Increased default dimensionality of latent space

1ebd5f15 · lucas_miranda · 69196db8 · 1ebd5f15 · 1ebd5f15 · 1ebd5f15
Commit 1ebd5f15 authored Jun 04, 2021 by lucas_miranda
--- a/deepof/models.py
+++ b/deepof/models.py
@@ -133,9 +133,9 @@ class GMVAE:
        defaults = {
            "bidirectional_merge": "concat",
-            "clipvalue": 1.0,
+            "clipvalue": 0.75,
            "dense_activation": "relu",
-            "dense_layers_per_branch": 3,
+            "dense_layers_per_branch": 1,
            "dropout_rate": 0.1,
            "learning_rate": 1e-4,
            "units_conv": 64,

--- a/deepof_experiments.smk
+++ b/deepof_experiments.smk
@@ -19,11 +19,11 @@ warmup_epochs = [15]
 warmup_mode = ["sigmoid"]
 losses = ["ELBO"]  # , "MMD", "ELBO+MMD"]
 overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.]
-encodings = [32]  # [2, 4, 6, 8, 10, 12, 14, 16]
+encodings = [16]  # [2, 4, 6, 8, 10, 12, 14, 16]
 cluster_numbers = [15]  # [1, 5, 10, 15, 20, 25]
 latent_reg = ["variance"]  # ["none", "categorical", "variance", "categorical+variance"]
 entropy_knn = [10]
-next_sequence_pred_weights = [0.15]
+next_sequence_pred_weights = [0.0]
 phenotype_pred_weights = [0.0]
 rule_based_pred_weights = [0.0]
 window_lengths = [22]  # range(11,56,11)

--- a/supplementary_notebooks/deepof_model_evaluation.ipynb
+++ b/supplementary_notebooks/deepof_model_evaluation.ipynb
@@ -630,7 +630,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -647,7 +647,7 @@
    "    compile_model=True,\n",
    "    batch_size=batch_size,\n",
    "    encoding=encoding,\n",
-    "    next_sequence_prediction=NextSeqPred,\n",
+    "    next_sequence_prediction=0.1,\n",
    "    phenotype_prediction=PhenoPred,\n",
    "    rule_based_prediction=RuleBasedPred,\n",
    ").build(\n",
@@ -658,11 +658,101 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
   "metadata": {
-    "scrolled": true
+    "scrolled": false
   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"SEQ_2_SEQ_GMVAE\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_15 (InputLayer)           [(None, 22, 26)]     0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1d_24 (Conv1D)              (None, 11, 64)       8384        input_15[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_94 (BatchNo (None, 11, 64)       256         conv1d_24[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_48 (Bidirectional (None, 11, 256)      148992      batch_normalization_94[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_95 (BatchNo (None, 11, 256)      1024        bidirectional_48[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_49 (Bidirectional (None, 128)          123648      batch_normalization_95[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_96 (BatchNo (None, 128)          512         bidirectional_49[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_88 (Dense)                (None, 64)           8256        batch_normalization_96[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_97 (BatchNo (None, 64)           256         dense_88[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_8 (Dropout)             (None, 64)           0           batch_normalization_97[0][0]     \n",
+      "__________________________________________________________________________________________________\n",
+      "sequential_14 (Sequential)      (None, 32)           2208        dropout_8[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "cluster_means (Dense)           (None, 90)           2970        sequential_14[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "cluster_variances (Dense)       (None, 90)           2970        sequential_14[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_14 (Concatenate)    (None, 180)          0           cluster_means[0][0]              \n",
+      "                                                                 cluster_variances[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "cluster_assignment (Dense)      (None, 15)           495         sequential_14[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "reshape_8 (Reshape)             (None, 12, 15)       0           concatenate_14[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "encoding_distribution (Distribu multiple             0           cluster_assignment[0][0]         \n",
+      "                                                                 reshape_8[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "kl_divergence_layer_6 (KLDiverg multiple             181         encoding_distribution[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "latent_distribution (Lambda)    multiple             0           kl_divergence_layer_6[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_97 (Dense)                (None, 32)           224         latent_distribution[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_102 (BatchN (None, 32)           128         dense_97[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_92 (Dense)                (None, 64)           2112        batch_normalization_102[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_103 (BatchN (None, 64)           256         dense_92[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "repeat_vector_9 (RepeatVector)  (None, 22, 64)       0           batch_normalization_103[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_52 (Bidirectional (None, 22, 256)      148992      repeat_vector_9[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_104 (BatchN (None, 22, 256)      1024        bidirectional_52[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_53 (Bidirectional (None, 22, 256)      296448      batch_normalization_104[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_105 (BatchN (None, 22, 256)      1024        bidirectional_53[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1d_26 (Conv1D)              (None, 22, 64)       81984       batch_normalization_105[0][0]    \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_99 (Dense)                (None, 22, 26)       1690        conv1d_26[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "tf.math.softplus_7 (TFOpLambda) (None, 22, 26)       0           dense_99[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_98 (Dense)                (None, 22, 26)       1690        conv1d_26[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_7 (Lambda)               (None, 22, 26)       0           tf.math.softplus_7[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_16 (Concatenate)    (None, 22, 52)       0           dense_98[0][0]                   \n",
+      "                                                                 lambda_7[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "vae_reconstruction (Functional) multiple             337940      latent_distribution[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "vae_prediction (IndependentNorm multiple             0           concatenate_16[0][0]             \n",
+      "==================================================================================================\n",
+      "Total params: 1,173,664\n",
+      "Trainable params: 1,170,271\n",
+      "Non-trainable params: 3,393\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
   "source": [
    "# Uncomment to see model summaries\n",
    "# encoder.summary()\n",
 %% Cell type:code id: tags:
 ``` python
 %load_ext autoreload
 %autoreload 2
 ```
 %% Cell type:code id: tags:
 ``` python
 import warnings
 warnings.filterwarnings("ignore")
 ```
 %% Cell type:markdown id: tags:
 # deepOF model evaluation
 %% Cell type:markdown id: tags:
 Given a dataset and a trained model, this notebook allows the user to
 * Load and inspect the different models (encoder, decoder, grouper, gmvaep)
 * Visualize reconstruction quality for a given model
 * Visualize a static latent space
 * Visualize trajectories on the latent space for a given video
 * sample from the latent space distributions and generate video clips showcasing generated data
 %% Cell type:code id: tags:
 ``` python
 import os
 os.chdir(os.path.dirname("../"))
 ```
 %% Cell type:code id: tags:
 ``` python
 import deepof.data
 import deepof.utils
 import numpy as np
 import pandas as pd
 import re
 import tensorflow as tf
 from collections import Counter
 from sklearn.preprocessing import StandardScaler
 from sklearn.manifold import TSNE
 from sklearn.decomposition import PCA
 from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 import umap
 from ipywidgets import interactive, interact, HBox, Layout, VBox
 from IPython import display
 from matplotlib.animation import FuncAnimation
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 import seaborn as sns
 import tqdm.notebook as tqdm
 from ipywidgets import interact
 ```
 %% Cell type:markdown id: tags:
 ### 1. Define and run project
 %% Cell type:code id: tags:
 ``` python
 path = os.path.join("..", "..", "Desktop", "deepof-data", "deepof_single_topview")
 trained_network = os.path.join("..", "..", "Desktop", "deepof_trained_weights_280521", "var_annealing")
 exclude_bodyparts = tuple([""])
 window_size = 22
 batch_size = 32
 ```
 %% Cell type:code id: tags:
 ``` python
 %%time
 proj = deepof.data.project(
    path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380],
 )
 ```
 %% Output
    CPU times: user 47.3 s, sys: 3.22 s, total: 50.5 s
    Wall time: 42.8 s
 %% Cell type:code id: tags:
 ``` python
 %%time
 proj = proj.run(verbose=True)
 print(proj)
 ```
 %% Output
    Loading trajectories...
    Smoothing trajectories...
    Interpolating outliers...
    Iterative imputation of ocluded bodyparts...
    Computing distances...
    Computing angles...
    Done!
    deepof analysis of 166 videos
    CPU times: user 16min 22s, sys: 26.6 s, total: 16min 49s
    Wall time: 3min 28s
 %% Cell type:markdown id: tags:
 ### 2. Load pretrained deepof model
 %% Cell type:code id: tags:
 ``` python
 coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True)
 data_prep = coords.preprocess(test_videos=0, window_step=1, window_size=window_size, shuffle=False)[
    0
 ]
 ```
 %% Cell type:code id: tags:
 ``` python
 [i for i in os.listdir(trained_network) if i.endswith("h5")]
 ```
 %% Output
    ['GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
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     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=7_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=1_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=3_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=5_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=2_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=8_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=10_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=5_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=20_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=15_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=9_final_weights.h5',
     'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.15_PhenoPred=0.0_RuleBasedPred=0.15_loss=ELBO_loss_warmup=10_warmup_mode=sigmoid_encoding=6_k=15_latreg=variance_entknn=100_run=4_final_weights.h5']
 %% Cell type:code id: tags:
 ``` python
 deepof_weights = [i for i in os.listdir(trained_network) if i.endswith("h5")][1]
 deepof_weights
 ```
 %% Output
    'GMVAE_input_type=coords_window_size=22_NextSeqPred=0.0_PhenoPred=0.0_RuleBasedPred=0.0_loss=ELBO_loss_warmup=25_warmup_mode=linear_encoding=6_k=15_latreg=variance_entknn=100_run=6_final_weights.h5'
 %% Cell type:code id: tags:
 ``` python
 # Set model parameters
 encoding = int(re.findall("encoding=(\d+)_", deepof_weights)[0])
 k = int(re.findall("k=(\d+)_", deepof_weights)[0])
 loss = re.findall("loss=(.+?)_", deepof_weights)[0]
 NextSeqPred = float(re.findall("NextSeqPred=(.+?)_", deepof_weights)[0])
 PhenoPred = float(re.findall("PhenoPred=(.+?)_", deepof_weights)[0])
 RuleBasedPred = float(re.findall("RuleBasedPred=(.+?)_", deepof_weights)[0])
 ```
 %% Cell type:code id: tags:
 ``` python
 (
    encoder,
    decoder,
    grouper,
    gmvaep,
    prior,
    posterior,
 ) = deepof.models.GMVAE(
    loss=loss,
    number_of_components=k,
    compile_model=True,
    batch_size=batch_size,
    encoding=encoding,
-    next_sequence_prediction=NextSeqPred,
+    next_sequence_prediction=0.1,
    phenotype_prediction=PhenoPred,
    rule_based_prediction=RuleBasedPred,
 ).build(
    data_prep.shape
 )
 #gmvaep.load_weights(os.path.join(trained_network, deepof_weights))
 ```
 %% Cell type:code id: tags:
 ``` python
 # Uncomment to see model summaries
 # encoder.summary()
 # decoder.summary()
 # grouper.summary()
 gmvaep.summary()
 ```
+%% Output
+    Model: "SEQ_2_SEQ_GMVAE"
+    __________________________________________________________________________________________________
+    Layer (type)                    Output Shape         Param #     Connected to
+    ==================================================================================================
+    input_15 (InputLayer)           [(None, 22, 26)]     0
+    __________________________________________________________________________________________________
+    conv1d_24 (Conv1D)              (None, 11, 64)       8384        input_15[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_94 (BatchNo (None, 11, 64)       256         conv1d_24[0][0]
+    __________________________________________________________________________________________________
+    bidirectional_48 (Bidirectional (None, 11, 256)      148992      batch_normalization_94[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_95 (BatchNo (None, 11, 256)      1024        bidirectional_48[0][0]
+    __________________________________________________________________________________________________
+    bidirectional_49 (Bidirectional (None, 128)          123648      batch_normalization_95[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_96 (BatchNo (None, 128)          512         bidirectional_49[0][0]
+    __________________________________________________________________________________________________
+    dense_88 (Dense)                (None, 64)           8256        batch_normalization_96[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_97 (BatchNo (None, 64)           256         dense_88[0][0]
+    __________________________________________________________________________________________________
+    dropout_8 (Dropout)             (None, 64)           0           batch_normalization_97[0][0]
+    __________________________________________________________________________________________________
+    sequential_14 (Sequential)      (None, 32)           2208        dropout_8[0][0]
+    __________________________________________________________________________________________________
+    cluster_means (Dense)           (None, 90)           2970        sequential_14[0][0]
+    __________________________________________________________________________________________________
+    cluster_variances (Dense)       (None, 90)           2970        sequential_14[0][0]
+    __________________________________________________________________________________________________
+    concatenate_14 (Concatenate)    (None, 180)          0           cluster_means[0][0]
+                                                                     cluster_variances[0][0]
+    __________________________________________________________________________________________________
+    cluster_assignment (Dense)      (None, 15)           495         sequential_14[0][0]
+    __________________________________________________________________________________________________
+    reshape_8 (Reshape)             (None, 12, 15)       0           concatenate_14[0][0]
+    __________________________________________________________________________________________________
+    encoding_distribution (Distribu multiple             0           cluster_assignment[0][0]
+                                                                     reshape_8[0][0]
+    __________________________________________________________________________________________________
+    kl_divergence_layer_6 (KLDiverg multiple             181         encoding_distribution[0][0]
+    __________________________________________________________________________________________________
+    latent_distribution (Lambda)    multiple             0           kl_divergence_layer_6[0][0]
+    __________________________________________________________________________________________________
+    dense_97 (Dense)                (None, 32)           224         latent_distribution[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_102 (BatchN (None, 32)           128         dense_97[0][0]
+    __________________________________________________________________________________________________
+    dense_92 (Dense)                (None, 64)           2112        batch_normalization_102[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_103 (BatchN (None, 64)           256         dense_92[0][0]
+    __________________________________________________________________________________________________
+    repeat_vector_9 (RepeatVector)  (None, 22, 64)       0           batch_normalization_103[0][0]
+    __________________________________________________________________________________________________
+    bidirectional_52 (Bidirectional (None, 22, 256)      148992      repeat_vector_9[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_104 (BatchN (None, 22, 256)      1024        bidirectional_52[0][0]
+    __________________________________________________________________________________________________
+    bidirectional_53 (Bidirectional (None, 22, 256)      296448      batch_normalization_104[0][0]
+    __________________________________________________________________________________________________
+    batch_normalization_105 (BatchN (None, 22, 256)      1024        bidirectional_53[0][0]
+    __________________________________________________________________________________________________
+    conv1d_26 (Conv1D)              (None, 22, 64)       81984       batch_normalization_105[0][0]
+    __________________________________________________________________________________________________
+    dense_99 (Dense)                (None, 22, 26)       1690        conv1d_26[0][0]
+    __________________________________________________________________________________________________
+    tf.math.softplus_7 (TFOpLambda) (None, 22, 26)       0           dense_99[0][0]
+    __________________________________________________________________________________________________
+    dense_98 (Dense)                (None, 22, 26)       1690        conv1d_26[0][0]
+    __________________________________________________________________________________________________
+    lambda_7 (Lambda)               (None, 22, 26)       0           tf.math.softplus_7[0][0]
+    __________________________________________________________________________________________________
+    concatenate_16 (Concatenate)    (None, 22, 52)       0           dense_98[0][0]
+                                                                     lambda_7[0][0]
+    __________________________________________________________________________________________________
+    vae_reconstruction (Functional) multiple             337940      latent_distribution[0][0]
+    __________________________________________________________________________________________________
+    vae_prediction (IndependentNorm multiple             0           concatenate_16[0][0]
+    ==================================================================================================
+    Total params: 1,173,664
+    Trainable params: 1,170,271
+    Non-trainable params: 3,393
+    __________________________________________________________________________________________________
 %% Cell type:code id: tags:
 ``` python
 # Uncomment to plot model structure
 def plot_model(model, name):
    tf.keras.utils.plot_model(
        model,
        to_file=os.path.join(
            path,
            "deepof_{}_{}.png".format(name, datetime.now().strftime("%Y%m%d-%H%M%S")),
        ),
        show_shapes=True,
        show_dtype=False,
        show_layer_names=True,
        rankdir="TB",
        expand_nested=True,
        dpi=200,
    )
 # plot_model(encoder, "encoder")
 # plot_model(decoder, "decoder")
 # plot_model(grouper, "grouper")
 # plot_model(gmvaep, "gmvaep")
 ```
 %% Cell type:markdown id: tags:
 ### 3. Visualize priors
 %% Cell type:code id: tags:
 ``` python
 import tensorflow_probability as tfp
 tfb = tfp.bijectors
 tfd = tfp.distributions
 tfpl = tfp.layers
 ```
 %% Cell type:code id: tags:
 ``` python
 def get_prior(number_of_components, encoding, init):
    prior = tfd.MixtureSameFamily(
        mixture_distribution=tfd.categorical.Categorical(
            probs=tf.ones(number_of_components) / number_of_components
        ),
        components_distribution=tfd.MultivariateNormalDiag(
            loc=tf.Variable(
                init([number_of_components, encoding],),
                name="prior_means",
            ),
            scale_diag=tfp.util.TransformedVariable(
                tf.ones([number_of_components, encoding]) / number_of_components,
                tfb.Softplus(),
                name="prior_scales",
            ),
        ),
    )
    return prior
 ```
 %% Cell type:code id: tags:
 ``` python
 def sample_and_plot(prior, samples, ax, label):
    """Sample from the prior and plot with colours corresponding to different clusters"""
    samples = prior.sample(samples)
    means = prior.components_distribution.mean()
    samples = tf.concat([samples, means], axis=0)
    pca = PCA(n_components=2)
    prior = pca.fit_transform(samples)
    samples = prior[:-number_of_components, :]
    means = prior[-number_of_components:, :]
    ax.scatter(prior[:,0], prior[:,1])
    ax.scatter(means[:,0], means[:,1], label=label)
    ax.set_xlabel("PC1")
    ax.set_ylabel("PC2")
    ax.legend()
 ```
 %% Cell type:code id: tags:
 ``` python
 number_of_components = 15
 encoding = 6
 initializers = [
    tf.keras.initializers.Constant(),
    tf.keras.initializers.GlorotNormal(),
    tf.keras.initializers.GlorotUniform(),
    tf.keras.initializers.HeNormal(),
    tf.keras.initializers.HeUniform(),
    tf.keras.initializers.LecunNormal(),
    tf.keras.initializers.LecunUniform(),
    tf.keras.initializers.Orthogonal(),
    tf.keras.initializers.RandomNormal(),
    tf.keras.initializers.RandomUniform(),
    tf.keras.initializers.TruncatedNormal(),
    tf.keras.initializers.VarianceScaling(),
 ]
 fig, ax = plt.subplots(4, 3, figsize=(10, 15), sharex=True, sharey=True)
 ax = [item for sublist in ax for item in sublist]
 for i, x in enumerate(ax):
    prior = get_prior(number_of_components, encoding, initializers[i])
    sample_and_plot(
        prior,
        1000,
        x,
        label=re.findall("initializers_v2.(.*?) ", str(initializers[i]))[0],
    )
 fig.tight_layout(rect=[0.0, 0.0, 1.0, 0.97])
 plt.suptitle("effect of initialization on deepOF prior")
 plt.show()
 ```
 %% Output
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-7-8a0b102aafa0> in <module>
          3
          4 initializers = [
    ----> 5     tf.keras.initializers.Constant(),
          6     tf.keras.initializers.GlorotNormal(),
          7     tf.keras.initializers.GlorotUniform(),
    NameError: name 'tf' is not defined
 %% Cell type:code id: tags:
 ``` python
 from scipy.spatial.distance import pdist
 def quantify_separation(init, samples):
    distances = []
    for i in range(samples):
        means = get_prior(
            number_of_components, encoding, init
        ).components_distribution.mean()
        mean_dist = np.mean(pdist(means))
        distances.append(mean_dist)
    return (
        np.mean(distances),
        np.min(distances),
        np.max(distances),
        1.96 * np.std(distances),
    )
 prior_init_eval_dict = {}
 for init in tqdm.tqdm(initializers):
    prior_init_eval_dict[
        re.findall("initializers_v2.(.*?) ", str(init))[0]
    ] = quantify_separation(init, 100)
 ```
 %% Cell type:code id: tags:
 ``` python
 prior_init_eval = pd.DataFrame(prior_init_eval_dict).T
 prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True)
 prior_init_eval.sort_values("mean", ascending=False)
 ```
 %% Output
    ---------------------------------------------------------------------------
    NameError                                 Traceback (most recent call last)
    <ipython-input-8-9346bb65520c> in <module>
    ----> 1 prior_init_eval = pd.DataFrame(prior_init_eval_dict).T
          2 prior_init_eval.rename(columns={0:"mean", 1:"min", 2:"max", 3:"CI95"}, inplace=True)
          3 prior_init_eval.sort_values("mean", ascending=False)
    NameError: name 'pd' is not defined
 %% Cell type:markdown id: tags:
 ### 4. Evaluate reconstruction (to be incorporated into deepof.evaluate)
 %% Cell type:code id: tags:
 ``` python
 # Auxiliary animation functions
 def plot_mouse_graph(instant_x, instant_y, instant_rec_x, instant_rec_y, ax, edges):
    """Generates a graph plot of the mouse"""
    plots = []
    rec_plots = []
    for edge in edges:
        (temp_plot,) = ax.plot(
            [float(instant_x[edge[0]]), float(instant_x[edge[1]])],
            [float(instant_y[edge[0]]), float(instant_y[edge[1]])],
            color="#006699",
            linewidth=2.0,
        )
        (temp_rec_plot,) = ax.plot(
            [float(instant_rec_x[edge[0]]), float(instant_rec_x[edge[1]])],
            [float(instant_rec_y[edge[0]]), float(instant_rec_y[edge[1]])],
            color="red",
            linewidth=2.0,
        )
        plots.append(temp_plot)
        rec_plots.append(temp_rec_plot)
    return plots, rec_plots
 def update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges):
    """Updates the graph plot to enable animation"""
    for plot, edge in zip(plots, edges):
        plot.set_data(
            [float(x[edge[0]]), float(x[edge[1]])],
            [float(y[edge[0]]), float(y[edge[1]])],
        )
    for plot, edge in zip(rec_plots, edges):
        plot.set_data(
            [float(rec_x[edge[0]]), float(rec_x[edge[1]])],
            [float(rec_y[edge[0]]), float(rec_y[edge[1]])],
        )
 ```
 %% Cell type:code id: tags:
 ``` python
 # Display a video with the original data superimposed with the reconstructions
 coords = proj.get_coords(center="Center", align="Spine_1", align_inplace=True)
 random_exp = np.random.choice(list(coords.keys()), 1)[0]
 print(random_exp)
 def animate_mice_across_time(random_exp):
    # Define canvas
    fig, ax = plt.subplots(1, 1, figsize=(10, 10))
    # Retrieve body graph
    edges = deepof.utils.connect_mouse_topview()
    for bpart in exclude_bodyparts:
        if bpart:
            edges.remove_node(bpart)
    for limb in ["Left_fhip", "Right_fhip", "Left_bhip", "Right_bhip"]:
        edges.remove_edge("Center", limb)
        if ("Tail_base", limb) in edges.edges():
            edges.remove_edge("Tail_base", limb)
    edges = edges.edges()
    # Compute observed and predicted data to plot
    data = coords[random_exp]
    coords_rec = coords.filter_videos([random_exp])
    data_prep = coords_rec.preprocess(
        test_videos=0, window_step=1, window_size=window_size, shuffle=False
    )[0][:512]
    data_rec = gmvaep.predict(data_prep)
    try:
        data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :]))
    except TypeError:
        data_rec = data_rec[0]
        data_rec = pd.DataFrame(coords_rec._scaler.inverse_transform(data_rec[:, 6, :]))
    data_rec.columns = data.columns
    data = pd.DataFrame(coords_rec._scaler.inverse_transform(data_prep[:, 6, :]))
    data.columns = data_rec.columns
    # Add Central coordinate, lost during alignment
    data["Center", "x"] = 0
    data["Center", "y"] = 0
    data_rec["Center", "x"] = 0
    data_rec["Center", "y"] = 0
    # Plot!
    init_x = data.xs("x", level=1, axis=1, drop_level=False).iloc[0, :]
    init_y = data.xs("y", level=1, axis=1, drop_level=False).iloc[0, :]
    init_rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[0, :]
    init_rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[0, :]
    plots, rec_plots = plot_mouse_graph(
        init_x, init_y, init_rec_x, init_rec_y, ax, edges
    )
    scatter = ax.scatter(
        x=np.array(init_x), y=np.array(init_y), color="#006699", label="Original"
    )
    rec_scatter = ax.scatter(
        x=np.array(init_rec_x),
        y=np.array(init_rec_y),
        color="red",
        label="Reconstruction",
    )
    # Update data in main plot
    def animation_frame(i):
        # Update scatter plot
        x = data.xs("x", level=1, axis=1, drop_level=False).iloc[i, :]
        y = data.xs("y", level=1, axis=1, drop_level=False).iloc[i, :]
        rec_x = data_rec.xs("x", level=1, axis=1, drop_level=False).iloc[i, :]
        rec_y = data_rec.xs("y", level=1, axis=1, drop_level=False).iloc[i, :]
        scatter.set_offsets(np.c_[np.array(x), np.array(y)])
        rec_scatter.set_offsets(np.c_[np.array(rec_x), np.array(rec_y)])
        update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges)
        return scatter
    animation = FuncAnimation(fig, func=animation_frame, frames=250, interval=50,)
    ax.set_title("Original versus reconstructed data")
    ax.set_ylim(-100, 60)
    ax.set_xlim(-60, 60)
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    plt.legend()
    video = animation.to_html5_video()
    html = display.HTML(video)
    display.display(html)
    plt.close()
 animate_mice_across_time(random_exp)
 ```
 %% Output
    Test 11_s11
 %% Cell type:markdown id: tags:
 ### 5. Evaluate latent space (to be incorporated into deepof.evaluate)
 %% Cell type:code id: tags:
 ``` python
 # Get encodings and groupings for the same random video as above
 data_prep = coords.preprocess(
    test_videos=0, window_step=1, window_size=window_size, shuffle=True
 )[0][:10000]
 ```
 %% Cell type:code id: tags:
 ``` python
 encodings = encoder.predict(data_prep)
 groupings = grouper.predict(data_prep)
 hard_groups = np.argmax(groupings, axis=1)
 ```
 %% Cell type:code id: tags:
 ``` python
 @interact(minimum_confidence=(0.0, 1.0, 0.01))
 def plot_cluster_population(minimum_confidence):
    plt.figure(figsize=(12, 8))
    groups = hard_groups[np.max(groupings, axis=1) > minimum_confidence].flatten()
    groups = np.concatenate([groups, np.arange(groupings.shape[1])])
    sns.countplot(groups)
    plt.xlabel("Cluster")
    plt.title("Training instances per cluster")
    plt.ylim(0, hard_groups.shape[0] * 1.1)
    plt.show()
 ```
 %% Output
 %% Cell type:markdown id: tags:
 The slider in the figure above lets you set the minimum confidence the model may yield when assigning a training instance to a cluster in order to be visualized.
 %% Cell type:code id: tags:
 ``` python
 # Plot real data in the latent space
 samples = np.random.choice(range(encodings.shape[0]), 10000)
 sample_enc = encodings[samples, :]
 sample_grp = groupings[samples, :]
 sample_hgr = hard_groups[samples]
 k = sample_grp.shape[1]
 umap_reducer = umap.UMAP(n_components=2)
 pca_reducer = PCA(n_components=2)
 tsne_reducer = TSNE(n_components=2)
 lda_reducer = LinearDiscriminantAnalysis(n_components=2)
 umap_enc = umap_reducer.fit_transform(sample_enc)
 pca_enc = pca_reducer.fit_transform(sample_enc)
 tsne_enc = tsne_reducer.fit_transform(sample_enc)
 try:
    lda_enc = lda_reducer.fit_transform(sample_enc, sample_hgr)
 except ValueError:
    warnings.warn(
        "Only one class found. Can't use LDA", DeprecationWarning, stacklevel=2