Newer
Older
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><center>Prediction using Topological Data Analysis</center></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Give brief overview of notebook's purpose.\n",
"Also maybe add cool picture."
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"from Topological_ML import TDA_Prediction as tdap\n",
"from sklearn.datasets import fetch_california_housing\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"import kmapper as km\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"from sklearn import ensemble"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"cal_housing = fetch_california_housing()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def numpy_to_pandas(sklearn_data):\n",
" df = pd.DataFrame(data = sklearn_data.data, columns = sklearn_data.feature_names)\n",
" df['response'] = pd.Series(sklearn_data.target)\n",
" return df\n",
"\n",
"df = numpy_to_pandas(cal_housing)"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
65
66
67
68
69
70
71
72
73
74
75
76
77
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
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
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
190
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
"def descriptive_statistic(df, n):\n",
" \"\"\"\n",
" Provides brief descriptive statistics on dataset. \n",
" Takes dataframe as input.\n",
" \"\"\"\n",
" d = dict()\n",
" d['head'] = df.head(n)\n",
" d['shape'] = df.shape\n",
" d['missing values'] = df.isna().sum()\n",
" d['describe'] = df.describe()\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'head': MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude \\\n",
" 0 8.3252 41.0 6.984127 1.023810 322.0 2.555556 37.88 \n",
" 1 8.3014 21.0 6.238137 0.971880 2401.0 2.109842 37.86 \n",
" 2 7.2574 52.0 8.288136 1.073446 496.0 2.802260 37.85 \n",
" 3 5.6431 52.0 5.817352 1.073059 558.0 2.547945 37.85 \n",
" 4 3.8462 52.0 6.281853 1.081081 565.0 2.181467 37.85 \n",
" \n",
" Longitude response \n",
" 0 -122.23 4.526 \n",
" 1 -122.22 3.585 \n",
" 2 -122.24 3.521 \n",
" 3 -122.25 3.413 \n",
" 4 -122.25 3.422 ,\n",
" 'shape': (20640, 9),\n",
" 'missing values': MedInc 0\n",
" HouseAge 0\n",
" AveRooms 0\n",
" AveBedrms 0\n",
" Population 0\n",
" AveOccup 0\n",
" Latitude 0\n",
" Longitude 0\n",
" response 0\n",
" dtype: int64,\n",
" 'describe': MedInc HouseAge AveRooms AveBedrms Population \\\n",
" count 20640.000000 20640.000000 20640.000000 20640.000000 20640.000000 \n",
" mean 3.870671 28.639486 5.429000 1.096675 1425.476744 \n",
" std 1.899822 12.585558 2.474173 0.473911 1132.462122 \n",
" min 0.499900 1.000000 0.846154 0.333333 3.000000 \n",
" 25% 2.563400 18.000000 4.440716 1.006079 787.000000 \n",
" 50% 3.534800 29.000000 5.229129 1.048780 1166.000000 \n",
" 75% 4.743250 37.000000 6.052381 1.099526 1725.000000 \n",
" max 15.000100 52.000000 141.909091 34.066667 35682.000000 \n",
" \n",
" AveOccup Latitude Longitude response \n",
" count 20640.000000 20640.000000 20640.000000 20640.000000 \n",
" mean 3.070655 35.631861 -119.569704 2.068558 \n",
" std 10.386050 2.135952 2.003532 1.153956 \n",
" min 0.692308 32.540000 -124.350000 0.149990 \n",
" 25% 2.429741 33.930000 -121.800000 1.196000 \n",
" 50% 2.818116 34.260000 -118.490000 1.797000 \n",
" 75% 3.282261 37.710000 -118.010000 2.647250 \n",
" max 1243.333333 41.950000 -114.310000 5.000010 }"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"descriptive_statistic(df, 5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((20640,), (20640, 7))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lm = LinearRegression()\n",
"\n",
"ys = df['response']\n",
"xs = np.c_[df['MedInc'],df['HouseAge'], df['AveRooms'], df['Population'], df['AveOccup'], df['Latitude'], df['Longitude']]\n",
"\n",
"lm.fit(xs,ys)\n",
"ys.shape, xs.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(20640,)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred = lm.predict(xs)\n",
"pred.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.5961995839710023"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_sk = lm.score(xs,ys)\n",
"r2_sk"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"train, test = train_test_split(df, test_size = .2, random_state = 42)\n",
"x_train = train.drop('response', axis = 1)\n",
"y_train = train.response\n",
"\n",
"def linear_regression(x, y):\n",
" model = LinearRegression()\n",
" model.fit(x, y)\n",
" return model.score(x ,y)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = pd.DataFrame({\"A\": [1,2,3,4,5,6,7,8,9,10]})\n",
"b = pd.DataFrame({\"B\": [2,4,6,8,10,12,14,16,18,20]})\n",
"test = linear_regression(a, b)\n",
"test"
]
},
{
"cell_type": "code",
"execution_count": 32,
254
255
256
257
258
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
292
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
{
"data": {
"text/plain": [
"0.6125511913966952"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = linear_regression(x_train, y_train)\n",
"test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### MAPPER"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"cal_housing = fetch_california_housing()\n",
"df = numpy_to_pandas(cal_housing)\n",
"\n",
"features = [c for c in df.columns if c not in ['response']]\n",
"\n",
"X = np.array(df[features])\n",
"y = np.array(df.response)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# We create a custom 1-D lens with Isolation Forest\n",
"def lens_1d(X, rs, v):\n",
" model = ensemble.IsolationForest(random_state = rs)\n",
" model.fit(X)\n",
" lens1 = model.decision_function(X).reshape((X.shape[0], 1))\n",
" mapper = km.KeplerMapper(verbose = v)\n",
" lens2 = mapper.fit_transform(X, projection=\"l2norm\")\n",
" lens = np.c_[lens1, lens2]\n",
" return lens"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shawkmasboob/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/iforest.py:237: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to \"auto\". This will change the predict method behavior.\n",
" FutureWarning)\n",
"/Users/shawkmasboob/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
" FutureWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"KeplerMapper()\n",
"..Composing projection pipeline of length 1:\n",
"\tProjections: l2norm\n",
"\tDistance matrices: False\n",
"\tScalers: MinMaxScaler(copy=True, feature_range=(0, 1))\n",
"..Projecting on data shaped (20640, 8)\n",
"\n",
"..Projecting data using: l2norm\n",
"\n",
"..Scaling with: MinMaxScaler(copy=True, feature_range=(0, 1))\n",
"\n"
]
},
{
"cell_type": "code",
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
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KeplerMapper()\n",
"..Composing projection pipeline of length 1:\n",
"\tProjections: l2norm\n",
"\tDistance matrices: False\n",
"\tScalers: MinMaxScaler(copy=True, feature_range=(0, 1))\n",
"..Projecting on data shaped (2, 1)\n",
"\n",
"..Projecting data using: l2norm\n",
"\n",
"..Scaling with: MinMaxScaler(copy=True, feature_range=(0, 1))\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shawkmasboob/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/iforest.py:237: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to \"auto\". This will change the predict method behavior.\n",
" FutureWarning)\n",
"/Users/shawkmasboob/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
" FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"array([[0., 0.],\n",
" [0., 0.]])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = pd.DataFrame({\"A\": [0,0]})\n",
"lens_1d(a,123,1)"
]
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
428
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
462
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
530
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
568
569
570
571
572
573
"execution_count": 16,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KeplerMapper()\n",
"Mapping on data shaped (20640, 8) using lens shaped (20640, 2)\n",
"\n",
"Minimal points in hypercube before clustering: 2\n",
"Creating 225 hypercubes.\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"Cube_9 is empty.\n",
"\n",
"Cube_10 is empty.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"Cube_16 is empty.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"Cube_21 is empty.\n",
"\n",
"Cube_22 is empty.\n",
"\n",
"Cube_23 is empty.\n",
"\n",
"Cube_24 is empty.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"Cube_31 is empty.\n",
"\n",
"Cube_32 is empty.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"Cube_47 is empty.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
" > Found 2 clusters.\n",
"\n",
"\n",
"Created 288 edges and 128 nodes in 0:00:01.880401.\n"
]
}
],
"source": [
"# Create the simplicial complex\n",
"mapper = km.KeplerMapper(verbose=3)\n",
"graph = mapper.map(lens, X, cover=km.Cover(n_cubes=15, perc_overlap=0.4), \n",
" clusterer=sklearn.cluster.KMeans(n_clusters=2, random_state=1618033))"
]
},
{
"cell_type": "code",
"execution_count": 17,
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
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shawkmasboob/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:579: MatplotlibDeprecationWarning: \n",
"The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
" if not cb.iterable(width):\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 1440x1440 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"plt.figure(figsize=(20,20))\n",
"km.draw_matplotlib(graph)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}