1 (from datasets)\n",
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"Collecting typing-extensions>=3.7.4.3 (from huggingface_hub)\n",
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"Collecting aiohappyeyeballs>=2.5.0 (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets)\n",
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"source": [
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"### 📥 Step 1: Preprocess the Data\n",
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"outputs": [
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"text": [
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"/home/escher/Projects/OpenPsychometrics/2018-11-08-IPIP-FFM-data/2018-11-08-OpenPsychometrics-IPIP-FFM/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n",
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+
" <td>194.0</td>\n",
|
| 307 |
+
" <td>4</td>\n",
|
| 308 |
+
" <td>2</td>\n",
|
| 309 |
+
" <td>US</td>\n",
|
| 310 |
+
" <td>37.5402</td>\n",
|
| 311 |
+
" <td>-122.3041</td>\n",
|
| 312 |
+
" <td>839500</td>\n",
|
| 313 |
+
" </tr>\n",
|
| 314 |
+
" <tr>\n",
|
| 315 |
+
" <th>...</th>\n",
|
| 316 |
+
" <td>...</td>\n",
|
| 317 |
+
" <td>...</td>\n",
|
| 318 |
+
" <td>...</td>\n",
|
| 319 |
+
" <td>...</td>\n",
|
| 320 |
+
" <td>...</td>\n",
|
| 321 |
+
" <td>...</td>\n",
|
| 322 |
+
" <td>...</td>\n",
|
| 323 |
+
" <td>...</td>\n",
|
| 324 |
+
" <td>...</td>\n",
|
| 325 |
+
" <td>...</td>\n",
|
| 326 |
+
" <td>...</td>\n",
|
| 327 |
+
" <td>...</td>\n",
|
| 328 |
+
" <td>...</td>\n",
|
| 329 |
+
" <td>...</td>\n",
|
| 330 |
+
" <td>...</td>\n",
|
| 331 |
+
" <td>...</td>\n",
|
| 332 |
+
" <td>...</td>\n",
|
| 333 |
+
" <td>...</td>\n",
|
| 334 |
+
" <td>...</td>\n",
|
| 335 |
+
" <td>...</td>\n",
|
| 336 |
+
" <td>...</td>\n",
|
| 337 |
+
" </tr>\n",
|
| 338 |
+
" <tr>\n",
|
| 339 |
+
" <th>609199</th>\n",
|
| 340 |
+
" <td>1.0</td>\n",
|
| 341 |
+
" <td>5.0</td>\n",
|
| 342 |
+
" <td>1.0</td>\n",
|
| 343 |
+
" <td>5.0</td>\n",
|
| 344 |
+
" <td>1.0</td>\n",
|
| 345 |
+
" <td>5.0</td>\n",
|
| 346 |
+
" <td>1.0</td>\n",
|
| 347 |
+
" <td>5.0</td>\n",
|
| 348 |
+
" <td>1.0</td>\n",
|
| 349 |
+
" <td>5.0</td>\n",
|
| 350 |
+
" <td>...</td>\n",
|
| 351 |
+
" <td>360.0</td>\n",
|
| 352 |
+
" <td>640.0</td>\n",
|
| 353 |
+
" <td>6.0</td>\n",
|
| 354 |
+
" <td>364.0</td>\n",
|
| 355 |
+
" <td>12</td>\n",
|
| 356 |
+
" <td>1</td>\n",
|
| 357 |
+
" <td>US</td>\n",
|
| 358 |
+
" <td>38.0</td>\n",
|
| 359 |
+
" <td>-97.0</td>\n",
|
| 360 |
+
" <td>259178</td>\n",
|
| 361 |
+
" </tr>\n",
|
| 362 |
+
" <tr>\n",
|
| 363 |
+
" <th>609200</th>\n",
|
| 364 |
+
" <td>4.0</td>\n",
|
| 365 |
+
" <td>1.0</td>\n",
|
| 366 |
+
" <td>4.0</td>\n",
|
| 367 |
+
" <td>3.0</td>\n",
|
| 368 |
+
" <td>4.0</td>\n",
|
| 369 |
+
" <td>0.0</td>\n",
|
| 370 |
+
" <td>5.0</td>\n",
|
| 371 |
+
" <td>2.0</td>\n",
|
| 372 |
+
" <td>4.0</td>\n",
|
| 373 |
+
" <td>2.0</td>\n",
|
| 374 |
+
" <td>...</td>\n",
|
| 375 |
+
" <td>360.0</td>\n",
|
| 376 |
+
" <td>640.0</td>\n",
|
| 377 |
+
" <td>15.0</td>\n",
|
| 378 |
+
" <td>180.0</td>\n",
|
| 379 |
+
" <td>17</td>\n",
|
| 380 |
+
" <td>1</td>\n",
|
| 381 |
+
" <td>CR</td>\n",
|
| 382 |
+
" <td>9.8533</td>\n",
|
| 383 |
+
" <td>-83.9023</td>\n",
|
| 384 |
+
" <td>365838</td>\n",
|
| 385 |
+
" </tr>\n",
|
| 386 |
+
" <tr>\n",
|
| 387 |
+
" <th>609201</th>\n",
|
| 388 |
+
" <td>4.0</td>\n",
|
| 389 |
+
" <td>2.0</td>\n",
|
| 390 |
+
" <td>3.0</td>\n",
|
| 391 |
+
" <td>2.0</td>\n",
|
| 392 |
+
" <td>5.0</td>\n",
|
| 393 |
+
" <td>1.0</td>\n",
|
| 394 |
+
" <td>4.0</td>\n",
|
| 395 |
+
" <td>2.0</td>\n",
|
| 396 |
+
" <td>5.0</td>\n",
|
| 397 |
+
" <td>2.0</td>\n",
|
| 398 |
+
" <td>...</td>\n",
|
| 399 |
+
" <td>400.0</td>\n",
|
| 400 |
+
" <td>640.0</td>\n",
|
| 401 |
+
" <td>24.0</td>\n",
|
| 402 |
+
" <td>519.0</td>\n",
|
| 403 |
+
" <td>392</td>\n",
|
| 404 |
+
" <td>6</td>\n",
|
| 405 |
+
" <td>US</td>\n",
|
| 406 |
+
" <td>38.0</td>\n",
|
| 407 |
+
" <td>-97.0</td>\n",
|
| 408 |
+
" <td>131932</td>\n",
|
| 409 |
+
" </tr>\n",
|
| 410 |
+
" <tr>\n",
|
| 411 |
+
" <th>609202</th>\n",
|
| 412 |
+
" <td>1.0</td>\n",
|
| 413 |
+
" <td>4.0</td>\n",
|
| 414 |
+
" <td>3.0</td>\n",
|
| 415 |
+
" <td>3.0</td>\n",
|
| 416 |
+
" <td>2.0</td>\n",
|
| 417 |
+
" <td>4.0</td>\n",
|
| 418 |
+
" <td>2.0</td>\n",
|
| 419 |
+
" <td>4.0</td>\n",
|
| 420 |
+
" <td>2.0</td>\n",
|
| 421 |
+
" <td>5.0</td>\n",
|
| 422 |
+
" <td>...</td>\n",
|
| 423 |
+
" <td>1368.0</td>\n",
|
| 424 |
+
" <td>912.0</td>\n",
|
| 425 |
+
" <td>217.0</td>\n",
|
| 426 |
+
" <td>209.0</td>\n",
|
| 427 |
+
" <td>17</td>\n",
|
| 428 |
+
" <td>2</td>\n",
|
| 429 |
+
" <td>US</td>\n",
|
| 430 |
+
" <td>38.0</td>\n",
|
| 431 |
+
" <td>-97.0</td>\n",
|
| 432 |
+
" <td>671155</td>\n",
|
| 433 |
+
" </tr>\n",
|
| 434 |
+
" <tr>\n",
|
| 435 |
+
" <th>609203</th>\n",
|
| 436 |
+
" <td>4.0</td>\n",
|
| 437 |
+
" <td>2.0</td>\n",
|
| 438 |
+
" <td>2.0</td>\n",
|
| 439 |
+
" <td>3.0</td>\n",
|
| 440 |
+
" <td>2.0</td>\n",
|
| 441 |
+
" <td>1.0</td>\n",
|
| 442 |
+
" <td>2.0</td>\n",
|
| 443 |
+
" <td>5.0</td>\n",
|
| 444 |
+
" <td>3.0</td>\n",
|
| 445 |
+
" <td>5.0</td>\n",
|
| 446 |
+
" <td>...</td>\n",
|
| 447 |
+
" <td>1366.0</td>\n",
|
| 448 |
+
" <td>768.0</td>\n",
|
| 449 |
+
" <td>11.0</td>\n",
|
| 450 |
+
" <td>203.0</td>\n",
|
| 451 |
+
" <td>12</td>\n",
|
| 452 |
+
" <td>1</td>\n",
|
| 453 |
+
" <td>CA</td>\n",
|
| 454 |
+
" <td>45.9784</td>\n",
|
| 455 |
+
" <td>-66.6905</td>\n",
|
| 456 |
+
" <td>121958</td>\n",
|
| 457 |
+
" </tr>\n",
|
| 458 |
+
" </tbody>\n",
|
| 459 |
+
"</table>\n",
|
| 460 |
+
"<p>609204 rows × 111 columns</p>\n",
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| 461 |
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"</div>"
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],
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| 489 |
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"\n",
|
| 490 |
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" lat_appx_lots_of_err long_appx_lots_of_err __index_level_0__ \n",
|
| 491 |
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"0 38.0 -97.0 422981 \n",
|
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"... ... ... ... \n",
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| 502 |
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"\n",
|
| 503 |
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"[609204 rows x 111 columns]"
|
| 504 |
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]
|
| 505 |
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},
|
| 506 |
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"execution_count": 3,
|
| 507 |
+
"metadata": {},
|
| 508 |
+
"output_type": "execute_result"
|
| 509 |
+
}
|
| 510 |
+
],
|
| 511 |
"source": [
|
|
|
|
| 512 |
"import pandas as pd\n",
|
| 513 |
"from datasets import load_dataset\n",
|
| 514 |
"\n",
|
|
|
|
| 518 |
"# Convert to a pandas DataFrame for easier manipulation\n",
|
| 519 |
"df = pd.DataFrame(dataset[\"train\"])\n",
|
| 520 |
"df"
|
| 521 |
+
]
|
| 522 |
+
}
|
| 523 |
+
],
|
| 524 |
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"metadata": {
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"colab": {
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| 526 |
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