{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# deepOF model evaluation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a dataset and a trained model, this notebook allows the user to \n", "\n", "* Load and inspect the different models (encoder, decoder, grouper, gmvaep)\n", "* Visualize reconstruction quality for a given model\n", "* Visualize a static latent space\n", "* Visualize trajectories on the latent space for a given video\n", "* sample from the latent space distributions and generate video clips showcasing generated data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "os.chdir(os.path.dirname(\"../\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import deepof.data\n", "import deepof.utils\n", "import numpy as np\n", "import pandas as pd\n", "import re\n", "import tensorflow as tf\n", "from collections import Counter\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "from sklearn.manifold import TSNE\n", "from sklearn.decomposition import PCA\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "import umap\n", "\n", "from ipywidgets import interactive, interact, HBox, Layout, VBox\n", "from IPython import display\n", "from matplotlib.animation import FuncAnimation\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import tqdm.notebook as tqdm\n", "\n", "from ipywidgets import interact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Define and run project" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "path = os.path.join(\"..\", \"..\", \"Desktop\", \"deepof-data\", \"deepof_single_topview\")\n", "trained_network = os.path.join(\"..\", \"..\", \"Desktop\", \"deepof_trained_weights_280521\", \"var_annealing\")\n", "exclude_bodyparts = tuple([\"\"])\n", "window_size = 22\n", "batch_size = 32" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 47.3 s, sys: 3.22 s, total: 50.5 s\n", "Wall time: 42.8 s\n" ] } ], "source": [ "%%time\n", "proj = deepof.data.project(\n", " path=path, smooth_alpha=0.999, exclude_bodyparts=exclude_bodyparts, arena_dims=[380],\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading trajectories...\n", "Smoothing trajectories...\n", "Interpolating outliers...\n", "Iterative imputation of ocluded bodyparts...\n", "Computing distances...\n", "Computing angles...\n", "Done!\n", "deepof analysis of 166 videos\n", "CPU times: user 16min 22s, sys: 26.6 s, total: 16min 49s\n", "Wall time: 3min 28s\n" ] } ], "source": [ "%%time\n", "proj = proj.run(verbose=True)\n", "print(proj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Load pretrained deepof model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "coords = proj.get_coords(center=\"Center\", align=\"Spine_1\", align_inplace=True)\n", "data_prep = coords.preprocess(test_videos=0, window_step=1, window_size=window_size, shuffle=False)[\n", " 0\n", "]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['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',\n", " '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',\n", " 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'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=2_final_weights.h5',\n", " '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=3_final_weights.h5',\n", " '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=1_final_weights.h5',\n", " '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=6_final_weights.h5',\n", " '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=2_final_weights.h5',\n", " '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=2_final_weights.h5',\n", " '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=10_final_weights.h5',\n", " '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=1_final_weights.h5',\n", " '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=3_final_weights.h5',\n", " '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=1_final_weights.h5',\n", " '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=3_final_weights.h5',\n", " '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=9_final_weights.h5',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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',\n", " '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']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i for i in os.listdir(trained_network) if i.endswith(\"h5\")]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'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'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "deepof_weights = [i for i in os.listdir(trained_network) if i.endswith(\"h5\")][1]\n", "deepof_weights" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Set model parameters\n", "encoding = int(re.findall(\"encoding=(\\d+)_\", deepof_weights)[0])\n", "k = int(re.findall(\"k=(\\d+)_\", deepof_weights)[0])\n", "loss = re.findall(\"loss=(.+?)_\", deepof_weights)[0]\n", "NextSeqPred = float(re.findall(\"NextSeqPred=(.+?)_\", deepof_weights)[0])\n", "PhenoPred = float(re.findall(\"PhenoPred=(.+?)_\", deepof_weights)[0])\n", "RuleBasedPred = float(re.findall(\"RuleBasedPred=(.+?)_\", deepof_weights)[0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "(\n", " encoder,\n", " decoder,\n", " grouper,\n", " gmvaep,\n", " prior,\n", " posterior,\n", ") = deepof.models.GMVAE(\n", " loss=loss,\n", " number_of_components=k,\n", " compile_model=True,\n", " batch_size=batch_size,\n", " encoding=encoding,\n", " next_sequence_prediction=NextSeqPred,\n", " phenotype_prediction=PhenoPred,\n", " rule_based_prediction=RuleBasedPred,\n", ").build(\n", " data_prep.shape\n", ")\n", "#gmvaep.load_weights(os.path.join(trained_network, deepof_weights))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Uncomment to see model summaries\n", "# encoder.summary()\n", "# decoder.summary()\n", "# grouper.summary()\n", "gmvaep.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Uncomment to plot model structure\n", "def plot_model(model, name):\n", " tf.keras.utils.plot_model(\n", " model,\n", " to_file=os.path.join(\n", " path,\n", " \"deepof_{}_{}.png\".format(name, datetime.now().strftime(\"%Y%m%d-%H%M%S\")),\n", " ),\n", " show_shapes=True,\n", " show_dtype=False,\n", " show_layer_names=True,\n", " rankdir=\"TB\",\n", " expand_nested=True,\n", " dpi=200,\n", " )\n", "\n", "\n", "# plot_model(encoder, \"encoder\")\n", "# plot_model(decoder, \"decoder\")\n", "# plot_model(grouper, \"grouper\")\n", "# plot_model(gmvaep, \"gmvaep\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Visualize priors" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow_probability as tfp\n", "\n", "tfb = tfp.bijectors\n", "tfd = tfp.distributions\n", "tfpl = tfp.layers" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def get_prior(number_of_components, encoding, init):\n", "\n", " prior = tfd.MixtureSameFamily(\n", " mixture_distribution=tfd.categorical.Categorical(\n", " probs=tf.ones(number_of_components) / number_of_components\n", " ),\n", " components_distribution=tfd.MultivariateNormalDiag(\n", " loc=tf.Variable(\n", " init([number_of_components, encoding],),\n", " name=\"prior_means\",\n", " ),\n", " scale_diag=tfp.util.TransformedVariable(\n", " tf.ones([number_of_components, encoding]) / number_of_components,\n", " tfb.Softplus(),\n", " name=\"prior_scales\",\n", " ),\n", " ),\n", " )\n", " \n", " return prior" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def sample_and_plot(prior, samples, ax, label):\n", " \"\"\"Sample from the prior and plot with colours corresponding to different clusters\"\"\"\n", " \n", " samples = prior.sample(samples)\n", " means = prior.components_distribution.mean()\n", " samples = tf.concat([samples, means], axis=0)\n", " pca = PCA(n_components=2)\n", " prior = pca.fit_transform(samples)\n", " \n", " samples = prior[:-number_of_components, :]\n", " means = prior[-number_of_components:, :]\n", " \n", " ax.scatter(prior[:,0], prior[:,1])\n", " ax.scatter(means[:,0], means[:,1], label=label)\n", " ax.set_xlabel(\"PC1\")\n", " ax.set_ylabel(\"PC2\")\n", " ax.legend()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'tf' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m initializers = [\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlorotNormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlorotUniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined" ] } ], "source": [ "number_of_components = 15\n", "encoding = 6\n", "\n", "initializers = [\n", " tf.keras.initializers.Constant(),\n", " tf.keras.initializers.GlorotNormal(),\n", " tf.keras.initializers.GlorotUniform(),\n", " tf.keras.initializers.HeNormal(),\n", " tf.keras.initializers.HeUniform(),\n", " tf.keras.initializers.LecunNormal(),\n", " tf.keras.initializers.LecunUniform(),\n", " tf.keras.initializers.Orthogonal(),\n", " tf.keras.initializers.RandomNormal(),\n", " tf.keras.initializers.RandomUniform(),\n", " tf.keras.initializers.TruncatedNormal(),\n", " tf.keras.initializers.VarianceScaling(),\n", "]\n", "\n", "fig, ax = plt.subplots(4, 3, figsize=(10, 15), sharex=True, sharey=True)\n", "ax = [item for sublist in ax for item in sublist]\n", "\n", "for i, x in enumerate(ax):\n", " prior = get_prior(number_of_components, encoding, initializers[i])\n", " sample_and_plot(\n", " prior,\n", " 1000,\n", " x,\n", " label=re.findall(\"initializers_v2.(.*?) \", str(initializers[i]))[0],\n", " )\n", "\n", "fig.tight_layout(rect=[0.0, 0.0, 1.0, 0.97])\n", "plt.suptitle(\"effect of initialization on deepOF prior\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.spatial.distance import pdist\n", "\n", "\n", "def quantify_separation(init, samples):\n", "\n", " distances = []\n", "\n", " for i in range(samples):\n", " means = get_prior(\n", " number_of_components, encoding, init\n", " ).components_distribution.mean()\n", " mean_dist = np.mean(pdist(means))\n", " distances.append(mean_dist)\n", "\n", " return (\n", " np.mean(distances),\n", " np.min(distances),\n", " np.max(distances),\n", " 1.96 * np.std(distances),\n", " )\n", "\n", "\n", "prior_init_eval_dict = {}\n", "for init in tqdm.tqdm(initializers):\n", " prior_init_eval_dict[\n", " re.findall(\"initializers_v2.(.*?) \", str(init))[0]\n", " ] = quantify_separation(init, 100)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprior_init_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprior_init_eval_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprior_init_eval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"min\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"max\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"CI95\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprior_init_eval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" ] } ], "source": [ "prior_init_eval = pd.DataFrame(prior_init_eval_dict).T\n", "prior_init_eval.rename(columns={0:\"mean\", 1:\"min\", 2:\"max\", 3:\"CI95\"}, inplace=True)\n", "prior_init_eval.sort_values(\"mean\", ascending=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Evaluate reconstruction (to be incorporated into deepof.evaluate)" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [], "source": [ "# Auxiliary animation functions\n", "\n", "\n", "def plot_mouse_graph(instant_x, instant_y, instant_rec_x, instant_rec_y, ax, edges):\n", " \"\"\"Generates a graph plot of the mouse\"\"\"\n", " plots = []\n", " rec_plots = []\n", " for edge in edges:\n", " (temp_plot,) = ax.plot(\n", " [float(instant_x[edge[0]]), float(instant_x[edge[1]])],\n", " [float(instant_y[edge[0]]), float(instant_y[edge[1]])],\n", " color=\"#006699\",\n", " linewidth=2.0,\n", " )\n", " (temp_rec_plot,) = ax.plot(\n", " [float(instant_rec_x[edge[0]]), float(instant_rec_x[edge[1]])],\n", " [float(instant_rec_y[edge[0]]), float(instant_rec_y[edge[1]])],\n", " color=\"red\",\n", " linewidth=2.0,\n", " )\n", " plots.append(temp_plot)\n", " rec_plots.append(temp_rec_plot)\n", " return plots, rec_plots\n", "\n", "\n", "def update_mouse_graph(x, y, rec_x, rec_y, plots, rec_plots, edges):\n", " \"\"\"Updates the graph plot to enable animation\"\"\"\n", "\n", " for plot, edge in zip(plots, edges):\n", " plot.set_data(\n", " [float(x[edge[0]]), float(x[edge[1]])],\n", " [float(y[edge[0]]), float(y[edge[1]])],\n", " )\n", " for plot, edge in zip(rec_plots, edges):\n", " plot.set_data(\n", " [float(rec_x[edge[0]]), float(rec_x[edge[1]])],\n", " [float(rec_y[edge[0]]), float(rec_y[edge[1]])],\n", " )" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test 11_s11\n" ] }, { "data": { "text/html": [ "