From cacd6b83203cfdf5df6612f766937896419a96d1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Tubiana?= Date: Sat, 25 Dec 2021 19:24:49 +0200 Subject: [PATCH] Improved script for visualization of spatio-chemical filters --- Saliency analysis.ipynb | 1226 +++++++++++++++++ Visualize spatio-chemical filters.ipynb | 778 +++-------- ...superimposed neighborhood and filter.ipynb | 489 +++---- datasets/.DS_Store | Bin 6148 -> 6148 bytes visualizations/show_3d_filters.py | 70 +- visualizations/show_3d_neighborhoods.py | 84 +- 6 files changed, 1724 insertions(+), 923 deletions(-) create mode 100644 Saliency analysis.ipynb diff --git a/Saliency analysis.ipynb b/Saliency analysis.ipynb new file mode 100644 index 0000000..af6d294 --- /dev/null +++ b/Saliency analysis.ipynb @@ -0,0 +1,1226 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "93d849ab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from utilities import wrappers,dataset_utils\n", + "from preprocessing import pipelines,PDBio,PDB_processing,protein_chemistry,sequence_utils\n", + "import tensorflow as tf\n", + "import keras.backend as K\n", + "import numpy as np\n", + "from visualizations import show_3d_filters,show_3d_neighborhoods,weight_logo_3d\n", + "import predict_features\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "# %matplotlib inline\n", + "\n", + "def flatten_list(nested_list):\n", + " out = []\n", + " for list_ in nested_list:\n", + " out+= list_\n", + " return np.array(out)\n", + "\n", + "def get_gradient(model, input, residue_index, layer = 'SCAN_filter_activity_atom'):\n", + " gradient0 = K.gradients(\n", + " model.model.get_layer('attention_layer').output[0][:, residue_index, 0],\n", + " model.model.get_layer(layer).output\n", + " )\n", + "\n", + " gradient1 = K.gradients(\n", + " model.model.get_layer('attention_layer').output[0][:, residue_index, 1],\n", + " model.model.get_layer(layer).output\n", + " )\n", + " gradient = gradient0 + gradient1\n", + " activation = model.model.get_layer(layer).output\n", + "\n", + " sess = K.get_session()\n", + " output = sess.run(gradient +[activation], feed_dict=dict(\n", + " [(input_tensor,input_value[np.newaxis]) for input_tensor,input_value in zip(model.model.inputs, input) ]\n", + " )\n", + " )\n", + " test_gradient = output[1][0] - output[0][0]\n", + " test_activation = output[-1][0]\n", + " return test_gradient,test_activation\n", + "\n", + "sg = weight_logo_3d.make_sphere_geometry(30)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9528e8bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", + "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", + "\n", + "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", + "\n", + "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:71: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " list_labels = np.array(list_labels)\n", + "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:72: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", + " list_resids = np.array(list_resids)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/embeddings.py:432: The name tf.squared_difference is deprecated. Please use tf.math.squared_difference instead.\n", + "\n", + "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", + "\n", + "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:68: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", + "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:72: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "keep_dims is deprecated, use keepdims instead\n", + "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", + "\n", + "Model: \"model_1\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_atom (InputLayer) (None, 1746) 0 \n", + "__________________________________________________________________________________________________\n", + "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", + " masked_frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", + " embedded_attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", + " neighborhood_atom[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_aa (InputLayer) (None, 194, 20) 0 \n", + "__________________________________________________________________________________________________\n", + "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", + " masked_sequence_indices_atom[0][0\n", + " attention_pooling_coefficients_at\n", + " attention_pooling_features_atom[0\n", + "__________________________________________________________________________________________________\n", + "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", + " pooling_intermediate_atom[0][2] \n", + " pooling_edges_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", + " masked_frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", + " SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", + " all_embedded_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", + " neighborhood_aa[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", + "__________________________________________________________________________________________________\n", + "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", + " masked_sequence_indices_aa[0][0] \n", + " cross_attention[0][0] \n", + " node_output[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", + "__________________________________________________________________________________________________\n", + "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", + " self_attention[0][0] \n", + " neighborhood_graph[0][1] \n", + " neighborhood_graph[0][2] \n", + " edges_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", + "==================================================================================================\n", + "Total params: 474,912\n", + "Trainable params: 473,988\n", + "Non-trainable params: 924\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py:66: NumbaPendingDeprecationWarning: \n", + "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function 'impl_append..impl'.\n", + "\n", + "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", + "\n", + "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/listobject.py\", line 597:\n", + "\n", + " def impl(l, item):\n", + " ^\n", + "\n", + " l.append(item)\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", + "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function '_append'.\n", + "\n", + "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", + "\n", + "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py\", line 65:\n", + "@njit\n", + "def _append(l, item):\n", + "^\n", + "\n", + " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", + "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'in_seq..seq_contains_impl'.\n", + "\n", + "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", + "\n", + "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 660:\n", + "def in_seq(context, builder, sig, args):\n", + " def seq_contains_impl(lst, value):\n", + " ^\n", + "\n", + " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", + "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'list_index..list_index_impl'.\n", + "\n", + "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", + "\n", + "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 911:\n", + "\n", + " def list_index_impl(lst, value):\n", + " ^\n", + "\n", + " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting binding sites from pdb structures with ScanNet_interface\n", + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", + "List of inputs:\n", + "['7jvb_0_A']\n", + "Loading model ScanNet_interface\n", + "Model: \"model_2\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_atom (InputLayer) (None, 1746) 0 \n", + "__________________________________________________________________________________________________\n", + "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", + " masked_frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", + " embedded_attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", + " neighborhood_atom[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_aa (InputLayer) (None, 194, 20) 0 \n", + "__________________________________________________________________________________________________\n", + "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", + " masked_sequence_indices_atom[0][0\n", + " attention_pooling_coefficients_at\n", + " attention_pooling_features_atom[0\n", + "__________________________________________________________________________________________________\n", + "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", + " pooling_intermediate_atom[0][2] \n", + " pooling_edges_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", + " masked_frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", + " SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", + " all_embedded_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", + " neighborhood_aa[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", + "__________________________________________________________________________________________________\n", + "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", + " masked_sequence_indices_aa[0][0] \n", + " cross_attention[0][0] \n", + " node_output[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", + "__________________________________________________________________________________________________\n", + "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", + " self_attention[0][0] \n", + " neighborhood_graph[0][1] \n", + " neighborhood_graph[0][2] \n", + " edges_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", + "==================================================================================================\n", + "Total params: 474,912\n", + "Trainable params: 473,988\n", + "Non-trainable params: 924\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating groups...\n", + "Grouped 1 examples in 1 groups\n", + "Grouping and padding...\n", + "Performing prediction...\n", + "1/1 [==============================] - 1s 793ms/step\n", + "Ungrouping and unpadding...\n", + "prediction done!\n", + "Predicting binding sites from pdb structures with ScanNet_interface\n", + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", + "List of inputs:\n", + "['7jvb_0_A']\n", + "Loading model ScanNet_interface\n", + "Model: \"model_3\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_atom (InputLayer) (None, 1746) 0 \n", + "__________________________________________________________________________________________________\n", + "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", + " masked_frame_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", + " embedded_attributes_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", + " neighborhood_atom[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "attributes_aa (InputLayer) (None, 194, 20) 0 \n", + "__________________________________________________________________________________________________\n", + "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", + " masked_sequence_indices_atom[0][0\n", + " attention_pooling_coefficients_at\n", + " attention_pooling_features_atom[0\n", + "__________________________________________________________________________________________________\n", + "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", + " pooling_intermediate_atom[0][2] \n", + " pooling_edges_atom[0][0] \n", + "__________________________________________________________________________________________________\n", + "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", + "__________________________________________________________________________________________________\n", + "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", + " masked_frame_indices_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", + " SCAN_filters_atom_aggregated_acti\n", + "__________________________________________________________________________________________________\n", + "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", + " all_embedded_attributes_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", + " neighborhood_aa[0][1] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", + "__________________________________________________________________________________________________\n", + "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", + "__________________________________________________________________________________________________\n", + "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", + " masked_sequence_indices_aa[0][0] \n", + " cross_attention[0][0] \n", + " node_output[0][0] \n", + "__________________________________________________________________________________________________\n", + "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", + "__________________________________________________________________________________________________\n", + "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", + " self_attention[0][0] \n", + " neighborhood_graph[0][1] \n", + " neighborhood_graph[0][2] \n", + " edges_graph[0][0] \n", + "__________________________________________________________________________________________________\n", + "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", + "==================================================================================================\n", + "Total params: 474,912\n", + "Trainable params: 473,988\n", + "Non-trainable params: 924\n", + "__________________________________________________________________________________________________\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating groups...\n", + "Grouped 1 examples in 1 groups\n", + "Grouping and padding...\n", + "Performing prediction...\n", + "1/1 [==============================] - 1s 798ms/step\n", + "Ungrouping and unpadding...\n", + "prediction done!\n", + "Warning, attention coeffs are flipped\n" + ] + } + ], + "source": [ + "\n", + "model_name = 'ScanNet_PAI_noMSA_0'\n", + "dataset_name = 'BCE_fold1'\n", + "pdbid = '7jvb_A'\n", + "biounit = False\n", + "\n", + "list_origins = dataset_utils.read_labels('datasets/BCE/labels_fold1.txt')[0]\n", + "filter_specificities = show_3d_filters.calculate_filter_specificities(\n", + " model_name,\n", + " dataset_name = dataset_name,\n", + " dataset_origins = list_origins,\n", + " biounit=False,\n", + " ncores=4,\n", + " only_atom=False\n", + ")\n", + "\n", + "\n", + "\n", + "file,chainid = PDBio.getPDB(pdbid,biounit=biounit)\n", + "chain = PDBio.load_chains(file=file,chain_ids=chainid)[1]\n", + "resnumber = list(PDB_processing.get_PDB_indices(chain) )\n", + "sequence,_,_,atomids,_ = PDB_processing.process_chain(chain)\n", + "atomids = flatten_list(atomids)\n", + "nresidues = len(resnumber)\n", + "natoms = len(atomids)\n", + "model = wrappers.load_model('models/%s'%model_name,Lmax=nresidues)\n", + "pipeline = pipelines.ScanNetPipeline(Lmax_aa=nresidues,padded=True)\n", + "\n", + "\n", + "inputs = pipeline.process_example(chain_obj=chain)[0]\n", + "\n", + "dictionary_binding = predict_features.predict_features(pdbid,model=model_name,\n", + " layer=None )\n", + "dictionary_attention = predict_features.predict_features(pdbid,model=model_name,\n", + " layer='attention_layer' )\n", + "\n", + "\n", + "vector_binding = np.array(list(dictionary_binding.values()))\n", + "vector_attention = np.array(list(dictionary_attention.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f3b1334", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(figsize=(16, 8))\n", + "ax.plot(vector_binding)\n", + "ax2 = ax.twinx()\n", + "ax2.plot(vector_attention, c='gray')\n", + "ax.set_xticks(np.arange(nresidues)[::4])\n", + "ax.set_xticklabels(resnumber[::4], rotation=90)\n", + "ax.grid()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "e2fb9984", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Residue: 417K, binding: 0.252, attention: 1.468\n" + ] + } + ], + "source": [ + "modelid = 0\n", + "chainid = 'A'\n", + "residue = 417\n", + "\n", + "print ('Residue: %s%s, binding: %.3f, attention: %.3f'%(\n", + "residue,\n", + "sequence[resnumber.index(residue)],\n", + "dictionary_binding[(modelid,chainid,residue)],\n", + "dictionary_attention[(modelid,chainid,residue)], \n", + ") \n", + " )\n", + "\n", + "\n", + "residue_index = resnumber.index(residue)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "380c01a6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in true_divide\n", + " ret, rcount, out=ret, casting='unsafe', subok=False)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "455 CD1 119 0.54047835\n", + "491 N 117 0.41363844\n", + "493 OE1 119 0.37670958\n", + "453 O 43 0.19730543\n", + "455 CD1 64 0.17299509\n", + "493 N 119 0.15970834\n", + "415 CG2 64 0.15620513\n", + "417 NZ 64 0.12385975\n", + "455 CD1 46 0.10351321\n", + "493 CD 24 0.102931134\n", + "456 CG 119 0.10242688\n", + "491 N 100 -0.15572345\n", + "455 N 40 -0.17212613\n", + "455 CD1 10 -0.19442242\n" + ] + } + ], + "source": [ + "gradient,activation = get_gradient(model, inputs, residue_index, \n", + " layer = 'SCAN_filter_activity_atom'\n", + " )\n", + "try:\n", + " conditional_activity = filter_specificities['filter_conditional_activity_atom']['cond_median']\n", + "except:\n", + " conditional_activity = np.array([np.median(activation[:natoms][atomids==k], axis=0) for k in range(38)])\n", + "baseline_activation = conditional_activity[atomids,:]\n", + "saliency = (gradient[:natoms] * (activation[:natoms] - baseline_activation ) )\n", + "\n", + "plt.matshow(saliency.T, aspect='auto',\n", + " cmap='coolwarm', vmax=0.8 * np.abs(saliency).max(), vmin=- 0.8 * np.abs(saliency).max())\n", + "plt.title('Saliency map')\n", + "plt.xlabel('Atom index')\n", + "plt.ylabel('Filter index')\n", + "plt.show()\n", + "\n", + "\n", + "fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", + "\n", + "ax[0].hist(saliency.flatten(), bins=100, log=True)\n", + "atom_importance = np.sqrt((saliency ** 2).sum(-1))\n", + "ax[0].set_title('(Atom,Filter) importance',fontsize=14)\n", + "ax[1].hist(atom_importance, bins=100, log=True)\n", + "ax[1].set_title('Atom importance',fontsize=14)\n", + "plt.show()\n", + "\n", + "\n", + "explaining_atom_pairs = np.nonzero(np.abs(saliency) >= 0.1)\n", + "explanation_values = saliency[explaining_atom_pairs[0], explaining_atom_pairs[1]]\n", + "\n", + "order = np.argsort(explanation_values)[::-1]\n", + "\n", + "explaining_resids = [resnumber[x] for x in inputs[6][explaining_atom_pairs[0], 0]]\n", + "explaining_atomids = [protein_chemistry.list_atoms[x] for x in atomids[explaining_atom_pairs[0]]]\n", + "\n", + "nexplanations = len(explaining_resids)\n", + "\n", + "for n in order:\n", + " print(explaining_resids[n], explaining_atomids[n], explaining_atom_pairs[1][n], explanation_values[n])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88c25c30", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n" + ] + } + ], + "source": [ + "explaining_residue = 455\n", + "explaining_atom = 'CD1'\n", + "explaining_filter =119\n", + "\n", + "\n", + "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", + " pdb = pdbid[:4],\n", + " model = modelid,\n", + " chain = chainid,\n", + " resnumber = explaining_residue,\n", + " atom = explaining_atom,\n", + " assembly=False,\n", + " biounit=biounit,\n", + ")\n", + "\n", + "\n", + "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False,\n", + " radius_scale = 0.15)\n", + "\n", + "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,\n", + " explaining_filter,\n", + " y_offset = 0.25,\n", + " sg=sg,\n", + " list_additional_objects=list_objects,\n", + " threshold1=0.33);\n", + "display(renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "8e9e1079", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in true_divide\n", + " ret, rcount, out=ret, casting='unsafe', subok=False)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RESID: 442 D Filter: 94, Activity: 2.01, Saliency 0.12 \n", + "RESID: 451 Y Filter: 57, Activity: 0.88, Saliency 0.10 \n", + "RESID: 442 D Filter: 30, Activity: 1.88, Saliency -0.11 \n", + "RESID: 352 A Filter: 30, Activity: 0.96, Saliency -0.12 \n" + ] + } + ], + "source": [ + "gradient,activation = get_gradient(model, inputs, residue_index, \n", + " layer = 'SCAN_filter_activity_aa'\n", + " )\n", + "\n", + "seqnum = sequence_utils.seq2num(sequence)[0]\n", + "\n", + "\n", + "try:\n", + " conditional_activity = filter_specificities['filter_conditional_activity_aa']['cond_median']\n", + "except:\n", + " conditional_activity = np.array([np.median(activation[seqnum==k], axis=0) for k in range(20)])\n", + "baseline_activation = conditional_activity[seqnum,:]\n", + "saliency = (gradient * (activation - baseline_activation ) )\n", + "\n", + "\n", + "\n", + "plt.matshow(saliency.T, aspect='auto',\n", + " cmap='coolwarm', vmax=0.8 * np.abs(saliency).max(), vmin=- 0.8 * np.abs(saliency).max())\n", + "plt.title('Saliency map')\n", + "plt.xlabel('Atom index')\n", + "plt.ylabel('Filter index')\n", + "plt.show()\n", + "\n", + "\n", + "fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", + "\n", + "ax[0].hist(saliency.flatten(), bins=100, log=True)\n", + "residue_importance = np.sqrt((saliency ** 2).sum(-1))\n", + "ax[0].set_title('(Residue,Filter) importance',fontsize=14)\n", + "ax[1].hist(residue_importance, bins=100, log=True)\n", + "ax[1].set_title('Residue importance',fontsize=14)\n", + "plt.show()\n", + "\n", + "\n", + "explaining_residue_pairs = np.nonzero(np.abs(saliency) >= 0.1)\n", + "explanation_saliencies = saliency[explaining_residue_pairs[0], explaining_residue_pairs[1]]\n", + "explanation_values = activation[explaining_residue_pairs[0], explaining_residue_pairs[1]]\n", + "\n", + "order = np.argsort(explanation_saliencies)[::-1]\n", + "\n", + "explaining_resids = [resnumber[x] for x in explaining_residue_pairs[0] ]\n", + "explaining_residue = [sequence[x] for x in explaining_residue_pairs[0] ]\n", + "\n", + "nexplanations = len(explaining_resids)\n", + "\n", + "for n in order:\n", + " print('RESID: %s %s Filter: %s, Activity: %.2f, Saliency %.2f ' % (explaining_resids[n], explaining_residue[n], explaining_residue_pairs[1][n], \n", + " explanation_values[n], explanation_saliencies[n]) )\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "73246209", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7914a401ff78450bac293d42d7b08b45", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Renderer(camera=PerspectiveCamera(position=(19.200000000000003, 12.0, 19.200000000000003), projectionMatrix=(1…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "explaining_residue = 442\n", + "explaining_atom = None\n", + "explaining_filter = 94\n", + "\n", + "\n", + "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", + " pdb = pdbid[:4],\n", + " model = modelid,\n", + " chain = chainid,\n", + " resnumber = explaining_residue,\n", + " atom = explaining_atom,\n", + " assembly=False,\n", + " biounit=biounit,\n", + " Kmax=9*16\n", + ")\n", + "\n", + "\n", + "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False)\n", + "\n", + "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,\n", + " explaining_filter,\n", + " sg=sg,\n", + " list_additional_objects=list_objects,\n", + " threshold1=0.33);\n", + "display(renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ffc3fab4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RESID: 450 N Saliency 0.35 \n", + "RESID: 444 K Saliency -0.29 \n" + ] + } + ], + "source": [ + "gradient,activation = get_gradient(model, inputs, residue_index, \n", + " layer = 'attributes_aa'\n", + " )\n", + "\n", + "seqnum = sequence_utils.seq2num(sequence)[0]\n", + "\n", + "baseline_activation = np.array([1] + 19*[0])[np.newaxis]\n", + "\n", + "saliency = (gradient * (activation - baseline_activation ) ).sum(-1)\n", + "\n", + "\n", + "\n", + "plt.plot(saliency)\n", + "plt.title('Saliency map')\n", + "plt.xlabel('Residue index')\n", + "plt.ylabel('Saliency')\n", + "plt.show()\n", + "\n", + "\n", + "fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", + "\n", + "ax[0].hist(saliency.flatten(), bins=100, log=True)\n", + "residue_importance = np.sqrt((saliency ** 2).sum(-1))\n", + "ax[0].set_title('(Residue,Filter) importance',fontsize=14)\n", + "ax[1].hist(residue_importance, bins=100, log=True)\n", + "ax[1].set_title('Residue importance',fontsize=14)\n", + "plt.show()\n", + "\n", + "\n", + "explaining_residues = np.abs(saliency)>0.2\n", + "explanation_saliencies = saliency[explaining_residues]\n", + "\n", + "order = np.argsort(explanation_saliencies)[::-1]\n", + "\n", + "explaining_resids = [resnumber[x] for x in explaining_residue_pairs[0] ]\n", + "explaining_residue = [sequence[x] for x in explaining_residue_pairs[0] ]\n", + "\n", + "nexplanations = len(explaining_resids)\n", + "\n", + "for n in order:\n", + " print('RESID: %s %s Saliency %.2f ' % (explaining_resids[n], explaining_residue[n], explanation_saliencies[n]) )\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "49165c03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.matshow(gradient.T)" + ] + } + ], + "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": "3.6.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Visualize spatio-chemical filters.ipynb b/Visualize spatio-chemical filters.ipynb index e34a8a5..87e2f99 100644 --- a/Visualize spatio-chemical filters.ipynb +++ b/Visualize spatio-chemical filters.ipynb @@ -77,489 +77,12 @@ "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "primary\n", - "secondary\n", - "Accessible surface area\n", - "Discrepancy between lengthes, discarding example 186 190\n", - "Discrepancy between lengthes, discarding example 195 196\n", - "Discrepancy between lengthes, discarding example 186 190\n", - "Discrepancy between lengthes, discarding example 187 191\n", - "Discrepancy between lengthes, discarding example 186 190\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:71: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " list_labels = np.array(list_labels)\n", "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:72: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", " list_resids = np.array(list_resids)\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/embeddings.py:432: The name tf.squared_difference is deprecated. Please use tf.math.squared_difference instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:68: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:72: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "Model: \"model_1\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 11264, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 9216, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 11264, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 9216, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 9216) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 9216, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 9216, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 9216, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 9216, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 9216, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 9216, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 1024, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 9216, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 9216, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 1024, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 9216, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 1024, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 1024, 14, 1) 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 1024, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 1024, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 1024, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 1024, 64), ( 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 2049, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 1024, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 1024, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 2049, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 1024, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 1024, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 1024, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 1024, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 1024, 16, 3) 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 1024, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 1024, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 1024, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 1024, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 1024, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 1024, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 1024, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 1024, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 1024, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 1024, 32, 5) 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 1024, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 1024, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 1024, 2), (N 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 1024, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_2\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 11264, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 9216, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 11264, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 9216, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 9216) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 9216, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 9216, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 9216, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 9216, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 9216, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 9216, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 1024, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 9216, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 9216, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 1024, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 9216, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 1024, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 1024, 14, 1) 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 1024, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 1024, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 1024, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 1024, 64), ( 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 2049, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 1024, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 1024, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 2049, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 1024, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 1024, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 1024, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 1024, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 1024, 16, 3) 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 1024, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 1024, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 1024, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 1024, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 1024, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 1024, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 1024, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 1024, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 1024, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 1024, 32, 5) 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 1024, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 1024, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 1024, 2), (N 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 1024, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"model_4\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 11264, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 9216, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 11264, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 9216, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 9216) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 9216, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 9216, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 9216, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 9216, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 9216, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 9216, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 1024, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 9216, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 9216, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 1024, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 9216, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 9216, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 1024, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 1024, 14, 1) 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 1024, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 1024, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 1024, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 1024, 64), ( 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 2049, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 1024, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 1024, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 2049, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 1024, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 1024, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 1024, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 1024, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 1024, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 1024, 16, 3) 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 1024, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 1024, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 1024, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 1024, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 1024, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 1024, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 1024, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 1024, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 1024, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 1024, 32, 5) 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 1024, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 1024, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 1024, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 1024, 2), (N 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 1024, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 985 examples in 413 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "413/413 [==============================] - 501s 1s/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Generating groups...\n", - "Grouped 985 examples in 413 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/wrappers.py:404: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", - " return np.array([output_ for output_ in outputs])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "413/413 [==============================] - 544s 1s/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n" - ] } ], "source": [ @@ -579,7 +102,7 @@ " ncores=4,\n", " only_atom=False,\n", " top_percent = 5,\n", - " fresh = True,\n", + " fresh = False,\n", " Lmax = 1024\n", "\n", ")\n", @@ -587,74 +110,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100, 128)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "filter_specificities['filter_activity']['percentiles'].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "plt.plot(filter_specificities['filter_activity']['percentiles'][:,:10] )\n", - "\n", - "plt.matshow(filter_specificities['filter_activity']['correlation'])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -681,74 +136,80 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 19, "metadata": {}, "outputs": [ - { - "ename": "ValueError", - "evalue": "shapes (0,) and (3,3) not aligned: 0 (dim 0) != 3 (dim 0)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\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[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m 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\n", 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gBSQY2Flgunns2EVNMrgMGpARYD3wlWI1Wd+jgjcA3vvnEwZ1rum27XwKUNixeQ8HbLoxt8kjPFnZC7i0WO3/kZ6Cl7FWl8knSTmEfZTpwmGT10Zrmqkn7Q/SSuBw4F/7faOClyHv/QrgC2RU/3iE6eYxY5fk+ZQL4RfsjGKVY/t5k4KXrbcBu5LRehcF4sIuM3dGe0/9KNfJI4Tvs8Vq75cWCl5GvPcHEIaqZzpLbJTp6IiJK6OVzTxOv93GOuDtvW6s4GXnPxjQurWjbJ45cvzyvD1Om2sV8OpilWIvGyt4GWjNiz2cAX2fBeKRPad/UXjU5tsHsfssFYC/73VDSc8ROlYHZpTp6OjxL7IsznWtn+XAK4pVduq2oYKXkvd+L8IssIE/XF0Wb5o5fPyqvJ9yIVzrdqTgpfcqhvQ9jtAceczmW0Z2m24M4+OSWgX8XbHa+c5ewUvvxQz4NLu1UaZ56tjF8Ujc95omw7QMOLTTBgpeCt77RxAeGw1VTgeNbm058NxOGyh46ZxEmLM6VDkeNDprOeFM0JaCl86zSTgQIK0RpuNjxy+Mozi3Dd+ji1V2bPeigpfOgVYfvAAGjU4A+7Z7UcFLqDXQ85GWx5DzQaMRYaLQvBS85PYgTH42leNBo2sIw+TnpeAltzc5WK8ix4NGC8CfdXpRkrGqVbydHA8abVtMUkV7khtap3EvRpieOeXB/xiZjpblpOWLol8v2++RcPK8ryp4yU2TnyUCKBCPFNjMsnhzbuYm7zt18x/aBU+n2uRyPUwkDwrEbVcuUvCS+y06Y3QSE6oOzEvBS+5XzFcSVmaNQ/sKUwpeQs652fW+ZH7TQNsh0wpeOm1PJcIKdKodmGvJQSdyTm0E2g6fUfDSuZSwxKZsqwl8sdOKQQpeOj/ioSWa5CEbgYs6bZBJYcalzHv/UeB09Eu8tUlgvXOubV+nvqz0/gu1elubJpxmO3awK3gpOefqdOg2WII2E2rIdKTgZeMfCNc1S10MfMs5d2u3DRW8bNSAe6wPIgfGCQUpu+p4c1GvRVk9EopL5XzUXqjXogIZDWkqleMt13be+2cD/4PR5J8cmAa+7pw7rpeNu7V4lxImbaT+U69Ff9XvT5K11i/SZWTzM20z8sI5dzlhJcShT3fMiSmg53/jbsF7NvDZVIcTRMBH67XotRnsK5F6LVoDXA6cmMHuNhNWW5zrdJbmHe4Y8Fbn3B29vqFj8ErleAp4GfDvKQ9s1vn1WvTGjPbVs3ot2gH4EvC0DHY3BpxUKsfb/UK2llv/W+a0hovcZsIolGo/b+qpA7leiyLgLcA7Eh3a9hzw9lJ58L3X9Vr0cELonpTB7u4ByqVyfEOnjbz35xNav9zMyxiQGLgLeIJzrq+Vvnu6qy2V47hUjt8JnEl4DpeWB97VCvTA1GvRrsBXySZ0vwGO6ha6lgrwTRb/aXcjcFy/oYMEj8zqtegUwt1bFneG/wa8bhAtX70WPQL4X2D/DHZ3K/CMUjn+ba9v8N6vAW4E9oHBrPlqbAI40Tn31SRv7rsfr1SOvwA8E9iQ5APn+DvgQ60ujszUa1ERuJ5sQvdt4Oh+QgfgnBsjLLv+CxZfyzcOvCxp6CDFIIF6LTqUcO20a9IP38oFwBmlcpy6K6Jei/YBvgI8KvVRhZ/vBaVynLjkuvd+HaGD+c9Z+Nd8MeH0eqJz7utpdpS4pSmV4+8DRwI930J38HLgM/VauuWp67XoQEJLl0XoPg2cnCZ0AM65DcDxwJWEO+KFappwc1VKGzpI+cisVI5/TgjfD9MeCPBC4OKkT0taLfB1hJomaVWBvyyVsym76ZybAl5EeHg+TjY3aMM0BtQJd6+3ZLHDrBZKXk94InB06p3BNcDzSuX2czLn+fynEE6L6zP4/LcA7x5UV4/3/iDgYkKrnPdT7wxh/vAbgQ91GlHcr8wGgtZr0SrC3e5zMtjddcCzS+W46w1MvRYdQziNJVq+citN4MxSOf5oyv105b1fRlhq/fWEO948zs/dSBjudapz7hdZ7zzTEcj1WjQKfBg4I4vdETpr7+/wec8Avkj6Res2AS8uleMvptxPX7z3ewP/ApxA6J7Kw3rwG4H7CX2Rl2TZym0t86HvrU7hd9HjCi9dfI/Qf7bdkKN6LToZuJD0/YkPEm4irku5n8S89wcC7wSeRWh5M1n9sQ9NwnXcfYR/t8875wZa+29gcy7qtej1wPsz2NWPgaeVyvFdW+37VMJdZ9pT1F3AM0vl+OaU+8lEq4r8acBrgJ0JlQoG2QpuaO3/YuAjwDcH1cLNNdDJPvVadBrwcdIH5Hbg+FI5/m29Fr0C+BjpB7H+ktCaZn79klarzO2hwCsIreCjCXfDa0kXxI2t928iPNK7ALjMOTeR5niTGPgss3otKhOmuqU9fdxBCHHX8fw9+AHwrFI5zm29/lnFKiP7TN289yM3/+wZ65r3/cXq5sZDR+OpnUfZvC4iLsww2oSImK0XyY0p0IwKNKNpRiemo+X3TUUr79hQWH/dPSO7X3vLisNv+Onr15qOoBnK9MZ6LSoR7jwfNvAP6+564DmdblqsFKusJDzheDJQAp5IWMBlmjD8KCacPVYCI8viSdY0H2RZvIkCTaI4phkVmGGU8cI6JqM1xFEhJrRwU633j7Tefz/hMuZbhGfK325U+MOwftahzaut16KDgKuBPYfygfO7FHhJqRwP/dQyn9Z6XwcDpwKnAI8lPHxfzvAqUTUJp+AVhBut6wjXz1c3KgzsexrqhO56LdqL0EHcthr4AH2M0E9nOjS9FbYnEVa+eQmhTvAy8lPaNibcdCwnPPP+JFBrVDIZFLLF0CsJ1GvRzoSH5lmMkevVPwHnDmPgaTvFKg8HXksYkbOy9SePHcdzzYbwGuBtjQrfzWKnJiUs6rVoLfAF4OlD+Lg3lMrxB4bwOfMqVtmNMOL6la3/a9h9dFlpEoZ33Qq8qVEh8ZAoMKydUq9FywnN+IsG9BHTwOmlcvypAe2/o2KVtYSR1q8mdP2ssDiOARkj3Ji8plHhe0l2YFq0pzUAtAr8n4x3PUEYR1fLeL89KVb5c8KNzGwn8GIUE1rAtwHvbVT6G3FjXi2q9YjtXODtGe3yPuDEUjmuZ7S/nhWrFICzCS3dSui8SvUiMQbcDLywUWlfiHEu8+DNqteiTxB66tM6qVSOr8xgP30pVlkHXEHoe1tq1QQ2E56sPLdR4Wu9vCFPtVPuzWg/92W0n54Vq6wiTCx6CksvdBC6g3YErihWKfXyhjwFb0Fq9ct9jrBg3GK6gUhiDXB1scre3TZU8NI7GziOhdtNkrXVwJdaj//aUvBSaF3XvY2leXptZwSYHd7VloKXzstYeBN3hmENcE7rMmReCl5CrS/1Tai1a+cRdHgsquAltz+wm/VB5NgqOnSPKXjJ7cHSLcLYiwJQ7PSiJJOXYUx51rYyhIKX3P3WB7AAtH0ooOAl92PU6nWykTCQdF4KXkKNChsJlRN0nTe/AvCZTi9Kcv+Klg2dzzTwqUalfXUsBS+FRoUfEYYEaenQbU3RZTK/gpfeC1nYde+yNgac3ajw004b5WmyyR+h88H2aKhTFxsVflesciLwZfJfdmzQxgjrony424a5GQi60BWrnECYwLSKpTHyeK4xQo3CU3sZBq9TbUYaFa4mVEf9LUNudY3FhJ/X02PoQC1e5opV1gAfIlQGWOyn3klCXeST+51tpuANSLHKqcB/Eq6jF1sAm4R6LJcAZ7b6NPui4A1Qa6Do2YQawhELP4CzNZHrwBsaleRF17utV5u0m+DWUjk+LOF7h6pei44nFA7vW6kc9zQWr1hlPWFxvXMILeC6JJ9naILwi3MV8PZGhe+n3WG34KVpDneer4Rs3tRr0TnAe5K8t1SO+7p7LVZZDpwE/CXwDELH8zryeRc8ThjG/nPgE8BnGxX6Wt2ok279eNvW++vPUYTZ9Hl3yLA+qFFhitDl8oVilRWEZUxPI6wLPEOYpWY1U22G0CWyAriFUATzkkaFOwfxYd2C9x3g8IT7PoaFEbxDLT60UWEToVjllcUqy4DDCN/10wiFGXcktDrLyX4G23Rr36OEVu1W4FrgG8C3GhX+lPHnbadb8C4jXfByrbVqt0Wtvm00KmwmXLDXgQ/AluvCxxMqhD4e2I9Q1HInwjyPUbafaBQRzlJb/x3CNdoDwB8ItZ9vBW4ilOT9VaPC0O8wu13jHUQYd5ZEE1jfyyIpVuq16HDCP3YSd5fK8S5ZHUurGsFhhEuUpwKPAXYn3AlPEE6Fo4TWL0nH/yThjjQinE4j4E+EDu/vAl8jtHa/S/Fj9Kxbi/cTwm9I15nh8ygARxDKz+ZVmtNsqp+rdXo9HDiRcI23LyFgK9l+gGnaVYvgoWKQW9uz9efJhIUMVxSrW8rRXgJc06hwdwafvZ2OwSuV47heiy4lLH2UxDEs3uBd1e8bilVGgZMJK50fTbirXc1D/w5WCypHwA6t/94FeAFhtaEVxSp3EAZ0fiTL4ty9jE65jHTBy7NDEr4vpo9fqGKVXQlhex0hXLP9eHkuezF7jI8D3gy8uVjlS8B7CRXiU10Xdn1y0VpD9i6SLRUwRbjO6+mheb0W7Ub4x0niT6Vy3PNKQq111zaQrHDiDaVy/JRuGxWrHEh4eH4SIax5DlovmoTLgTsJ9Qw/029BxlldW7xSOd5cr0U1QrmGfi0nXD/0uk7YWaRYA61ei75bKsfX97j5/iSv1tnxNFusshPwbsJ3toLFMwqoQLij3g/4f8Bbi1Ve1ajwjSQ76kWiR0otPZ1uW10br0nxOQDvalUY7cVAru+KVV4G/IrQMZz0DnQhWEs4DV9drHJhsbrlGrEnvX4pV5N8XkGv13mvJP3KP0cS1v7qxSEJP+Me2L7kfrHKymKVTxFG365l8dY+nms14VLitmKVx/f6pp6CVyrHD0BvJUbncUTrOrGtei0aIfkNzFzvbBX17iZpi3d1qRxvs6Rmqw/ua4QxeEuxiM9KQp9jfRAVQZOeblcTet87eQ5hOaUsHELoDmirdTpOGrxtTrOtB/9XEpaGWug3D2lEPFQR9AndNu4neJcnPqTQZ9XJ2Sn2PZ+3t+5a29kLWJ9gv/N1o7yHUPt4KYdua2uBa1tjEdvqOXilctwgPNtLou11Xuux1REJ99vOfoShR+0kbe2+WyrHWx6gF6scROifW+gDPLM02/K9o9NG/d5xJT3dHt3huquf1u7rfWx7Xr0WtRtilDR4X5rz9/eigtvzWQX8TbFK22fZ/QYv6TCn9YSq6Nuo16K9CRfkvbiCsNrhZI/bP4ow6nc+h/S4j7m2XN+1xtMdx+LtLklrGii3e7HfL+17kHj0wnyn29f1cQy+VI7vJCxB1atzWwv2zZWkxbsXuGGrvx+G6qZ0spbQzTKvvioJtAYNXEb7lqSTY4DzZ/9Sr0UPB87o8b2Xl8rxbN/ZewjXVet7eN8uQAV451afuxOhNezXNXO6UdbD8MexzacQz/CUiatm9pv6/oj1sWztN6P7HhSW5d1ektNE0uu8Y+Y8VTiT3vu8zpv9j1I5vo+w/myv3tQK+axD+njv1uY+rcjFOMM1zQc4ecNHmvtO3cwIzVz9KU7/9PZ2x50keF8lWZGa3QhjzmaXDD2rx/ddVirHcycL/zv0PBdgR0J19lmJO47n/P1GjJ9O7Ln55zzvwQ+yvvlHRpnJVWsHjEWhf3NefQevVI4nST7GbvY67yWE4tW9OG+eY5ggLD7cq0q9Fu3e+u8kwbupVI7v2vr/aFSYAL6Jwek2ips8ceLa5vFjn2M5UxTyeYMzQpbBa0l7uu21C+XSUjluN4fzvwlzB3qxirA0KSQ71bYbFPD39H6XnYmVzTFO3Pix5kGbvsNofouRbgI+55xreyOaNHg1kq1ocwxhOfheHyaf1+6FUjmeBt7Sx2efWa9FBxKGQ/Vr3uA1KtxIKEc7lPDtNv1rnr/hfHae+R2jTOexlZu1CXhDpw0SHXyr9/5bCd5aBN7X47aXlMrxzV22uZTeJ+ssIwzh7vdnvo8wzbOdswjzUDf1ud/exTGPn/x684SNF7AinqRAnOfQjQMnOuc6LgOb5gdIero9uMftfLcNSuU4pr+Bo10fXs9jbjfKNhoVxoHjgTsYQPiWNyc5YeyCmUMnr8/zqXXWOHCKc67rwNA0wRvkZO0vlMpxT8+FWyOOB7kid9dJPY0KDxAGClxPhmVpd5r+PadsOD/eY7qR91PrZsKZ4QTnXE83nol/mFI5vp1sSsfOp2trN8ebGdzd5dzns/NqVHiQMDPrHwjzEpI3T3HM4yZvjE/c+DFWxWMUaOatq2RrY4TLrgN6aelmpf0tSjMkvp2LS+W4r/JXpXL8I+CCARzL9+Z2o3TSqBA3KnwAOIDQUo73+4Gj8RTHjX1+5imT1zDKNFE+C/pACNzvCUP8j3XO9fw9Qfri25exbedsFt6W8H3/SHg+k+VqO33PnQVoVGgAJxerHAW8i/Bcd5Qu82Z3nPkTJ2z8dLwq3sAIzbwGbgPhLv49wPnOuUTXtakKM7aGrN9FqOeRhYtK5fiFKY7n/WQ3hB7gqFI5/mbanRSr7EMYEHE6oRtqu4ELe2/6YXzkxBXRKJvjHLZymwmXDt8nzJ6rOefa3nD1InVF0Hot+gQd1iXt0+NL5ThprRbqtWhn4BfQ34ynNu4Hdmn1F2aiVR/5ZYTZdAcAk1HcXHfk+GXNvTffUhhlOk+BmyBcN48BFwFV51xm1/RZBO8U4OIMjuXCUjk+Ne1O6rXorYTJxml9vlSOX5TBfuZVrLIjcFwhnn7eix/8wCkr4vFlUTiFrcXmEdg4oTUeIVSSupBwY/Uz51zmN25ZBG8tYcpfmmurGDg4TWu31fGsIbR6aVfPPqNUjj+e9nh65b3fDSgRKkX9BfBIwnTPqdafAmFQQtL6KrPLAmxu7Wu2CtVdwA+BawjPnn+S9jTai0yKb7davZ1T7OKeUjnOotWcPZ4jmGfEc58uti6l670fIUwb3Ivw1GcvwnySfQhVnlYQblpm/0SEa7EZHrouu5dQ8es2wkTzX7f+/MY51/ddd1ZU9V1M5Lk3XBYxBU9MKHhiQsETEwqemFDwxISCJyYUPDGh4IkJBU9MKHhiQsETEwqemFDwxISCJyYUPDGh4IkJBU9MKHhiQsETE/8fQtsq34HMZLUAAAAASUVORK5CYII=\n", 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\n", 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"text/plain": [ - "
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" + ] }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,10,sg=sg);\n", + "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,119,sg=sg);\n", "display(renderer)" ] }, @@ -761,11 +222,46 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "47e1b56d6a72479db839e75fc5d15a0d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Renderer(camera=PerspectiveCamera(position=(8.0, 5.0, 8.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,20,sg=sg);\n", + "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,43,sg=sg,threshold1=0.25);\n", "display(renderer)" ] }, @@ -778,9 +274,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9120c0d664a043c18e1b2467a15e5548", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Renderer(camera=PerspectiveCamera(position=(-3.0, 6.0, 10.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,30,sg=sg,\n", " camera_position=[-0.3, 0.6, 1.0]);\n", @@ -797,9 +344,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e91706f6ead94b6390adc3bdb9f511cb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Renderer(camera=PerspectiveCamera(position=(-3.0, 6.0, 10.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,30,sg=sg,\n", " camera_position=[-0.3, 0.6, 1.0]);\n", diff --git a/Visualize superimposed neighborhood and filter.ipynb b/Visualize superimposed neighborhood and filter.ipynb index 608ad4c..dbe193f 100644 --- a/Visualize superimposed neighborhood and filter.ipynb +++ b/Visualize superimposed neighborhood and filter.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0d8187ae", + "id": "0b17df01", "metadata": {}, "outputs": [ { @@ -66,7 +66,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "3f3d28c4", + "id": "0bf51963", "metadata": {}, "outputs": [ { @@ -285,7 +285,7 @@ "Grouped 1 examples in 1 groups\n", "Grouping and padding...\n", "Performing prediction...\n", - "1/1 [==============================] - 1s 562ms/step\n", + "1/1 [==============================] - 1s 629ms/step\n", "Ungrouping and unpadding...\n", "prediction done!\n", "Predicting binding sites from pdb structures with ScanNet_interface\n", @@ -432,7 +432,7 @@ "Grouped 1 examples in 1 groups\n", "Grouping and padding...\n", "Performing prediction...\n", - "1/1 [==============================] - 1s 771ms/step\n", + "1/1 [==============================] - 1s 796ms/step\n", "Ungrouping and unpadding...\n", "prediction done!\n", "Predicting binding sites from pdb structures with ScanNet_interface\n", @@ -579,7 +579,7 @@ "Grouped 1 examples in 1 groups\n", "Grouping and padding...\n", "Performing prediction...\n", - "1/1 [==============================] - 1s 852ms/step\n", + "1/1 [==============================] - 1s 975ms/step\n", "Ungrouping and unpadding...\n", "prediction done!\n", "Warning, attention coeffs are flipped\n", @@ -727,7 +727,7 @@ "Grouped 1 examples in 1 groups\n", "Grouping and padding...\n", "Performing prediction...\n", - "1/1 [==============================] - 1s 823ms/step\n", + "1/1 [==============================] - 1s 800ms/step\n", "Ungrouping and unpadding...\n", "prediction done!\n" ] @@ -757,7 +757,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "15b6b8ad", + "id": "fe512064", "metadata": { "scrolled": false }, @@ -874,222 +874,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "e833c12d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[334,\n", - " 335,\n", - " 336,\n", - " 337,\n", - " 338,\n", - " 339,\n", - " 340,\n", - " 341,\n", - " 342,\n", - " 343,\n", - " 344,\n", - " 345,\n", - " 346,\n", - " 347,\n", - " 348,\n", - " 349,\n", - " 350,\n", - " 351,\n", - " 352,\n", - " 353,\n", - " 354,\n", - " 355,\n", - " 356,\n", - " 357,\n", - " 358,\n", - " 359,\n", - " 360,\n", - " 361,\n", - " 362,\n", - " 363,\n", - " 364,\n", - " 365,\n", - " 366,\n", - " 367,\n", - " 368,\n", - " 369,\n", - " 370,\n", - " 371,\n", - " 372,\n", - " 373,\n", - " 374,\n", - " 375,\n", - " 376,\n", - " 377,\n", - " 378,\n", - " 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"execute_result" - } - ], - "source": [ - "resids" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "b143bce5", + "execution_count": 4, + "id": "b570ad87", "metadata": {}, "outputs": [ { @@ -1119,24 +905,24 @@ }, { "cell_type": "code", - "execution_count": 57, - "id": "de44a8ff", + "execution_count": 5, + "id": "bc5d788d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "74 3.5254724 99.53796\n", - "118 2.0385098 99.12466\n", - "60 1.8124382 99.06545\n", - "89 2.1844616 99.065186\n", - "1 2.1504884 99.01279\n" + "22 1.302515 96.10252\n", + "70 0.8990701 94.43268\n", + "36 0.5780388 93.403015\n", + "4 0.7472925 91.83968\n", + "76 1.0113704 90.58128\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1160,7 +946,7 @@ } ], "source": [ - "resnumber = 374\n", + "resnumber = 455\n", "normalized = True\n", "\n", "\n", @@ -1182,8 +968,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "8e847106", + "execution_count": 72, + "id": "2f2f8b95", "metadata": {}, "outputs": [ { @@ -1211,8 +997,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "dc8f9e64", + "execution_count": 7, + "id": "5b2192db", "metadata": {}, "outputs": [], "source": [ @@ -1221,8 +1007,8 @@ }, { "cell_type": "code", - "execution_count": 61, - "id": "b0f8afdb", + "execution_count": 73, + "id": "0e0437f4", "metadata": { "scrolled": false }, @@ -1230,7 +1016,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5318ec71b9844b428a980509c518da01", + "model_id": "2c86653ae12a4f9db540c8776ba43ab5", "version_major": 2, "version_minor": 0 }, @@ -1241,6 +1027,60 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ @@ -1256,21 +1096,184 @@ "\n", "\n", "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,\n", - " top_filters[2],\n", + " top_filters[0],\n", " sg=sg,\n", " list_additional_objects=list_objects,\n", " show_asa=True,\n", - " scale=2.0);\n", + " scale=2.0,\n", + " threshold1=0.33);\n", + "display(renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "e9076ac8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "importlib.reload(show_3d_neighborhoods)\n", + "importlib.reload(show_3d_filters)\n", + "from network import neighborhoods\n", + "importlib.reload(neighborhoods)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "7f9a5500", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n" + ] + } + ], + "source": [ + "\n", + "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", + " pdb = pdb,\n", + " model = model,\n", + " chain = chain,\n", + " resnumber = 468,\n", + " atom = 'CG1',\n", + " assembly=False,\n", + " biounit=False,\n", + ")\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "6b31c2ac", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1aef14ff7b0940a586a7a301377e6b4f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Renderer(camera=PerspectiveCamera(position=(8.0, 5.0, 8.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False,\n", + " radius_scale = 0.15)\n", + "\n", + "\n", + "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,\n", + " 46,\n", + " y_offset = 0.25,\n", + " sg=sg,\n", + " list_additional_objects=list_objects,\n", + " threshold1=0.33);\n", "display(renderer)" ] }, { "cell_type": "code", "execution_count": null, - "id": "45ec0277", + "id": "f72269a8", + "metadata": {}, + "outputs": [], + "source": [ + "# from utilities import wrappers\n", + "# model = wrappers.load_model('models/ScanNet_PAI_noMSA_0')\n", + "\n", + "\n", + "\n", + "w1,w2 = model.model.get_layer('SCAN_filter_input_aa').get_weights()\n", + "w3 = model.model.get_layer('attention_pooling_features_atom').get_weights()[0]\n", + "\n", + "plt.matshow(w1[:,:,top_filters[0]]); plt.colorbar()\n", + "plt.show()\n", + "\n", + "\n", + "index = np.argmax(w1[0,:,top_filters[0]]) - 32\n", + "plt.plot(w3[:,index]); plt.show()\n", + "\n", + "\n", + "print( np.argsort( np.abs( w3[:,index]))[::-1][:5] )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f5679bc", "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebaae6d1", + "metadata": {}, + "outputs": [], + "source": [ + "plt.matshow(filter_specificities['aa_specificity']['value'].T)" + ] } ], "metadata": { diff --git a/datasets/.DS_Store b/datasets/.DS_Store index 2f5898b3625152fc6ddab8a5c9873e820a1a7552..c8c2f9ec7fbf11ff3ae04e6b5788da52e9cc970f 100644 GIT binary patch delta 139 zcmZoMXfc=|#>B!ku~2NHo+2ar#(>?7ixZfc7}+=TFj+88)@SM$wlvmJFt(`GQK&XF zF)`3lFgG)te38jii-jQo2%Q*$Q;L&wlJfI&7#SEC;DTkrMR_^-dFeng#?67uhZr}r ebMSKjtpaksGf(ChapVB1X97wyY>p6F!wdi;?;p+p delta 76 zcmZoMXfc=|#>B)qu~2NHo+2ab#(>?7jI5h^SS%PhQi_vvlJfI&HVblyu}o}Ox|yAW dp982C$p6kfnP0?_g8>K_85meLM~JLp1_1lM5={UA diff --git a/visualizations/show_3d_filters.py b/visualizations/show_3d_filters.py index 7edc5cf..f1217ce 100644 --- a/visualizations/show_3d_filters.py +++ b/visualizations/show_3d_filters.py @@ -7,7 +7,7 @@ import os from keras.models import Model import matplotlib -from preprocessing import PDBio,PDB_processing,pipelines +from preprocessing import PDBio,PDB_processing,pipelines,protein_chemistry from network import neighborhoods import numpy as np @@ -280,70 +280,7 @@ def calculate_filter_specificities(model_name, -def get_neighborhood( - pdb = '1a3x', - model = 0, - chain = 'A', - resnumber = 1, - assembly=False, - biounit=True, - Kmax = 64 - -): - filename,_ = PDBio.getPDB(pdb,biounit=biounit) - if assembly == True: - chain_ids = 'all' - elif isinstance(assembly,list): - chain_ids = assembly - else: - chain_ids = [(model,chain)] - struct, chains = PDBio.load_chains(file=filename,chain_ids=chain_ids) - pipeline = pipelines.ScanNetPipeline(aa_features='sequence', - atom_features='type' - ) - [aa_triplets, aa_attributes,aa_indices,aa_clouds, - atom_triplets, atom_attributes, atom_indices, atom_clouds],_ = pipeline.process_example(chains) - - aa_frames = neighborhoods.get_Frames( - [[aa_triplets],[aa_clouds]], order='2') - - - _, atom_types = neighborhoods.get_LocalNeighborhood([ [aa_frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_attributes[atom_triplets[:,0]]], - ) - atom_positions, atom_triplets = neighborhoods.get_LocalNeighborhood([ [aa_frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_triplets], - ) - - atom_types = atom_types.astype(np.int) - atom_triplets = atom_triplets.astype(np.int) - - - - - - resids = PDB_processing.get_PDB_indices(chains,return_chain=True,return_model=True) - - index = np.nonzero( (resids[:,0].astype(np.int) == model) & (resids[:,1]==chain) & (resids[:,2].astype(np.int)==resnumber) )[0][0] - - atom_positions = atom_positions[0][index] - atom_types = atom_types[0][index] - atom_triplets = atom_triplets[0][index] - atom_index = list(atom_triplets[:,0]) - atom_bonds = np.zeros([Kmax,Kmax],dtype=np.bool) - - for triplet in atom_triplets: - if triplet[1] in atom_index: - atom_bonds[atom_index.index(triplet[0]),atom_index.index(triplet[1])] = 1 - if triplet[2] in atom_index: - atom_bonds[atom_index.index(triplet[0]),atom_index.index(triplet[2])] = 1 - atom_bonds += atom_bonds.T - atom_bonds = list(zip(*np.nonzero(atom_bonds) ) ) - atom_bonds = [(i,j) for i,j in atom_bonds if j>i] - return atom_positions,atom_types-1,atom_bonds - - - - -def plot_atomic_filter(filter_specificities, index, threshold1=0.33, threshold2=0.25, sg=None,camera_position=None): +def plot_atomic_filter(filter_specificities, index, threshold1=0.33, threshold2=0.25,y_offset=0,list_additional_objects = [], sg=None,camera_position=None): if camera_position is None: camera_position = [0.8, 0.5, 0.8] @@ -395,7 +332,8 @@ def plot_atomic_filter(filter_specificities, index, threshold1=0.33, threshold2= show_frame=True, fs=1., scale=2.0, - offset=[0, 0, 0], + offset=[0, y_offset, 0], + list_additional_objects=list_additional_objects, camera_position=camera_position, key_light_position=[0.5, 1, 0], maxi=5) diff --git a/visualizations/show_3d_neighborhoods.py b/visualizations/show_3d_neighborhoods.py index e8d24d5..9af6fb2 100644 --- a/visualizations/show_3d_neighborhoods.py +++ b/visualizations/show_3d_neighborhoods.py @@ -1,6 +1,6 @@ import pythreejs import numpy as np -from preprocessing import PDBio,PDB_processing,pipelines +from preprocessing import PDBio,PDB_processing,pipelines,protein_chemistry from network import neighborhoods def rgb_to_hex(rgb): @@ -37,7 +37,7 @@ def show_atoms( atom_bonds, render=True, radius_scale=0.2, - stick_radius=0.15, + stick_radius=0.75, stick_height=0.8, camera_position=[0.8, 0.5, 0.8], key_light_position=[0.5, 1, 0.0] @@ -78,8 +78,8 @@ def show_atoms( children = [] sphere_geometry = [pythreejs.SphereGeometry(radius=radius_scale * radius) for radius in VanDerWaalsRadii] - stick_geometry = pythreejs.CylinderGeometry(radiusTop=stick_radius, - radiusBottom=stick_radius, + stick_geometry = pythreejs.CylinderGeometry(radiusTop=radius_scale *stick_radius, + radiusBottom=radius_scale *stick_radius, height=stick_height) list_spheres = [ @@ -131,12 +131,21 @@ def get_neighborhood( model = 0, chain = 'A', resnumber = 1, + atom = None, resindex = None, + atomindex= None, assembly=False, biounit=True, - Kmax = 64 + Kmax = None ): + if Kmax is None: + if (atom is not None) | (atomindex is not None): + Kmax = 32 + else: + Kmax = 16*9 + + filename,_ = PDBio.getPDB(pdb,biounit=biounit) if assembly == True: chain_ids = 'all' @@ -146,37 +155,64 @@ def get_neighborhood( chain_ids = [(model,chain)] struct, chains = PDBio.load_chains(file=filename,chain_ids=chain_ids) pipeline = pipelines.ScanNetPipeline(aa_features='sequence', - atom_features='type' + atom_features='id' ) + [aa_triplets, aa_attributes,aa_indices,aa_clouds, - atom_triplets, atom_attributes, atom_indices, atom_clouds],_ = pipeline.process_example(chains) - - aa_frames = neighborhoods.get_Frames( - [[aa_triplets],[aa_clouds]], order='2') + atom_triplets, atom_ids, atom_indices, atom_clouds],_ = pipeline.process_example(chains) + atom_ids -=1 + atom_attributes = protein_chemistry.index_to_type[atom_ids]+1 - _, atom_types = neighborhoods.get_LocalNeighborhood([ [aa_frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_attributes[atom_triplets[:,0]]], + resids = PDB_processing.get_PDB_indices(chains,return_chain=True,return_model=True) + + if (atom is not None) | (atomindex is not None): + if (atomindex is not None): + index = atomindex + else: + try: + index = np.nonzero( + (resids[atom_indices[:,0], 0].astype(np.int) == model) & + (resids[atom_indices[:,0], 1] == chain) & + (resids[atom_indices[:,0], 2].astype(np.int) == resnumber) & + (atom_ids == protein_chemistry.atom_to_index[atom]) + )[0][0] + except: + raise ValueError('Atom #%s/%s:%s@%s not found' % (model,chain,resnumber,atom) ) + else: + if resindex is not None: + index = resindex + else: + try: + index = np.nonzero( (resids[:,0].astype(np.int) == model) & + (resids[:,1]==chain) & + (resids[:,2].astype(np.int)==resnumber) + )[0][0] + except: + raise ValueError('Residue #%s/%s:%s not found' % (model, chain, resnumber)) + + + if (atom is not None) | (atomindex is not None): + frames = neighborhoods.get_Frames( + [[atom_triplets[index:index+1]],[atom_clouds]], order='2') + else: + frames = neighborhoods.get_Frames( + [[aa_triplets[index:index+1]],[aa_clouds]], order='2') + + + _, atom_types = neighborhoods.get_LocalNeighborhood([ [frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_attributes], ) - atom_positions, atom_triplets = neighborhoods.get_LocalNeighborhood([ [aa_frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_triplets], + atom_positions, atom_triplets = neighborhoods.get_LocalNeighborhood([ [frames[0]], [atom_clouds[atom_triplets[:,0]]] ],{'self_neighborhood':False,'Kmax': Kmax},attributes= [atom_triplets], ) atom_types = atom_types.astype(np.int) atom_triplets = atom_triplets.astype(np.int) + atom_positions = atom_positions[0][0] + atom_types = atom_types[0][0] -1 # Inside the code, atom_type = 0 corresponds to virtual atom or empty placeholder. + atom_triplets = atom_triplets[0][0] - - - resids = PDB_processing.get_PDB_indices(chains,return_chain=True,return_model=True) - - if resindex is not None: - index = resindex - else: - index = np.nonzero((resids[:, 0].astype(np.int) == model) & (resids[:, 1] == chain) & ( - resids[:, 2].astype(np.int) == resnumber))[0][0] - atom_positions = atom_positions[0][index] - atom_types = atom_types[0][index] -1 # Inside the code, atom_type = 0 corresponds to virtual atom or empty placeholder. - atom_triplets = atom_triplets[0][index] atom_index = list(atom_triplets[:,0]) atom_bonds = np.zeros([Kmax,Kmax],dtype=np.bool)