07_feature_engineering.ipynb 6.47 MB
Newer Older
Eva Zangerle's avatar
Eva Zangerle committed
1
2
{
 "cells": [
Eva Zangerle's avatar
Eva Zangerle committed
3
4
5
  {
   "cell_type": "markdown",
   "id": "edd718da-1295-49c4-b556-3cc7b718f93c",
Eva Zangerle's avatar
Eva Zangerle committed
6
7
8
   "metadata": {
    "tags": []
   },
Eva Zangerle's avatar
Eva Zangerle committed
9
10
11
12
13
14
   "source": [
    "# Data Preparation and Quality\n",
    "Lecture Data Engineering and Analytics<br>\n",
    "Eva Zangerle"
   ]
  },
Eva Zangerle's avatar
Eva Zangerle committed
15
16
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
17
   "execution_count": 1,
Eva Zangerle's avatar
Eva Zangerle committed
18
19
20
21
22
23
24
25
26
   "id": "5b126eda-5b79-4531-b8ea-72898d09dc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required packages\n",
    "import json\n",
    "import os\n",
    "from pprint import pprint\n",
    "from sys import getsizeof\n",
27
    "\n",
Eva Zangerle's avatar
Eva Zangerle committed
28
29
30
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
31
    "import plotly.express as px\n",
Eva Zangerle's avatar
Eva Zangerle committed
32
    "import seaborn as sns\n",
33
    "import sklearn.datasets\n",
Eva Zangerle's avatar
Eva Zangerle committed
34
35
36
37
    "import sklearn.preprocessing as preproc\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap\n",
    "from scipy import stats\n",
38
    "from sklearn import linear_model, preprocessing\n",
Eva Zangerle's avatar
Eva Zangerle committed
39
    "from sklearn.cluster import DBSCAN, KMeans\n",
40
    "from sklearn.decomposition import PCA\n",
41
    "from sklearn.feature_extraction import FeatureHasher, text\n",
42
    "from sklearn.impute import SimpleImputer\n",
43
    "from sklearn.metrics import pairwise_distances_argmin"
Eva Zangerle's avatar
Eva Zangerle committed
44
45
   ]
  },
Eva Zangerle's avatar
Eva Zangerle committed
46
47
  {
   "cell_type": "code",
48
49
50
51
52
53
54
55
56
57
58
59
   "execution_count": 2,
   "id": "5406f6f3-1c06-4f3b-aaaf-9ac6f2967729",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"../data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a870c325-706d-4c07-bb57-e3b84322e6e4",
Eva Zangerle's avatar
Eva Zangerle committed
60
61
62
63
64
65
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
66
67
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
Eva Zangerle's avatar
Eva Zangerle committed
68
69
      "Author: Eva Zangerle\n",
      "\n",
70
      "Last updated: 2021-11-29 09:33:54\n",
Eva Zangerle's avatar
Eva Zangerle committed
71
72
73
74
75
76
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
77
    "%watermark -a \"Eva Zangerle\" -u -d -t"
Eva Zangerle's avatar
Eva Zangerle committed
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de6ba96a-a20d-4c9e-bb38-46b80ab6ae1f",
   "metadata": {},
   "source": [
    "## Enhancing Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05832b33-0bb8-4496-a2a1-f23f8c7927b1",
   "metadata": {},
   "source": [
    "### Scaling and Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b0f558-ce94-4349-a525-d65e09db72f8",
   "metadata": {},
   "source": [
    "The following example is based on the online news popularity dataset (taken from the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/online+news+popularity). The dataset provides set of features about articles published by Mashable in a period of two years and was originally used for predicting popularity of articles in social networks. In the following example, we are primarily interested in the word count for each article (`n_tokens_content`) and showcase the results of different scaling methods. This example is adapted from the FeatEng book."
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
106
   "execution_count": 3,
Eva Zangerle's avatar
Eva Zangerle committed
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
   "id": "5b966c0f-b892-408f-935f-d6fcb26db76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "news = pd.read_csv(\n",
    "    os.path.join(data_dir, \"OnlineNewsPopularity.csv\"),\n",
    "    delimiter=\", \",\n",
    "    engine=\"python\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071c9705-c8a4-486b-b124-0a55583d412f",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> We use `, ` as a delimiter here. If we would use only the comma as a delimiter, we would be able to read the dataframe, but for instance, accessing a specific field would fails as the key is not recognized due to the trailing space. Furthermore, we specify the python parsing engine to allow separators of more than one character.</div>"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
129
   "execution_count": 4,
Eva Zangerle's avatar
Eva Zangerle committed
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
   "id": "70e9a7e8-a3da-43be-b561-ac73ed655728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>n_tokens_title</th>\n",
       "      <th>n_tokens_content</th>\n",
       "      <th>n_unique_tokens</th>\n",
       "      <th>n_non_stop_words</th>\n",
       "      <th>n_non_stop_unique_tokens</th>\n",
       "      <th>num_hrefs</th>\n",
       "      <th>num_self_hrefs</th>\n",
       "      <th>num_imgs</th>\n",
       "      <th>num_videos</th>\n",
       "      <th>average_token_length</th>\n",
       "      <th>num_keywords</th>\n",
       "      <th>data_channel_is_lifestyle</th>\n",
       "      <th>data_channel_is_entertainment</th>\n",
       "      <th>...</th>\n",
       "      <th>global_rate_positive_words</th>\n",
       "      <th>global_rate_negative_words</th>\n",
       "      <th>rate_positive_words</th>\n",
       "      <th>rate_negative_words</th>\n",
       "      <th>avg_positive_polarity</th>\n",
       "      <th>min_positive_polarity</th>\n",
       "      <th>max_positive_polarity</th>\n",
       "      <th>avg_negative_polarity</th>\n",
       "      <th>min_negative_polarity</th>\n",
       "      <th>max_negative_polarity</th>\n",
       "      <th>title_subjectivity</th>\n",
       "      <th>title_sentiment_polarity</th>\n",
       "      <th>abs_title_subjectivity</th>\n",
       "      <th>abs_title_sentiment_polarity</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
190
       "      <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
       "      <td>731.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.663594</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815385</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.680365</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045662</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.378636</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
224
       "      <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.913725</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.286915</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.118750</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
258
       "      <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.575130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.393365</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056872</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.466667</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
292
       "      <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>0.503788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.665635</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.404896</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041431</td>\n",
       "      <td>0.020716</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.385965</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.80</td>\n",
       "      <td>-0.369697</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.682836</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074627</td>\n",
       "      <td>0.012127</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>0.139785</td>\n",
       "      <td>0.411127</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.220192</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39639</th>\n",
394
       "      <td>http://mashable.com/2014/12/27/samsung-app-aut...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>0.529052</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684783</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.523121</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037572</td>\n",
       "      <td>0.014451</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.333791</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.125000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39640</th>\n",
428
       "      <td>http://mashable.com/2014/12/27/seth-rogen-jame...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.696296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.885057</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.405488</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.374825</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-0.211111</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39641</th>\n",
462
       "      <td>http://mashable.com/2014/12/27/son-pays-off-mo...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.516355</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.644128</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.076923</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033937</td>\n",
       "      <td>0.024887</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.307273</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.356439</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39642</th>\n",
       "      <td>http://mashable.com/2014/12/27/ukraine-blasts/</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>682.0</td>\n",
       "      <td>0.539493</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.692661</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.975073</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.236851</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.205246</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.012500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39643</th>\n",
530
       "      <td>http://mashable.com/2014/12/27/youtube-channel...</td>\n",
Eva Zangerle's avatar
Eva Zangerle committed
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0.701987</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.471338</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063694</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.247338</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39644 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
       "                                                     url  timedelta  \\\n",
       "0      http://mashable.com/2013/01/07/amazon-instant-...      731.0   \n",
       "1      http://mashable.com/2013/01/07/ap-samsung-spon...      731.0   \n",
       "2      http://mashable.com/2013/01/07/apple-40-billio...      731.0   \n",
       "3      http://mashable.com/2013/01/07/astronaut-notre...      731.0   \n",
       "4       http://mashable.com/2013/01/07/att-u-verse-apps/      731.0   \n",
       "...                                                  ...        ...   \n",
       "39639  http://mashable.com/2014/12/27/samsung-app-aut...        8.0   \n",
       "39640  http://mashable.com/2014/12/27/seth-rogen-jame...        8.0   \n",
       "39641  http://mashable.com/2014/12/27/son-pays-off-mo...        8.0   \n",
       "39642     http://mashable.com/2014/12/27/ukraine-blasts/        8.0   \n",
       "39643  http://mashable.com/2014/12/27/youtube-channel...        8.0   \n",
       "\n",
       "       n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
       "0                12.0             219.0         0.663594               1.0   \n",
       "1                 9.0             255.0         0.604743               1.0   \n",
       "2                 9.0             211.0         0.575130               1.0   \n",
       "3                 9.0             531.0         0.503788               1.0   \n",
       "4                13.0            1072.0         0.415646               1.0   \n",
       "...               ...               ...              ...               ...   \n",
       "39639            11.0             346.0         0.529052               1.0   \n",
       "39640            12.0             328.0         0.696296               1.0   \n",
       "39641            10.0             442.0         0.516355               1.0   \n",
       "39642             6.0             682.0         0.539493               1.0   \n",
       "39643            10.0             157.0         0.701987               1.0   \n",
       "\n",
       "       n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  \\\n",
       "0                      0.815385        4.0             2.0       1.0   \n",
       "1                      0.791946        3.0             1.0       1.0   \n",
       "2                      0.663866        3.0             1.0       1.0   \n",
       "3                      0.665635        9.0             0.0       1.0   \n",
       "4                      0.540890       19.0            19.0      20.0   \n",
       "...                         ...        ...             ...       ...   \n",
       "39639                  0.684783        9.0             7.0       1.0   \n",
       "39640                  0.885057        9.0             7.0       3.0   \n",
       "39641                  0.644128       24.0             1.0      12.0   \n",
       "39642                  0.692661       10.0             1.0       1.0   \n",
       "39643                  0.846154        1.0             1.0       0.0   \n",
       "\n",
       "       num_videos  average_token_length  num_keywords  \\\n",
       "0             0.0              4.680365           5.0   \n",
       "1             0.0              4.913725           4.0   \n",
       "2             0.0              4.393365           6.0   \n",
       "3             0.0              4.404896           7.0   \n",
       "4             0.0              4.682836           7.0   \n",
       "...           ...                   ...           ...   \n",
       "39639         1.0              4.523121           8.0   \n",
       "39640        48.0              4.405488           7.0   \n",
       "39641         1.0              5.076923           8.0   \n",
       "39642         0.0              4.975073           5.0   \n",
       "39643         2.0              4.471338           4.0   \n",
Eva Zangerle's avatar
Eva Zangerle committed
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
       "\n",
       "       data_channel_is_lifestyle  data_channel_is_entertainment  ...  \\\n",
       "0                            0.0                            1.0  ...   \n",
       "1                            0.0                            0.0  ...   \n",
       "2                            0.0                            0.0  ...   \n",
       "3                            0.0                            1.0  ...   \n",
       "4                            0.0                            0.0  ...   \n",
       "...                          ...                            ...  ...   \n",
       "39639                        0.0                            0.0  ...   \n",
       "39640                        0.0                            0.0  ...   \n",
       "39641                        0.0                            0.0  ...   \n",
       "39642                        0.0                            0.0  ...   \n",
       "39643                        0.0                            1.0  ...   \n",
       "\n",
       "       global_rate_positive_words  global_rate_negative_words  \\\n",
       "0                        0.045662                    0.013699   \n",
       "1                        0.043137                    0.015686   \n",
       "2                        0.056872                    0.009479   \n",
       "3                        0.041431                    0.020716   \n",
       "4                        0.074627                    0.012127   \n",
       "...                           ...                         ...   \n",
       "39639                    0.037572                    0.014451   \n",
       "39640                    0.039634                    0.009146   \n",
       "39641                    0.033937                    0.024887   \n",
       "39642                    0.020528                    0.023460   \n",
       "39643                    0.063694                    0.012739   \n",
       "\n",
       "       rate_positive_words  rate_negative_words  avg_positive_polarity  \\\n",
       "0                 0.769231             0.230769               0.378636   \n",
       "1                 0.733333             0.266667               0.286915   \n",
       "2                 0.857143             0.142857               0.495833   \n",
       "3                 0.666667             0.333333               0.385965   \n",
       "4                 0.860215             0.139785               0.411127   \n",
       "...                    ...                  ...                    ...   \n",
       "39639             0.722222             0.277778               0.333791   \n",
       "39640             0.812500             0.187500               0.374825   \n",
       "39641             0.576923             0.423077               0.307273   \n",
       "39642             0.466667             0.533333               0.236851   \n",
       "39643             0.833333             0.166667               0.247338   \n",
       "\n",
       "       min_positive_polarity  max_positive_polarity  avg_negative_polarity  \\\n",
       "0                   0.100000                   0.70              -0.350000   \n",
       "1                   0.033333                   0.70              -0.118750   \n",
       "2                   0.100000                   1.00              -0.466667   \n",
       "3                   0.136364                   0.80              -0.369697   \n",
       "4                   0.033333                   1.00              -0.220192   \n",
       "...                      ...                    ...                    ...   \n",
       "39639               0.100000                   0.75              -0.260000   \n",
       "39640               0.136364                   0.70              -0.211111   \n",
       "39641               0.136364                   0.50              -0.356439   \n",
       "39642               0.062500                   0.50              -0.205246   \n",
       "39643               0.100000                   0.50              -0.200000   \n",
       "\n",
       "       min_negative_polarity  max_negative_polarity  title_subjectivity  \\\n",
       "0                     -0.600              -0.200000            0.500000   \n",
       "1                     -0.125              -0.100000            0.000000   \n",
       "2                     -0.800              -0.133333            0.000000   \n",
       "3                     -0.600              -0.166667            0.000000   \n",
       "4                     -0.500              -0.050000            0.454545   \n",
       "...                      ...                    ...                 ...   \n",
       "39639                 -0.500              -0.125000            0.100000   \n",
       "39640                 -0.400              -0.100000            0.300000   \n",
       "39641                 -0.800              -0.166667            0.454545   \n",
       "39642                 -0.500              -0.012500            0.000000   \n",
       "39643                 -0.200              -0.200000            0.333333   \n",
       "\n",
       "       title_sentiment_polarity  abs_title_subjectivity  \\\n",
       "0                     -0.187500                0.000000   \n",
       "1                      0.000000                0.500000   \n",
       "2                      0.000000                0.500000   \n",
       "3                      0.000000                0.500000   \n",
       "4                      0.136364                0.045455   \n",
       "...                         ...                     ...   \n",
       "39639                  0.000000                0.400000   \n",
       "39640                  1.000000                0.200000   \n",
       "39641                  0.136364                0.045455   \n",
       "39642                  0.000000                0.500000   \n",
       "39643                  0.250000                0.166667   \n",
       "\n",
       "       abs_title_sentiment_polarity  shares  \n",
       "0                          0.187500     593  \n",
       "1                          0.000000     711  \n",
       "2                          0.000000    1500  \n",
       "3                          0.000000    1200  \n",
       "4                          0.136364     505  \n",
       "...                             ...     ...  \n",
       "39639                      0.000000    1800  \n",
       "39640                      1.000000    1900  \n",
       "39641                      0.136364    1900  \n",
       "39642                      0.000000    1100  \n",
       "39643                      0.250000    1300  \n",
       "\n",
       "[39644 rows x 61 columns]"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
714
     "execution_count": 4,
Eva Zangerle's avatar
Eva Zangerle committed
715
716
717
718
719
720
721
722
723
724
725
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at data\n",
    "news"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
726
   "execution_count": 5,
Eva Zangerle's avatar
Eva Zangerle committed
727
728
729
730
731
   "id": "280cf353-a469-4ea1-94f8-d74173600310",
   "metadata": {},
   "outputs": [
    {
     "data": {
732
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
733
734
735
736
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
737
738
739
     "metadata": {
      "needs_background": "light"
     },
Eva Zangerle's avatar
Eva Zangerle committed
740
741
742
743
744
745
746
747
748
749
750
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = news[\"n_tokens_content\"].hist()\n",
    "fig.set_xlabel(\"Word Count\")\n",
    "fig.set_ylabel(\"Articles\");"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
751
   "execution_count": 6,
Eva Zangerle's avatar
Eva Zangerle committed
752
753
754
755
756
757
758
759
760
761
   "id": "0a88dfdf-9fae-44e9-8ba1-857f965b31c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# min max scaling\n",
    "news[\"minmax\"] = preproc.minmax_scale(news[\"n_tokens_content\"])"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
762
   "execution_count": 7,
Eva Zangerle's avatar
Eva Zangerle committed
763
764
765
766
767
768
769
770
771
772
773
774
   "id": "975f64b2-33d6-4a3a-bbcf-b46c6c3c903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardization\n",
    "news[\"standardized\"] = preproc.StandardScaler().fit_transform(\n",
    "    news[[\"n_tokens_content\"]]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
775
   "execution_count": 8,
Eva Zangerle's avatar
Eva Zangerle committed
776
777
778
779
780
781
782
783
784
785
   "id": "d9ce7284-8c2a-4d95-9962-c5eb867c3145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# l2 normalization\n",
    "news[\"normalized\"] = preproc.normalize(news[[\"n_tokens_content\"]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
786
   "execution_count": 9,
Eva Zangerle's avatar
Eva Zangerle committed
787
788
789
790
791
   "id": "b4bab441-0d1d-402c-8141-d81d49c66a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
792
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
793
794
795
796
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
797
798
799
     "metadata": {
      "needs_background": "light"
     },
Eva Zangerle's avatar
Eva Zangerle committed
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare different results\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(10, 15))\n",
    "# fig.tight_layout();\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Article word count\")\n",
    "ax1.set_ylabel(\"Number of articles\")\n",
    "news[\"minmax\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Min-max scaled word count\")\n",
    "ax2.set_ylabel(\"Number of articles\")\n",
    "news[\"standardized\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_xlabel(\"Standardized word count\")\n",
    "ax3.set_ylabel(\"Number of articles\")\n",
    "news[\"normalized\"].hist(ax=ax4, bins=100)\n",
    "ax4.set_xlabel(\"L2-normalized word count\")\n",
    "ax4.set_ylabel(\"Number of articles\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fef108e-a831-4454-a8be-f9bb90bdd0d9",
   "metadata": {},
   "source": [
    "### Power Transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c154449a-6dc6-4707-b3fd-39ea0c2ae3e3",
   "metadata": {},
   "source": [
    "For investigating power transforms, we will look at an example we already discussed during our exploratory data analysis: the count of ratings per movie. "
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
839
   "execution_count": 10,
Eva Zangerle's avatar
Eva Zangerle committed
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
   "id": "f2eba9b9-c768-46f7-9171-f94aa2c12896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 855598 entries, 0 to 855597\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   userID       855598 non-null  int64  \n",
      " 1   movieID      855598 non-null  int64  \n",
      " 2   rating       855598 non-null  float64\n",
      " 3   date_day     855598 non-null  int64  \n",
      " 4   date_month   855598 non-null  int64  \n",
      " 5   date_year    855598 non-null  int64  \n",
      " 6   date_hour    855598 non-null  int64  \n",
      " 7   date_minute  855598 non-null  int64  \n",
      " 8   date_second  855598 non-null  int64  \n",
      "dtypes: float64(1), int64(8)\n",
      "memory usage: 58.7 MB\n"
     ]
    }
   ],
   "source": [
    "# read in rating data\n",
    "ratings = pd.read_csv(\n",
    "    os.path.join(data_dir, \"hetrec/user_ratedmovies.dat\"), delimiter=\"\\t\"\n",
    ")\n",
    "ratings.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
876
   "execution_count": 11,
Eva Zangerle's avatar
Eva Zangerle committed
877
878
879
880
881
882
883
884
885
886
887
   "id": "513fc696-bc60-462c-9166-8a5f53f578b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# asymmetric, skewed distribution\n",
    "# number of ratings per movie\n",
    "rating_counts = ratings.groupby(\"movieID\")[\"rating\"].agg(\"count\")"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
888
   "execution_count": 12,
Eva Zangerle's avatar
Eva Zangerle committed
889
890
891
892
893
   "id": "061330ae-f08f-4b3f-8059-f7a575a2e3f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
894
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
895
896
897
898
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
899
900
901
     "metadata": {
      "needs_background": "light"
     },
Eva Zangerle's avatar
Eva Zangerle committed
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of number of movies with given number of ratings\n",
    "# (binned value counts)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "axes[0].hist(rating_counts, bins=100)\n",
    "axes[0].set_ylabel(\"Number of Movies\")\n",
    "axes[0].set_xlabel(\"Number of Ratings\")\n",
    "axes[1].hist(rating_counts, log=True, bins=100)\n",
    "axes[1].set_ylabel(\"Number of Movies (log)\")\n",
    "axes[1].set_xlabel(\"Number of Ratings\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13a1272-f61c-486d-bd0a-7ad95dcd627e",
   "metadata": {},
   "source": [
    "Another example is the number of words in an article as already shown previously as part of the news popularity dataset."
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
927
   "execution_count": 13,
Eva Zangerle's avatar
Eva Zangerle committed
928
929
930
931
932
   "id": "e89f79a5-ecfd-4d19-8499-9ed7b650c745",
   "metadata": {},
   "outputs": [
    {
     "data": {
933
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
934
935
936
937
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
938
939
940
     "metadata": {
      "needs_background": "light"
     },
Eva Zangerle's avatar
Eva Zangerle committed
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift + 1 due to log(0)\n",
    "news[\"log_n_tokens_content\"] = np.log10(news[\"n_tokens_content\"] + 1)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_xlabel(\"Number of Words in Article\")\n",
    "ax1.set_ylabel(\"Number of Articles\")\n",
    "news[\"log_n_tokens_content\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_xlabel(\"Log of Number of Words\")\n",
    "ax2.set_ylabel(\"Number of Articles\");"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
959
   "execution_count": 14,
Eva Zangerle's avatar
Eva Zangerle committed
960
961
962
963
964
965
966
967
968
   "id": "8dfed8b5-cf59-4186-8f22-39b0013bf844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38045297261832045"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
969
     "execution_count": 14,
Eva Zangerle's avatar
Eva Zangerle committed
970
971
972
973
974
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
975
    "# box-cox transform\n",
Eva Zangerle's avatar
Eva Zangerle committed
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
    "# again, +1 as boxcox expects data to be positive\n",
    "# log transform\n",
    "news[\"n_tokens_content_lmbda0\"] = stats.boxcox(\n",
    "    news[\"n_tokens_content\"] + 1, lmbda=0\n",
    ")\n",
    "\n",
    "# as close to normal distribution as possible (optimal box-cox transform)\n",
    "# If the lmbda parameter is None, the second returned argument\n",
    "# is the lambda that maximizes the log-likelihood function.\n",
    "values, lambda_param = stats.boxcox(news[\"n_tokens_content\"] + 1)\n",
    "news[\"n_tokens_content_opt\"] = values\n",
    "lambda_param"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
992
   "execution_count": 15,
Eva Zangerle's avatar
Eva Zangerle committed
993
994
995
996
997
   "id": "792ae313-f920-4ddd-8f2d-d5fd6921ba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
998
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
999
1000
1001
1002
      "text/plain": [
       "<Figure size 720x1080 with 3 Axes>"
      ]
     },
1003
1004
1005
     "metadata": {
      "needs_background": "light"
     },
Eva Zangerle's avatar
Eva Zangerle committed
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 15))\n",
    "\n",
    "news[\"n_tokens_content\"].hist(ax=ax1, bins=100)\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_title(\"Article Length Histogram\")\n",
    "ax1.set_xlabel(\"\")\n",
    "ax1.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_lmbda0\"].hist(ax=ax2, bins=100)\n",
    "ax2.set_yscale(\"log\")\n",
    "ax2.set_title(\"Log Transformed Length Histogram\")\n",
    "ax2.set_xlabel(\"\")\n",
    "ax2.set_ylabel(\"Occurrence\")\n",
    "\n",
    "news[\"n_tokens_content_opt\"].hist(ax=ax3, bins=100)\n",
    "ax3.set_yscale(\"log\")\n",
    "ax3.set_title(\"Box-Cox Transformed Length Histogram\")\n",
    "ax3.set_xlabel(\"\")\n",
    "ax3.set_ylabel(\"Occurrence\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d978ee33-f2e4-4941-a369-f053466316de",
   "metadata": {},
   "source": [
    "### Discretization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7be77ad3-8608-4577-a57c-6bf757a9e75a",
   "metadata": {},
   "source": [
    "In a first step, we will look at synthetic count data: we create uniformly distributed random counts (once small values, once with a wide range)."
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1049
   "execution_count": 16,
Eva Zangerle's avatar
Eva Zangerle committed
1050
1051
1052
1053
1054
1055
   "id": "a72e595e-dbf2-4247-b9cc-4a258ea6e3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1056
1057
       "array([60, 85, 52, 68, 32, 46, 54, 98, 59, 91, 64, 12, 42, 94, 60, 64, 23,\n",
       "       65, 40, 37])"
Eva Zangerle's avatar
Eva Zangerle committed
1058
1059
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1060
     "execution_count": 16,
Eva Zangerle's avatar
Eva Zangerle committed
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uniformly distributed, small values\n",
    "small_counts = np.random.randint(0, 100, 20)\n",
    "small_counts"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1073
   "execution_count": 17,
Eva Zangerle's avatar
Eva Zangerle committed
1074
1075
1076
1077
1078
1079
   "id": "82b66c99-f869-4330-a93e-522ec78ea7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1080
       "array([6, 8, 5, 6, 3, 4, 5, 9, 5, 9, 6, 1, 4, 9, 6, 6, 2, 6, 4, 3])"
Eva Zangerle's avatar
Eva Zangerle committed
1081
1082
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1083
     "execution_count": 17,
Eva Zangerle's avatar
Eva Zangerle committed
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning by division\n",
    "np.floor_divide(small_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1095
   "execution_count": 18,
Eva Zangerle's avatar
Eva Zangerle committed
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
   "id": "70968305-2047-46fe-a71c-ea4e87d2fe1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counts spanning a wide value range\n",
    "large_counts = [\n",
    "    296,\n",
    "    8286,\n",
    "    64011,\n",
    "    80,\n",
    "    3,\n",
    "    725,\n",
    "    867,\n",
    "    2215,\n",
    "    7689,\n",
    "    11495,\n",
    "    91897,\n",
    "    44,\n",
    "    28,\n",
    "    7971,\n",
    "    926,\n",
    "    122,\n",
    "    22222,\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1124
   "execution_count": 19,
Eva Zangerle's avatar
Eva Zangerle committed
1125
1126
1127
1128
1129
1130
1131
1132
1133
   "id": "03396b9f-c3bb-4f18-8930-30253bf4470b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 3., 4., 1., 0., 2., 2., 3., 3., 4., 4., 1., 1., 3., 2., 2., 4.])"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1134
     "execution_count": 19,
Eva Zangerle's avatar
Eva Zangerle committed
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fixed width binning via powers of 10 (0-9, 10-99, 100-999, 1000-999, etc.)\n",
    "np.floor(np.log10(large_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6e9522-b474-4d1e-8a90-3952acceae56",
   "metadata": {},
   "source": [
    "In the next step, we look at quantile binning to avoid empty bins."
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1154
   "execution_count": 20,
Eva Zangerle's avatar
Eva Zangerle committed
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
   "id": "09f2515a-439a-40f9-a350-dffeda1b27a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1      2.0\n",
       "0.2      5.0\n",
       "0.3      8.0\n",
       "0.4     13.0\n",
       "0.5     21.0\n",
       "0.6     34.0\n",
       "0.7     56.0\n",
       "0.8    104.0\n",
       "0.9    230.0\n",
       "Name: rating, dtype: float64"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1173
     "execution_count": 20,
Eva Zangerle's avatar
Eva Zangerle committed
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute 10 deciles\n",
    "deciles = rating_counts.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])\n",
    "deciles"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1186
   "execution_count": 21,
Eva Zangerle's avatar
Eva Zangerle committed
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
   "id": "3c6a23d2-0fb2-4e87-b158-9833176856cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieID\n",
       "1        (230.0, 1670.0]\n",
       "2        (230.0, 1670.0]\n",
       "3        (230.0, 1670.0]\n",
       "4           (34.0, 56.0]\n",
       "5         (104.0, 230.0]\n",
       "              ...       \n",
       "65088       (0.999, 2.0]\n",
       "65091       (0.999, 2.0]\n",
       "65126       (0.999, 2.0]\n",
       "65130       (0.999, 2.0]\n",
       "65133         (2.0, 5.0]\n",
       "Name: rating, Length: 10109, dtype: category\n",
       "Categories (10, interval[float64, right]): [(0.999, 2.0] < (2.0, 5.0] < (5.0, 8.0] < (8.0, 13.0] ... (34.0, 56.0] < (56.0, 104.0] < (104.0, 230.0] < (230.0, 1670.0]]"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1209
     "execution_count": 21,
Eva Zangerle's avatar
Eva Zangerle committed
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actually use deciles to bin data\n",
    "pd.qcut(rating_counts, 10)"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1221
   "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1222
1223
1224
1225
1226
1227
1228
1229
1230
   "id": "fe53b730-6fe8-41b4-b70d-0eec41627b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1231
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1232
1233
1234
1235
1236
1237
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1238
       "<matplotlib.legend.Legend at 0x7f518eadb580>"
Eva Zangerle's avatar
Eva Zangerle committed
1239
1240
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1241
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1242
1243
1244
1245
1246
1247
1248
1249
1250
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1251
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1252
1253
1254
1255
1256
1257
1258
1259
1260
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1261
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1262
1263
1264
1265
1266
1267
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1268
       "<matplotlib.legend.Legend at 0x7f518e4e7b50>"
Eva Zangerle's avatar
Eva Zangerle committed
1269
1270
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1271
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1272
1273
1274
1275
1276
1277
1278
1279
1280
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1281
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1282
1283
1284
1285
1286
1287
1288
1289
1290
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1291
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1292
1293
1294
1295
1296
1297
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1298
       "<matplotlib.legend.Legend at 0x7f518eadbdf0>"
Eva Zangerle's avatar
Eva Zangerle committed
1299
1300
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1301
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1302
1303
1304
1305
1306
1307
1308
1309
1310
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1311
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1312
1313
1314
1315
1316
1317
1318
1319
1320
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1321
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1322
1323
1324
1325
1326
1327
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1328
       "<matplotlib.legend.Legend at 0x7f518eac9cd0>"
Eva Zangerle's avatar
Eva Zangerle committed
1329
1330
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1331
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1332
1333
1334
1335
1336
1337
1338
1339
1340
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1341
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1342
1343
1344
1345
1346
1347
1348
1349
1350
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1351
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1352
1353
1354
1355
1356
1357
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1358
       "<matplotlib.legend.Legend at 0x7f518eac9490>"
Eva Zangerle's avatar
Eva Zangerle committed
1359
1360
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1361
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1362
1363
1364
1365
1366
1367
1368
1369
1370
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1371
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1372
1373
1374
1375
1376
1377
1378
1379
1380
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1381
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1382
1383
1384
1385
1386
1387
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1388
       "<matplotlib.legend.Legend at 0x7f518eab14c0>"
Eva Zangerle's avatar
Eva Zangerle committed
1389
1390
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1391
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1392
1393
1394
1395
1396
1397
1398
1399
1400
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1401
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1402
1403
1404
1405
1406
1407
1408
1409
1410
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1411
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1412
1413
1414
1415
1416
1417
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1418
       "<matplotlib.legend.Legend at 0x7f518e7bcc70>"
Eva Zangerle's avatar
Eva Zangerle committed
1419
1420
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1421
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1422
1423
1424
1425
1426
1427
1428
1429
1430
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1431
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1432
1433
1434
1435
1436
1437
1438
1439
1440
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1441
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1442
1443
1444
1445
1446
1447
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1448
       "<matplotlib.legend.Legend at 0x7f518eab10a0>"
Eva Zangerle's avatar
Eva Zangerle committed
1449
1450
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1451
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1452
1453
1454
1455
1456
1457
1458
1459
1460
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1461
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1462
1463
1464
1465
1466
1467
1468
1469
1470
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1471
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1472
1473
1474
1475
1476
1477
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1478
       "<matplotlib.legend.Legend at 0x7f518eaa8c70>"
Eva Zangerle's avatar
Eva Zangerle committed
1479
1480
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1481
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1482
1483
1484
1485
1486
1487
1488
1489
1490
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1491
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1492
1493
1494
1495
1496
1497
1498
1499
1500
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1501
     "execution_count": 22,
Eva Zangerle's avatar
Eva Zangerle committed
1502
1503
1504
1505
1506
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
1507
      "image/png": "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\n",
Eva Zangerle's avatar
Eva Zangerle committed
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the deciles on the histogram\n",
    "sns.set_style(\"white\")\n",
    "fig, ax = plt.subplots()\n",
    "rating_counts.hist(ax=ax, bins=100)\n",
    "for pos in deciles:\n",
    "    handle = plt.axvline(pos, color=\"r\")\n",
    "    ax.legend([handle], [\"deciles\"])\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xlabel(\"Review Count (log)\")\n",
    "    ax.set_ylabel(\"Occurrence (log)\");"
   ]
  },
  {
   "cell_type": "code",
Eva Zangerle's avatar
Eva Zangerle committed
1532
   "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1533
1534
1535
1536
1537
1538
1539
1540
1541
   "id": "752c20ea-849a-4c99-886f-4c202214f0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1542
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1543
1544
1545
1546
1547
1548
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1549
       "<matplotlib.legend.Legend at 0x7f518e49ce20>"
Eva Zangerle's avatar
Eva Zangerle committed
1550
1551
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1552
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1553
1554
1555
1556
1557
1558
1559
1560
1561
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1562
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1563
1564
1565
1566
1567
1568
1569
1570
1571
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1572
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1573
1574
1575
1576
1577
1578
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1579
       "<matplotlib.legend.Legend at 0x7f5195343f40>"
Eva Zangerle's avatar
Eva Zangerle committed
1580
1581
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1582
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1583
1584
1585
1586
1587
1588
1589
1590
1591
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1592
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1593
1594
1595
1596
1597
1598
1599
1600
1601
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1602
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1603
1604
1605
1606
1607
1608
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1609
       "<matplotlib.legend.Legend at 0x7f5195343d30>"
Eva Zangerle's avatar
Eva Zangerle committed
1610
1611
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1612
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1613
1614
1615
1616
1617
1618
1619
1620
1621
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1622
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1623
1624
1625
1626
1627
1628
1629
1630
1631
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1632
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1633
1634
1635
1636
1637
1638
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1639
       "<matplotlib.legend.Legend at 0x7f519533ccd0>"
Eva Zangerle's avatar
Eva Zangerle committed
1640
1641
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1642
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1643
1644
1645
1646
1647
1648
1649
1650
1651
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1652
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1653
1654
1655
1656
1657
1658
1659
1660
1661
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1662
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1663
1664
1665
1666
1667
1668
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1669
       "<matplotlib.legend.Legend at 0x7f519535fc40>"
Eva Zangerle's avatar
Eva Zangerle committed
1670
1671
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1672
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1673
1674
1675
1676
1677
1678
1679
1680
1681
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1682
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1683
1684
1685
1686
1687
1688
1689
1690
1691
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1692
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1693
1694
1695
1696
1697
1698
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1699
       "<matplotlib.legend.Legend at 0x7f519533ce20>"
Eva Zangerle's avatar
Eva Zangerle committed
1700
1701
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1702
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1703
1704
1705
1706
1707
1708
1709
1710
1711
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1712
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1713
1714
1715
1716
1717
1718
1719
1720
1721
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1722
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1723
1724
1725
1726
1727
1728
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1729
       "<matplotlib.legend.Legend at 0x7f519533c700>"
Eva Zangerle's avatar
Eva Zangerle committed
1730
1731
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1732
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1733
1734
1735
1736
1737
1738
1739
1740
1741
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1742
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1743
1744
1745
1746
1747
1748
1749
1750
1751
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1752
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1753
1754
1755
1756
1757
1758
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1759
       "<matplotlib.legend.Legend at 0x7f5193f06bb0>"
Eva Zangerle's avatar
Eva Zangerle committed
1760
1761
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1762
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1763
1764
1765
1766
1767
1768
1769
1770
1771
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1772
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1773
1774
1775
1776
1777
1778
1779
1780
1781
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1782
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1783
1784
1785
1786
1787
1788
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
Eva Zangerle's avatar
Eva Zangerle committed
1789
       "<matplotlib.legend.Legend at 0x7f5193f06940>"
Eva Zangerle's avatar
Eva Zangerle committed
1790
1791
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1792
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1793
1794
1795
1796
1797
1798
1799
1800
1801
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Review Count (log)')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1802
     "execution_count": 23,
Eva Zangerle's avatar
Eva Zangerle committed
1803
1804
1805
1806
1807
1808
1809
1810
1811
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Occurrence')"
      ]
     },
Eva Zangerle's avatar
Eva Zangerle committed
1812
     "execution_count": 23,