File size: 29,280 Bytes
d154fee
 
 
2a0dcbe
 
 
 
 
2f25aea
d154fee
 
 
 
2f25aea
 
 
 
d154fee
 
 
 
 
 
2f25aea
624349c
d154fee
2f25aea
d154fee
 
 
 
2f25aea
6caf480
2f25aea
 
 
 
 
 
 
 
 
 
 
d154fee
2f25aea
6caf480
 
 
 
 
 
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6caf480
2f25aea
 
d154fee
 
2f25aea
 
 
d154fee
 
 
2f25aea
 
 
 
d154fee
 
2f25aea
 
d154fee
6caf480
d154fee
2f25aea
d154fee
2f25aea
6caf480
2f25aea
6caf480
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d154fee
 
 
 
2f25aea
 
 
6caf480
2f25aea
 
 
 
 
 
 
 
 
 
 
 
6caf480
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6caf480
d154fee
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6caf480
 
2f25aea
6caf480
 
 
 
2f25aea
 
624349c
d154fee
 
 
 
 
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d154fee
2f25aea
d154fee
2f25aea
 
 
d154fee
2f25aea
d154fee
2f25aea
d154fee
 
2f25aea
d154fee
2f25aea
d154fee
 
2f25aea
d154fee
 
2f25aea
d154fee
2f25aea
 
 
 
 
 
 
 
 
d154fee
 
 
2f25aea
 
d154fee
2f25aea
 
 
d154fee
2f25aea
d154fee
2f25aea
 
d154fee
2f25aea
d154fee
 
 
 
 
 
2f25aea
 
d154fee
 
 
 
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
d154fee
 
2f25aea
 
 
d154fee
 
 
badcca6
2f25aea
 
 
 
 
 
 
 
 
 
 
d154fee
2f25aea
d154fee
 
 
 
2f25aea
d154fee
2f25aea
d154fee
2f25aea
d154fee
 
 
 
 
 
 
2f25aea
d154fee
2f25aea
d154fee
 
 
 
 
 
2f25aea
d154fee
 
 
 
 
 
 
6caf480
d154fee
 
 
 
 
17f036a
 
d1931b1
2a0dcbe
d154fee
2a0dcbe
 
d1931b1
2a0dcbe
d154fee
2a0dcbe
 
2f25aea
2a0dcbe
 
d154fee
2a0dcbe
 
d154fee
2a0dcbe
 
 
d154fee
2a0dcbe
 
 
 
 
d154fee
2a0dcbe
d154fee
2a0dcbe
 
 
 
d154fee
2a0dcbe
d154fee
2a0dcbe
2f25aea
2a0dcbe
 
 
 
d154fee
2a0dcbe
 
17f036a
2a0dcbe
 
 
 
 
d1931b1
 
 
 
 
 
 
 
2a0dcbe
d154fee
 
 
 
 
 
2f25aea
d154fee
 
 
 
 
 
 
2f25aea
 
 
 
 
 
d154fee
 
 
 
 
 
 
2f25aea
d154fee
2f25aea
d154fee
 
2f25aea
d154fee
2f25aea
d154fee
 
 
 
2f25aea
 
 
d154fee
 
 
 
 
2f25aea
d154fee
 
 
2f25aea
d154fee
2f25aea
 
d154fee
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
d154fee
 
 
17f036a
 
d154fee
2a0dcbe
d154fee
2a0dcbe
d154fee
2a0dcbe
d154fee
2a0dcbe
624349c
2a0dcbe
 
2f25aea
2a0dcbe
 
17f036a
2a0dcbe
 
 
 
d1931b1
 
 
2a0dcbe
d154fee
 
2f25aea
 
 
 
 
 
 
 
9e9cca9
 
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6caf480
d154fee
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
624349c
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17f036a
 
2f25aea
2a0dcbe
 
 
 
 
 
 
 
 
 
 
2f25aea
2a0dcbe
2f25aea
2a0dcbe
2f25aea
2a0dcbe
2f25aea
2a0dcbe
2f25aea
2a0dcbe
 
2f25aea
17f036a
 
2a0dcbe
 
 
 
 
 
 
 
 
 
2f25aea
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d154fee
 
 
17f036a
 
d154fee
2a0dcbe
d154fee
2a0dcbe
d154fee
2a0dcbe
d154fee
2a0dcbe
d154fee
2a0dcbe
 
 
d154fee
2a0dcbe
 
17f036a
2a0dcbe
 
 
 
d1931b1
 
 
2a0dcbe
d154fee
2f25aea
 
d154fee
2f25aea
d154fee
 
2f25aea
d154fee
2f25aea
d154fee
2f25aea
d154fee
 
 
2f25aea
 
 
 
 
d154fee
2f25aea
d154fee
 
 
 
2f25aea
d154fee
2f25aea
d154fee
 
 
 
2f25aea
d154fee
 
 
 
2f25aea
 
 
d154fee
 
2f25aea
 
 
d154fee
2f25aea
d154fee
 
 
 
2f25aea
 
 
d154fee
 
2f25aea
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
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
222
223
224
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
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
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
394
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
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
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
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
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
"""
Geneformer embedding extractor.

**Description:**

| Extracts gene or cell embeddings.
| Plots cell embeddings as heatmaps or UMAPs.
| Generates cell state embedding dictionary for use with InSilicoPerturber.

"""

# imports
import logging
import pickle
from collections import Counter
from pathlib import Path

import anndata
import matplotlib.pyplot as plt
import pandas as pd
import scanpy as sc
import seaborn as sns
import torch
from tdigest import TDigest
from tqdm.auto import trange

from . import perturber_utils as pu
from .tokenizer import TOKEN_DICTIONARY_FILE

logger = logging.getLogger(__name__)


# extract embeddings
def get_embs(
    model,
    filtered_input_data,
    emb_mode,
    layer_to_quant,
    pad_token_id,
    forward_batch_size,
    summary_stat=None,
    silent=False,
):
    model_input_size = pu.get_model_input_size(model)
    total_batch_length = len(filtered_input_data)

    if summary_stat is None:
        embs_list = []
    elif summary_stat is not None:
        # test embedding extraction for example cell and extract # emb dims
        example = filtered_input_data.select([i for i in range(1)])
        example.set_format(type="torch")
        emb_dims = test_emb(model, example["input_ids"], layer_to_quant)
        if emb_mode == "cell":
            # initiate tdigests for # of emb dims
            embs_tdigests = [TDigest() for _ in range(emb_dims)]
        if emb_mode == "gene":
            gene_set = list(
                {
                    element
                    for sublist in filtered_input_data["input_ids"]
                    for element in sublist
                }
            )
            # initiate dict with genes as keys and tdigests for # of emb dims as values
            embs_tdigests_dict = {
                k: [TDigest() for _ in range(emb_dims)] for k in gene_set
            }

    overall_max_len = 0

    for i in trange(0, total_batch_length, forward_batch_size, leave=(not silent)):
        max_range = min(i + forward_batch_size, total_batch_length)

        minibatch = filtered_input_data.select([i for i in range(i, max_range)])

        max_len = int(max(minibatch["length"]))
        original_lens = torch.tensor(minibatch["length"], device="cuda")
        minibatch.set_format(type="torch")

        input_data_minibatch = minibatch["input_ids"]
        input_data_minibatch = pu.pad_tensor_list(
            input_data_minibatch, max_len, pad_token_id, model_input_size
        )

        with torch.no_grad():
            outputs = model(
                input_ids=input_data_minibatch.to("cuda"),
                attention_mask=pu.gen_attention_mask(minibatch),
            )

        embs_i = outputs.hidden_states[layer_to_quant]

        if emb_mode == "cell":
            mean_embs = pu.mean_nonpadding_embs(embs_i, original_lens)
            if summary_stat is None:
                embs_list.append(mean_embs)
            elif summary_stat is not None:
                # update tdigests with current batch for each emb dim
                accumulate_tdigests(embs_tdigests, mean_embs, emb_dims)
            del mean_embs
        elif emb_mode == "gene":
            if summary_stat is None:
                embs_list.append(embs_i)
            elif summary_stat is not None:
                for h in trange(len(minibatch)):
                    length_h = minibatch[h]["length"]
                    input_ids_h = minibatch[h]["input_ids"][0:length_h]

                    # double check dimensions before unsqueezing
                    embs_i_dim = embs_i.dim()
                    if embs_i_dim != 3:
                        logger.error(
                            f"Embedding tensor should have 3 dimensions, not {embs_i_dim}"
                        )
                        raise

                    embs_h = embs_i[h, :, :].unsqueeze(dim=1)
                    dict_h = dict(zip(input_ids_h, embs_h))
                    for k in dict_h.keys():
                        accumulate_tdigests(
                            embs_tdigests_dict[int(k)], dict_h[k], emb_dims
                        )

        overall_max_len = max(overall_max_len, max_len)
        del outputs
        del minibatch
        del input_data_minibatch
        del embs_i

        torch.cuda.empty_cache()

    if summary_stat is None:
        if emb_mode == "cell":
            embs_stack = torch.cat(embs_list, dim=0)
        elif emb_mode == "gene":
            embs_stack = pu.pad_tensor_list(
                embs_list,
                overall_max_len,
                pad_token_id,
                model_input_size,
                1,
                pu.pad_3d_tensor,
            )

    # calculate summary stat embs from approximated tdigests
    elif summary_stat is not None:
        if emb_mode == "cell":
            if summary_stat == "mean":
                summary_emb_list = tdigest_mean(embs_tdigests, emb_dims)
            elif summary_stat == "median":
                summary_emb_list = tdigest_median(embs_tdigests, emb_dims)
            embs_stack = torch.tensor(summary_emb_list)
        elif emb_mode == "gene":
            if summary_stat == "mean":
                [
                    update_tdigest_dict_mean(embs_tdigests_dict, gene, emb_dims)
                    for gene in embs_tdigests_dict.keys()
                ]
            elif summary_stat == "median":
                [
                    update_tdigest_dict_median(embs_tdigests_dict, gene, emb_dims)
                    for gene in embs_tdigests_dict.keys()
                ]
            return embs_tdigests_dict

    return embs_stack


def accumulate_tdigests(embs_tdigests, mean_embs, emb_dims):
    # note: tdigest batch update known to be slow so updating serially
    [
        embs_tdigests[j].update(mean_embs[i, j].item())
        for i in range(mean_embs.size(0))
        for j in range(emb_dims)
    ]


def update_tdigest_dict(embs_tdigests_dict, gene, gene_embs, emb_dims):
    embs_tdigests_dict[gene] = accumulate_tdigests(
        embs_tdigests_dict[gene], gene_embs, emb_dims
    )


def update_tdigest_dict_mean(embs_tdigests_dict, gene, emb_dims):
    embs_tdigests_dict[gene] = tdigest_mean(embs_tdigests_dict[gene], emb_dims)


def update_tdigest_dict_median(embs_tdigests_dict, gene, emb_dims):
    embs_tdigests_dict[gene] = tdigest_median(embs_tdigests_dict[gene], emb_dims)


def summarize_gene_embs(h, minibatch, embs_i, embs_tdigests_dict, emb_dims):
    length_h = minibatch[h]["length"]
    input_ids_h = minibatch[h]["input_ids"][0:length_h]
    embs_h = embs_i[h, :, :].unsqueeze(dim=1)
    dict_h = dict(zip(input_ids_h, embs_h))
    [
        update_tdigest_dict(embs_tdigests_dict, k, dict_h[k], emb_dims)
        for k in dict_h.keys()
    ]


def tdigest_mean(embs_tdigests, emb_dims):
    return [embs_tdigests[i].trimmed_mean(0, 100) for i in range(emb_dims)]


def tdigest_median(embs_tdigests, emb_dims):
    return [embs_tdigests[i].percentile(50) for i in range(emb_dims)]


def test_emb(model, example, layer_to_quant):
    with torch.no_grad():
        outputs = model(input_ids=example.to("cuda"))

    embs_test = outputs.hidden_states[layer_to_quant]
    return embs_test.size()[2]


def label_cell_embs(embs, downsampled_data, emb_labels):
    embs_df = pd.DataFrame(embs.cpu().numpy())
    if emb_labels is not None:
        for label in emb_labels:
            emb_label = downsampled_data[label]
            embs_df[label] = emb_label
    return embs_df


def label_gene_embs(embs, downsampled_data, token_gene_dict):
    gene_set = {
        element for sublist in downsampled_data["input_ids"] for element in sublist
    }
    gene_emb_dict = {k: [] for k in gene_set}
    for i in range(embs.size()[0]):
        length = downsampled_data[i]["length"]
        dict_i = dict(
            zip(
                downsampled_data[i]["input_ids"][0:length],
                embs[i, :, :].unsqueeze(dim=1),
            )
        )
        for k in dict_i.keys():
            gene_emb_dict[k].append(dict_i[k])
    for k in gene_emb_dict.keys():
        gene_emb_dict[k] = (
            torch.squeeze(torch.mean(torch.stack(gene_emb_dict[k]), dim=0), dim=0)
            .cpu()
            .numpy()
        )
    embs_df = pd.DataFrame(gene_emb_dict).T
    embs_df.index = [token_gene_dict[token] for token in embs_df.index]
    return embs_df


def plot_umap(embs_df, emb_dims, label, output_file, kwargs_dict):
    only_embs_df = embs_df.iloc[:, :emb_dims]
    only_embs_df.index = pd.RangeIndex(0, only_embs_df.shape[0], name=None).astype(str)
    only_embs_df.columns = pd.RangeIndex(0, only_embs_df.shape[1], name=None).astype(
        str
    )
    vars_dict = {"embs": only_embs_df.columns}
    obs_dict = {"cell_id": list(only_embs_df.index), f"{label}": list(embs_df[label])}
    adata = anndata.AnnData(X=only_embs_df, obs=obs_dict, var=vars_dict)
    sc.tl.pca(adata, svd_solver="arpack")
    sc.pp.neighbors(adata)
    sc.tl.umap(adata)
    sns.set(rc={"figure.figsize": (10, 10)}, font_scale=2.3)
    sns.set_style("white")
    default_kwargs_dict = {"palette": "Set2", "size": 200}
    if kwargs_dict is not None:
        default_kwargs_dict.update(kwargs_dict)

    sc.pl.umap(adata, color=label, save=output_file, **default_kwargs_dict)


def gen_heatmap_class_colors(labels, df):
    pal = sns.cubehelix_palette(
        len(Counter(labels).keys()),
        light=0.9,
        dark=0.1,
        hue=1,
        reverse=True,
        start=1,
        rot=-2,
    )
    lut = dict(zip(map(str, Counter(labels).keys()), pal))
    colors = pd.Series(labels, index=df.index).map(lut)
    return colors


def gen_heatmap_class_dict(classes, label_colors_series):
    class_color_dict_df = pd.DataFrame(
        {"classes": classes, "color": label_colors_series}
    )
    class_color_dict_df = class_color_dict_df.drop_duplicates(subset=["classes"])
    return dict(zip(class_color_dict_df["classes"], class_color_dict_df["color"]))


def make_colorbar(embs_df, label):
    labels = list(embs_df[label])

    cell_type_colors = gen_heatmap_class_colors(labels, embs_df)
    label_colors = pd.DataFrame(cell_type_colors, columns=[label])

    # create dictionary for colors and classes
    label_color_dict = gen_heatmap_class_dict(labels, label_colors[label])
    return label_colors, label_color_dict


def plot_heatmap(embs_df, emb_dims, label, output_file, kwargs_dict):
    sns.set_style("white")
    sns.set(font_scale=2)
    plt.figure(figsize=(15, 15), dpi=150)
    label_colors, label_color_dict = make_colorbar(embs_df, label)

    default_kwargs_dict = {
        "row_cluster": True,
        "col_cluster": True,
        "row_colors": label_colors,
        "standard_scale": 1,
        "linewidths": 0,
        "xticklabels": False,
        "yticklabels": False,
        "figsize": (15, 15),
        "center": 0,
        "cmap": "magma",
    }

    if kwargs_dict is not None:
        default_kwargs_dict.update(kwargs_dict)
    g = sns.clustermap(
        embs_df.iloc[:, 0:emb_dims].apply(pd.to_numeric), **default_kwargs_dict
    )

    plt.setp(g.ax_row_colors.get_xmajorticklabels(), rotation=45, ha="right")

    for label_color in list(label_color_dict.keys()):
        g.ax_col_dendrogram.bar(
            0, 0, color=label_color_dict[label_color], label=label_color, linewidth=0
        )

        g.ax_col_dendrogram.legend(
            title=f"{label}",
            loc="lower center",
            ncol=4,
            bbox_to_anchor=(0.5, 1),
            facecolor="white",
        )

    plt.savefig(output_file, bbox_inches="tight")


class EmbExtractor:
    valid_option_dict = {
        "model_type": {"Pretrained", "GeneClassifier", "CellClassifier"},
        "num_classes": {int},
        "emb_mode": {"cell", "gene"},
        "cell_emb_style": {"mean_pool"},
        "gene_emb_style": {"mean_pool"},
        "filter_data": {None, dict},
        "max_ncells": {None, int},
        "emb_layer": {-1, 0},
        "emb_label": {None, list},
        "labels_to_plot": {None, list},
        "forward_batch_size": {int},
        "nproc": {int},
        "summary_stat": {None, "mean", "median", "exact_mean", "exact_median"},
    }

    def __init__(
        self,
        model_type="Pretrained",
        num_classes=0,
        emb_mode="cell",
        cell_emb_style="mean_pool",
        gene_emb_style="mean_pool",
        filter_data=None,
        max_ncells=1000,
        emb_layer=-1,
        emb_label=None,
        labels_to_plot=None,
        forward_batch_size=100,
        nproc=4,
        summary_stat=None,
        token_dictionary_file=TOKEN_DICTIONARY_FILE,
    ):
        """
        Initialize embedding extractor.

        **Parameters:**

        model_type : {"Pretrained", "GeneClassifier", "CellClassifier"}
            | Whether model is the pretrained Geneformer or a fine-tuned gene or cell classifier.
        num_classes : int
            | If model is a gene or cell classifier, specify number of classes it was trained to classify.
            | For the pretrained Geneformer model, number of classes is 0 as it is not a classifier.
        emb_mode : {"cell", "gene"}
            | Whether to output cell or gene embeddings.
        cell_emb_style : "mean_pool"
            | Method for summarizing cell embeddings.
            | Currently only option is mean pooling of gene embeddings for given cell.
        gene_emb_style : "mean_pool"
            | Method for summarizing gene embeddings.
            | Currently only option is mean pooling of contextual gene embeddings for given gene.
        filter_data : None, dict
            | Default is to extract embeddings from all input data.
            | Otherwise, dictionary specifying .dataset column name and list of values to filter by.
        max_ncells : None, int
            | Maximum number of cells to extract embeddings from.
            | Default is 1000 cells randomly sampled from input data.
            | If None, will extract embeddings from all cells.
        emb_layer : {-1, 0}
            | Embedding layer to extract.
            | The last layer is most specifically weighted to optimize the given learning objective.
            | Generally, it is best to extract the 2nd to last layer to get a more general representation.
            | -1: 2nd to last layer
            | 0: last layer
        emb_label : None, list
            | List of column name(s) in .dataset to add as labels to embedding output.
        labels_to_plot : None, list
            | Cell labels to plot.
            | Shown as color bar in heatmap.
            | Shown as cell color in umap.
            | Plotting umap requires labels to plot.
        forward_batch_size : int
            | Batch size for forward pass.
        nproc : int
            | Number of CPU processes to use.
        summary_stat : {None, "mean", "median", "exact_mean", "exact_median"}
            | If exact_mean or exact_median, outputs only exact mean or median embedding of input data.
            | If mean or median, outputs only approximated mean or median embedding of input data.
            | Non-exact recommended if encountering memory constraints while generating goal embedding positions.
            | Non-exact is slower but more memory-efficient.
        token_dictionary_file : Path
            | Path to pickle file containing token dictionary (Ensembl ID:token).

        **Examples:**

        .. code-block :: python

            >>> from geneformer import EmbExtractor
            >>> embex = EmbExtractor(model_type="CellClassifier",
            ...         num_classes=3,
            ...         emb_mode="cell",
            ...         filter_data={"cell_type":["cardiomyocyte"]},
            ...         max_ncells=1000,
            ...         max_ncells_to_plot=1000,
            ...         emb_layer=-1,
            ...         emb_label=["disease", "cell_type"],
            ...         labels_to_plot=["disease", "cell_type"])

        """

        self.model_type = model_type
        self.num_classes = num_classes
        self.emb_mode = emb_mode
        self.cell_emb_style = cell_emb_style
        self.gene_emb_style = gene_emb_style
        self.filter_data = filter_data
        self.max_ncells = max_ncells
        self.emb_layer = emb_layer
        self.emb_label = emb_label
        self.labels_to_plot = labels_to_plot
        self.forward_batch_size = forward_batch_size
        self.nproc = nproc
        if (summary_stat is not None) and ("exact" in summary_stat):
            self.summary_stat = None
            self.exact_summary_stat = summary_stat
        else:
            self.summary_stat = summary_stat
            self.exact_summary_stat = None

        self.validate_options()

        # load token dictionary (Ensembl IDs:token)
        with open(token_dictionary_file, "rb") as f:
            self.gene_token_dict = pickle.load(f)

        self.token_gene_dict = {v: k for k, v in self.gene_token_dict.items()}
        self.pad_token_id = self.gene_token_dict.get("<pad>")

    def validate_options(self):
        # confirm arguments are within valid options and compatible with each other
        for attr_name, valid_options in self.valid_option_dict.items():
            attr_value = self.__dict__[attr_name]
            if not isinstance(attr_value, (list, dict)):
                if attr_value in valid_options:
                    continue
            valid_type = False
            for option in valid_options:
                if (option in [int, list, dict, bool]) and isinstance(
                    attr_value, option
                ):
                    valid_type = True
                    break
            if valid_type:
                continue
            logger.error(
                f"Invalid option for {attr_name}. "
                f"Valid options for {attr_name}: {valid_options}"
            )
            raise

        if self.filter_data is not None:
            for key, value in self.filter_data.items():
                if not isinstance(value, list):
                    self.filter_data[key] = [value]
                    logger.warning(
                        "Values in filter_data dict must be lists. "
                        f"Changing {key} value to list ([{value}])."
                    )

    def extract_embs(
        self,
        model_directory,
        input_data_file,
        output_directory,
        output_prefix,
        output_torch_embs=False,
        cell_state=None,
    ):
        """
        Extract embeddings from input data and save as results in output_directory.

        **Parameters:**

        model_directory : Path
            | Path to directory containing model
        input_data_file : Path
            | Path to directory containing .dataset inputs
        output_directory : Path
            | Path to directory where embedding data will be saved as csv
        output_prefix : str
            | Prefix for output file
        output_torch_embs : bool
            | Whether or not to also output the embeddings as a tensor.
            | Note, if true, will output embeddings as both dataframe and tensor.
        cell_state : dict
            | Cell state key and value for state embedding extraction.

        **Examples:**

        .. code-block :: python

            >>> embs = embex.extract_embs("path/to/model",
            ...                           "path/to/input_data",
            ...                           "path/to/output_directory",
            ...                           "output_prefix")

        """

        filtered_input_data = pu.load_and_filter(
            self.filter_data, self.nproc, input_data_file
        )
        if cell_state is not None:
            filtered_input_data = pu.filter_by_dict(
                filtered_input_data, cell_state, self.nproc
            )
        downsampled_data = pu.downsample_and_sort(filtered_input_data, self.max_ncells)
        model = pu.load_model(
            self.model_type, self.num_classes, model_directory, mode="eval"
        )
        layer_to_quant = pu.quant_layers(model) + self.emb_layer
        embs = get_embs(
            model,
            downsampled_data,
            self.emb_mode,
            layer_to_quant,
            self.pad_token_id,
            self.forward_batch_size,
            self.summary_stat,
        )

        if self.emb_mode == "cell":
            if self.summary_stat is None:
                embs_df = label_cell_embs(embs, downsampled_data, self.emb_label)
            elif self.summary_stat is not None:
                embs_df = pd.DataFrame(embs.cpu().numpy()).T
        elif self.emb_mode == "gene":
            if self.summary_stat is None:
                embs_df = label_gene_embs(embs, downsampled_data, self.token_gene_dict)
            elif self.summary_stat is not None:
                embs_df = pd.DataFrame(embs).T
                embs_df.index = [self.token_gene_dict[token] for token in embs_df.index]

        # save embeddings to output_path
        if cell_state is None:
            output_path = (Path(output_directory) / output_prefix).with_suffix(".csv")
            embs_df.to_csv(output_path)

        if self.exact_summary_stat == "exact_mean":
            embs = embs.mean(dim=0)
            embs_df = pd.DataFrame(
                embs_df[0:255].mean(axis="rows"), columns=[self.exact_summary_stat]
            ).T
        elif self.exact_summary_stat == "exact_median":
            embs = torch.median(embs, dim=0)[0]
            embs_df = pd.DataFrame(
                embs_df[0:255].median(axis="rows"), columns=[self.exact_summary_stat]
            ).T

        if cell_state is not None:
            return embs
        else:
            if output_torch_embs:
                return embs_df, embs
            else:
                return embs_df

    def get_state_embs(
        self,
        cell_states_to_model,
        model_directory,
        input_data_file,
        output_directory,
        output_prefix,
        output_torch_embs=True,
    ):
        """
        Extract exact mean or exact median cell state embedding positions from input data and save as results in output_directory.

        **Parameters:**

        cell_states_to_model : None, dict
            | Cell states to model if testing perturbations that achieve goal state change.
            | Four-item dictionary with keys: state_key, start_state, goal_state, and alt_states
            | state_key: key specifying name of column in .dataset that defines the start/goal states
            | start_state: value in the state_key column that specifies the start state
            | goal_state: value in the state_key column taht specifies the goal end state
            | alt_states: list of values in the state_key column that specify the alternate end states
            | For example:
            |      {"state_key": "disease",
            |      "start_state": "dcm",
            |      "goal_state": "nf",
            |      "alt_states": ["hcm", "other1", "other2"]}
        model_directory : Path
            | Path to directory containing model
        input_data_file : Path
            | Path to directory containing .dataset inputs
        output_directory : Path
            | Path to directory where embedding data will be saved as csv
        output_prefix : str
            | Prefix for output file
        output_torch_embs : bool
            | Whether or not to also output the embeddings as a tensor.
            | Note, if true, will output embeddings as both dataframe and tensor.

        **Outputs**

        | Outputs state_embs_dict for use with in silico perturber.
        | Format is dictionary of embedding positions of each cell state to model shifts from/towards.
        | Keys specify each possible cell state to model.
        | Values are target embedding positions as torch.tensor.
        | For example:
        |      {"nf": emb_nf,
        |      "hcm": emb_hcm,
        |      "dcm": emb_dcm,
        |      "other1": emb_other1,
        |      "other2": emb_other2}
        """

        pu.validate_cell_states_to_model(cell_states_to_model)
        valid_summary_stats = ["exact_mean", "exact_median"]
        if self.exact_summary_stat not in valid_summary_stats:
            logger.error(
                "For extracting state embs, summary_stat in EmbExtractor "
                f"must be set to option in {valid_summary_stats}"
            )
            raise

        state_embs_dict = dict()
        state_key = cell_states_to_model["state_key"]
        for k, v in cell_states_to_model.items():
            if k == "state_key":
                continue
            elif (k == "start_state") or (k == "goal_state"):
                state_embs_dict[v] = self.extract_embs(
                    model_directory,
                    input_data_file,
                    output_directory,
                    output_prefix,
                    output_torch_embs,
                    cell_state={state_key: v},
                )
            else:  # k == "alt_states"
                for alt_state in v:
                    state_embs_dict[alt_state] = self.extract_embs(
                        model_directory,
                        input_data_file,
                        output_directory,
                        output_prefix,
                        output_torch_embs,
                        cell_state={state_key: alt_state},
                    )

        output_path = (Path(output_directory) / output_prefix).with_suffix(".pkl")
        with open(output_path, "wb") as fp:
            pickle.dump(state_embs_dict, fp)

        return state_embs_dict

    def plot_embs(
        self,
        embs,
        plot_style,
        output_directory,
        output_prefix,
        max_ncells_to_plot=1000,
        kwargs_dict=None,
    ):
        """
        Plot embeddings, coloring by provided labels.

        **Parameters:**

        embs : pandas.core.frame.DataFrame
            | Pandas dataframe containing embeddings output from extract_embs
        plot_style : str
            | Style of plot: "heatmap" or "umap"
        output_directory : Path
            | Path to directory where plots will be saved as pdf
        output_prefix : str
            | Prefix for output file
        max_ncells_to_plot : None, int
            | Maximum number of cells to plot.
            | Default is 1000 cells randomly sampled from embeddings.
            | If None, will plot embeddings from all cells.
        kwargs_dict : dict
            | Dictionary of kwargs to pass to plotting function.

        **Examples:**

        .. code-block :: python

            >>> embex.plot_embs(embs=embs,
            ...                 plot_style="heatmap",
            ...                 output_directory="path/to/output_directory",
            ...                 output_prefix="output_prefix")

        """

        if plot_style not in ["heatmap", "umap"]:
            logger.error(
                "Invalid option for 'plot_style'. " "Valid options: {'heatmap','umap'}"
            )
            raise

        if (plot_style == "umap") and (self.labels_to_plot is None):
            logger.error("Plotting UMAP requires 'labels_to_plot'. ")
            raise

        if max_ncells_to_plot > self.max_ncells:
            max_ncells_to_plot = self.max_ncells
            logger.warning(
                "max_ncells_to_plot must be <= max_ncells. "
                f"Changing max_ncells_to_plot to {self.max_ncells}."
            )

        if (max_ncells_to_plot is not None) and (max_ncells_to_plot < self.max_ncells):
            embs = embs.sample(max_ncells_to_plot, axis=0)

        if self.emb_label is None:
            label_len = 0
        else:
            label_len = len(self.emb_label)

        emb_dims = embs.shape[1] - label_len

        if self.emb_label is None:
            emb_labels = None
        else:
            emb_labels = embs.columns[emb_dims:]

        if plot_style == "umap":
            for label in self.labels_to_plot:
                if label not in emb_labels:
                    logger.warning(
                        f"Label {label} from labels_to_plot "
                        f"not present in provided embeddings dataframe."
                    )
                    continue
                output_prefix_label = "_" + output_prefix + f"_umap_{label}"
                output_file = (
                    Path(output_directory) / output_prefix_label
                ).with_suffix(".pdf")
                plot_umap(embs, emb_dims, label, output_prefix_label, kwargs_dict)

        if plot_style == "heatmap":
            for label in self.labels_to_plot:
                if label not in emb_labels:
                    logger.warning(
                        f"Label {label} from labels_to_plot "
                        f"not present in provided embeddings dataframe."
                    )
                    continue
                output_prefix_label = output_prefix + f"_heatmap_{label}"
                output_file = (
                    Path(output_directory) / output_prefix_label
                ).with_suffix(".pdf")
                plot_heatmap(embs, emb_dims, label, output_file, kwargs_dict)