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Commit acd0215f authored by lucas_miranda's avatar lucas_miranda
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Implemented VAEP hypermodel in hypermodels.py

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import warnings\n",
'warnings.filterwarnings("ignore")',
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#from source.utils import *\n",
"from source.preprocess import *\n",
"import pickle\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from collections import defaultdict\n",
"from tqdm import tqdm_notebook as tqdm",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {"tags": ["parameters"]},
"outputs": [],
"source": ['path = "../../Desktop/DLC_social_1/"'],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Set up and design the project"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('{}DLC_social_1_exp_conditions.pickle'.format(path), 'rb') as handle:\n",
" Treatment_dict = pickle.load(handle)",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Which angles to compute?\n",
"bp_dict = {'B_Nose':['B_Left_ear','B_Right_ear'],\n",
" 'B_Left_ear':['B_Nose','B_Right_ear','B_Center','B_Left_flank'],\n",
" 'B_Right_ear':['B_Nose','B_Left_ear','B_Center','B_Right_flank'],\n",
" 'B_Center':['B_Left_ear','B_Right_ear','B_Left_flank','B_Right_flank','B_Tail_base'],\n",
" 'B_Left_flank':['B_Left_ear','B_Center','B_Tail_base'],\n",
" 'B_Right_flank':['B_Right_ear','B_Center','B_Tail_base'],\n",
" 'B_Tail_base':['B_Center','B_Left_flank','B_Right_flank']}",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"DLC_social_1 = project(path=path,#Path where to find the required files\n",
" smooth_alpha=0.85, #Alpha value for exponentially weighted smoothing\n",
" distances=['B_Center','B_Nose','B_Left_ear','B_Right_ear','B_Left_flank',\n",
" 'B_Right_flank','B_Tail_base'],\n",
" ego=False,\n",
" angles=True,\n",
" connectivity=bp_dict,\n",
" arena='circular', #Type of arena used in the experiments\n",
" arena_dims=[380], #Dimensions of the arena. Just one if it's circular\n",
" video_format='.mp4',\n",
" table_format='.h5',\n",
" exp_conditions=Treatment_dict)",
],
},
{"cell_type": "markdown", "metadata": {}, "source": ["# Run project"]},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"DLC_social_1_coords = DLC_social_1.run(verbose=True)\n",
"print(DLC_social_1_coords)\n",
"type(DLC_social_1_coords)",
],
},
{"cell_type": "markdown", "metadata": {}, "source": ["# Generate coords"]},
{
"cell_type": "code",
"execution_count": null,
"metadata": {"scrolled": true},
"outputs": [],
"source": [
"%%time\n",
"ptest = DLC_social_1_coords.get_coords(center=True, polar=False, speed=0, length='00:10:00')\n",
"ptest._type",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"dtest = DLC_social_1_coords.get_distances(speed=0, length='00:10:00')\n",
"dtest._type",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"atest = DLC_social_1_coords.get_angles(degrees=True, speed=0, length='00:10:00')\n",
"atest._type",
],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Visualization playground"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["#ptest.plot_heatmaps(['B_Center', 'W_Center'], i=1)"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Plot animation of trajectory over time with different smoothings\n",
"#plt.plot(ptest['Day2Test13DLC']['B_Center'].iloc[:5000]['x'],\n",
"# ptest['Day2Test13DLC']['B_Center'].iloc[:5000]['y'], label='alpha=0.85')\n",
"\n",
"#plt.xlabel('x')\n",
"#plt.ylabel('y')\n",
"#plt.title('Mouse Center Trajectory using different exponential smoothings')\n",
"#plt.legend()\n",
"#plt.show()",
],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Dimensionality reduction playground"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["#pca = ptest.pca(4, 1000)"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["#plt.scatter(*pca[0].T)\n", "#plt.show()"],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Preprocessing playground"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mtest = merge_tables(DLC_social_1_coords.get_coords(center=True, polar=True, length='00:10:00'))#,\n",
"# DLC_social_1_coords.get_distances(speed=0, length='00:10:00'),\n",
"# DLC_social_1_coords.get_angles(degrees=True, speed=0, length='00:10:00'))",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#pptest = mtest.preprocess(window_size=51, filter='gaussian', sigma=10, shift=20)"
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pttest = mtest.preprocess(window_size=11, window_step=6, filter=None, standard_scaler=True)\n",
"pttest.shape",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plt.plot(pttest[2,:,2], label='normal')\n",
"#plt.plot(pptest[2,:,2], label='gaussian')\n",
"#plt.legend()\n",
"#plt.show()",
],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Trained models playground"],
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["### Seq 2 seq Variational Auto Encoder"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from tensorflow.keras import Input, Model, Sequential\n",
"from tensorflow.keras.constraints import UnitNorm\n",
"from tensorflow.keras.layers import Bidirectional, Dense, Dropout\n",
"from tensorflow.keras.layers import Lambda, LSTM\n",
"from tensorflow.keras.layers import RepeatVector, TimeDistributed\n",
"from tensorflow.keras.losses import Huber\n",
"from tensorflow.keras.optimizers import Adam\n",
"from source.model_utils import *\n",
"import keras as k\n",
"import tensorflow as tf",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"NAME = 'Baseline_VAE_short_512'\n",
"log_dir = os.path.abspath(\n",
' "logs/fit/{}_{}".format(NAME, datetime.now().strftime("%Y%m%d-%H%M%S"))\n',
")\n",
"tensorboard_callback = k.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from source.models import SEQ_2_SEQ_AE, SEQ_2_SEQ_VAE, SEQ_2_SEQ_VAEP"
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["encoder, decoder, ae = SEQ_2_SEQ_AE(pttest.shape).build()"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["encoder, generator, vae = SEQ_2_SEQ_VAE(pttest.shape).build()"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"encoder, generator, vaep = SEQ_2_SEQ_VAEP(pttest.shape).build()"
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["#ae.summary()\n", "#vae.summary()\n", "#vaep.summary()"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {"scrolled": false},
"outputs": [],
"source": [
"#from tensorflow.keras.utils import plot_model\n",
"#plot_model(vaep, show_shapes=True)",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {"scrolled": false},
"outputs": [],
"source": ["#plot_model(vae)"],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#np.random.shuffle(pttest)\n",
"pttrain = pttest[:-15000]\n",
"pttest = pttest[-15000:]",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#tf.config.experimental_run_functions_eagerly(False)\n",
"history = ae.fit(x=pttrain[:-1], y=pttrain[:-1], epochs=100, batch_size=512, verbose=1,\n",
" validation_data=(pttest[:-1], pttest[:-1]),\n",
" callbacks=[tensorboard_callback])",
],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {"scrolled": true},
"outputs": [],
"source": [
"#tf.config.experimental_run_functions_eagerly(False)\n",
"# history = vaep.fit(x=pttrain[:-1], y=[pttrain[:-1],pttrain[1:]], epochs=100, batch_size=512, verbose=1,\n",
"# validation_data=(pttest[:-1], [pttest[:-1],pttest[1:]]),\n",
"# callbacks=[tensorboard_callback])",
],
},
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3",
},
"language_info": {
"codemirror_mode": {"name": "ipython", "version": 3},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10",
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#from source.utils import *\n",
"from source.preprocess import *\n",
"import pickle\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from collections import defaultdict\n",
"from tqdm import tqdm_notebook as tqdm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"path = \"../../Desktop/DLC_social_1/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set up and design the project"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"with open('{}DLC_social_1_exp_conditions.pickle'.format(path), 'rb') as handle:\n",
" Treatment_dict = pickle.load(handle)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#Which angles to compute?\n",
"bp_dict = {'B_Nose':['B_Left_ear','B_Right_ear'],\n",
" 'B_Left_ear':['B_Nose','B_Right_ear','B_Center','B_Left_flank'],\n",
" 'B_Right_ear':['B_Nose','B_Left_ear','B_Center','B_Right_flank'],\n",
" 'B_Center':['B_Left_ear','B_Right_ear','B_Left_flank','B_Right_flank','B_Tail_base'],\n",
" 'B_Left_flank':['B_Left_ear','B_Center','B_Tail_base'],\n",
" 'B_Right_flank':['B_Right_ear','B_Center','B_Tail_base'],\n",
" 'B_Tail_base':['B_Center','B_Left_flank','B_Right_flank']}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.7 s, sys: 833 ms, total: 3.54 s\n",
"Wall time: 1.23 s\n"
]
}
],
"source": [
"%%time\n",
"DLC_social_1 = project(path=path,#Path where to find the required files\n",
" smooth_alpha=0.85, #Alpha value for exponentially weighted smoothing\n",
" distances=['B_Center','B_Nose','B_Left_ear','B_Right_ear','B_Left_flank',\n",
" 'B_Right_flank','B_Tail_base'],\n",
" ego=False,\n",
" angles=True,\n",
" connectivity=bp_dict,\n",
" arena='circular', #Type of arena used in the experiments\n",
" arena_dims=[380], #Dimensions of the arena. Just one if it's circular\n",
" video_format='.mp4',\n",
" table_format='.h5',\n",
" exp_conditions=Treatment_dict)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run project"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading trajectories...\n",
"Smoothing trajectories...\n",
"Computing distances...\n",
"Computing angles...\n",
"Done!\n",
"Coordinates of 47 videos across 4 conditions\n",
"CPU times: user 9.08 s, sys: 636 ms, total: 9.72 s\n",
"Wall time: 11.7 s\n"
]
},
"nbformat": 4,
"nbformat_minor": 4,
{
"data": {
"text/plain": [
"source.preprocess.coordinates"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"DLC_social_1_coords = DLC_social_1.run(verbose=True)\n",
"print(DLC_social_1_coords)\n",
"type(DLC_social_1_coords)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate coords"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 804 ms, sys: 66.5 ms, total: 870 ms\n",
"Wall time: 835 ms\n"
]
},
{
"data": {
"text/plain": [
"'coords'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"ptest = DLC_social_1_coords.get_coords(center=True, polar=False, speed=0, length='00:10:00')\n",
"ptest._type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 563 ms, sys: 402 ms, total: 965 ms\n",
"Wall time: 971 ms\n"
]
},
{
"data": {
"text/plain": [
"'dists'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"dtest = DLC_social_1_coords.get_distances(speed=0, length='00:10:00')\n",
"dtest._type"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 134 ms, sys: 80.2 ms, total: 214 ms\n",
"Wall time: 214 ms\n"
]
},
{
"data": {
"text/plain": [
"'angles'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"atest = DLC_social_1_coords.get_angles(degrees=True, speed=0, length='00:10:00')\n",
"atest._type"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization playground"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#ptest.plot_heatmaps(['B_Center', 'W_Center'], i=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Plot animation of trajectory over time with different smoothings\n",
"#plt.plot(ptest['Day2Test13DLC']['B_Center'].iloc[:5000]['x'],\n",
"# ptest['Day2Test13DLC']['B_Center'].iloc[:5000]['y'], label='alpha=0.85')\n",
"\n",
"#plt.xlabel('x')\n",
"#plt.ylabel('y')\n",
"#plt.title('Mouse Center Trajectory using different exponential smoothings')\n",
"#plt.legend()\n",
"#plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dimensionality reduction playground"
]
},