{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras\n", "from keras.models import Sequential, Model, load_model\n", "\n", "from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda\n", "from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute\n", "from keras.layers import Concatenate, Reshape, Softmax, Conv2DTranspose, Embedding, Multiply\n", "from keras.callbacks import ModelCheckpoint, EarlyStopping, Callback\n", "from keras import regularizers\n", "from keras import backend as K\n", "import keras.losses\n", "\n", "import tensorflow as tf\n", "from tensorflow.python.framework import ops\n", "\n", "import isolearn.keras as iso\n", "\n", "import numpy as np\n", "\n", "import tensorflow as tf\n", "import logging\n", "logging.getLogger('tensorflow').setLevel(logging.ERROR)\n", "\n", "import pandas as pd\n", "\n", "import os\n", "import pickle\n", "import numpy as np\n", "\n", "import scipy.sparse as sp\n", "import scipy.io as spio\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import isolearn.io as isoio\n", "import isolearn.keras as isol\n", "\n", "from genesis.visualization import *\n", "from genesis.generator import *\n", "from genesis.predictor import *\n", "from genesis.optimizer import *\n", "import sklearn\n", "from sklearn.decomposition import PCA\n", "from sklearn.manifold import TSNE\n", "\n", "from scipy.stats import pearsonr\n", "\n", "import seaborn as sns\n", "\n", "from matplotlib import colors\n", "\n", "import editdistance\n", "\n", "def subselect_list(li, ixs) :\n", " return [\n", " li[ixs[k]] for k in range(len(ixs))\n", " ]\n", "\n", "class IdentityEncoder(iso.SequenceEncoder) :\n", " \n", " def __init__(self, seq_len, channel_map) :\n", " super(IdentityEncoder, self).__init__('identity', (seq_len, len(channel_map)))\n", " \n", " self.seq_len = seq_len\n", " self.n_channels = len(channel_map)\n", " self.encode_map = channel_map\n", " self.decode_map = {\n", " nt: ix for ix, nt in self.encode_map.items()\n", " }\n", " \n", " def encode(self, seq) :\n", " encoding = np.zeros((self.seq_len, self.n_channels))\n", " \n", " for i in range(len(seq)) :\n", " if seq[i] in self.encode_map :\n", " channel_ix = self.encode_map[seq[i]]\n", " encoding[i, channel_ix] = 1.\n", "\n", " return encoding\n", " \n", " def encode_inplace(self, seq, encoding) :\n", " for i in range(len(seq)) :\n", " if seq[i] in self.encode_map :\n", " channel_ix = self.encode_map[seq[i]]\n", " encoding[i, channel_ix] = 1.\n", " \n", " def encode_inplace_sparse(self, seq, encoding_mat, row_index) :\n", " raise NotImplementError()\n", " \n", " def decode(self, encoding) :\n", " seq = ''\n", " \n", " for pos in range(0, encoding.shape[0]) :\n", " argmax_nt = np.argmax(encoding[pos, :])\n", " max_nt = np.max(encoding[pos, :])\n", " seq += self.decode_map[argmax_nt]\n", "\n", " return seq\n", " \n", " def decode_sparse(self, encoding_mat, row_index) :\n", " raise NotImplementError()\n", "\n", "#Plot joint histograms\n", "def plot_joint_histo(measurements, labels, x_label, y_label, colors=None, n_bins=50, figsize=(6, 4), legend_outside=False, save_fig=False, fig_name=\"default_1\", fig_dpi=150, min_val=None, max_val=None, max_y_val=None) :\n", " \n", " min_hist_val = np.min(measurements[0])\n", " max_hist_val = np.max(measurements[0])\n", " for i in range(1, len(measurements)) :\n", " min_hist_val = min(min_hist_val, np.min(measurements[i]))\n", " max_hist_val = max(max_hist_val, np.max(measurements[i]))\n", " \n", " if min_val is not None :\n", " min_hist_val = min_val\n", " if max_val is not None :\n", " max_hist_val = max_val\n", "\n", " hists = []\n", " bin_edges = []\n", " means = []\n", " for i in range(len(measurements)) :\n", " hist, b_edges = np.histogram(measurements[i], range=(min_hist_val, max_hist_val), bins=n_bins, density=True)\n", " \n", " hists.append(hist)\n", " bin_edges.append(b_edges)\n", " means.append(np.mean(measurements[i]))\n", " \n", " bin_width = bin_edges[0][1] - bin_edges[0][0]\n", "\n", "\n", " f = plt.figure(figsize=figsize)\n", "\n", " for i in range(len(measurements)) :\n", " if colors is not None :\n", " plt.bar(bin_edges[i][1:] - bin_width/2., hists[i], width=bin_width, linewidth=2, edgecolor='black', color=colors[i], label=labels[i])\n", " else :\n", " plt.bar(bin_edges[i][1:] - bin_width/2., hists[i], width=bin_width, linewidth=2, edgecolor='black', label=labels[i])\n", " \n", " plt.xticks(fontsize=14)\n", " plt.yticks(fontsize=14)\n", " \n", " plt.xlim(min_hist_val, max_hist_val)\n", " if max_y_val is not None :\n", " plt.ylim(0, max_y_val)\n", "\n", " plt.xlabel(x_label, fontsize=14)\n", " plt.ylabel(y_label, fontsize=14)\n", "\n", " if colors is not None :\n", " for i in range(len(measurements)) :\n", " plt.axvline(x=means[i], linewidth=2, color=colors[i], linestyle=\"--\")\n", "\n", " if not legend_outside :\n", " plt.legend(fontsize=14, loc='upper left')\n", " else :\n", " plt.legend(fontsize=14, bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", " \n", " plt.tight_layout()\n", " \n", " if save_fig :\n", " plt.savefig(fig_name + \".eps\")\n", " plt.savefig(fig_name + \".svg\")\n", " plt.savefig(fig_name + \".png\", dpi=fig_dpi, transparent=True)\n", " \n", " plt.show()\n", "\n", "#Plot join histograms\n", "def plot_joint_cmp(measurements, labels, y_label, plot_type='violin', colors=None, figsize=(6, 4), legend_outside=False, save_fig=False, fig_name=\"default_1\", fig_dpi=150, min_y_val=None, max_y_val=None) :\n", " \n", " f = plt.figure(figsize=figsize)\n", "\n", " sns_g = None\n", " if colors is not None :\n", " if plot_type == 'violin' :\n", " sns_g = sns.violinplot(data=measurements, palette=colors, scale='width') #, x=labels\n", " elif plot_type == 'strip' :\n", " sns_g = sns.stripplot(data=measurements, palette=colors, alpha=0.1, jitter=0.3, linewidth=2, edgecolor='black') #, x=labels\n", " for i in range(len(measurements)) :\n", " plt.plot(x=[i, i+1], y=[np.median(measurements[i]), np.median(measurements[i])], linewidth=2, color=colors[i], linestyle=\"--\")\n", " elif plot_type == 'bar' :\n", " for i in range(len(measurements)) :\n", " plt.bar([i], [np.percentile(measurements[i], 95)], width=0.4, color=colors[i], label=str(i) + \") \" + labels[i], linewidth=2, edgecolor='black')\n", " plt.bar([i+0.2], [np.percentile(measurements[i], 80)], width=0.4, color=colors[i], linewidth=2, edgecolor='black')\n", " plt.bar([i+0.4], [np.percentile(measurements[i], 50)], width=0.4, color=colors[i], linewidth=2, edgecolor='black')\n", " else :\n", " if plot_type == 'violin' :\n", " sns_g = sns.violinplot(data=measurements, scale='width') #, x=labels\n", " elif plot_type == 'strip' :\n", " sns_g = sns.stripplot(data=measurements, alpha=0.1, jitter=0.3, linewidth=2, edgecolor='black') #, x=labels\n", " elif plot_type == 'bar' :\n", " for i in range(len(measurements)) :\n", " plt.bar([i], [np.percentile(measurements[i], 95)], width=0.25, label=str(i) + \") \" + labels[i], linewidth=2, edgecolor='black')\n", " plt.bar([i+0.125], [np.percentile(measurements[i], 80)], width=0.25, linewidth=2, edgecolor='black')\n", " plt.bar([i+0.25], [np.percentile(measurements[i], 50)], width=0.25, linewidth=2, edgecolor='black')\n", " \n", " plt.xticks(np.arange(len(labels)), fontsize=14)\n", " plt.yticks(fontsize=14)\n", " \n", " #plt.xlim(min_hist_val, max_hist_val)\n", " if min_y_val is not None and max_y_val is not None :\n", " plt.ylim(min_y_val, max_y_val)\n", "\n", " plt.ylabel(y_label, fontsize=14)\n", " \n", " if plot_type not in ['violin', 'strip'] :\n", " if not legend_outside :\n", " plt.legend(fontsize=14, loc='upper left')\n", " else :\n", " plt.legend(fontsize=14, bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", " else :\n", " if not legend_outside :\n", " f.get_axes()[0].legend(fontsize=14, loc=\"upper left\", labels=[str(label_i) + \") \" + label for label_i, label in enumerate(labels)])\n", " else :\n", " f.get_axes()[0].legend(fontsize=14, bbox_to_anchor=(1.04,1), loc=\"upper left\", labels=[str(label_i) + \") \" + label for label_i, label in enumerate(labels)])\n", " \n", " plt.tight_layout()\n", " \n", " if save_fig :\n", " plt.savefig(fig_name + \".eps\")\n", " plt.savefig(fig_name + \".svg\")\n", " plt.savefig(fig_name + \".png\", dpi=fig_dpi, transparent=True)\n", " \n", " plt.show()\n", "\n", "#Load generated data from models to be evaluated\n", "\n", "def load_sequences(file_path, split_on_tab=True, seq_template=None, max_n_sequences=1e6, select_best_fitness=False, predictor=None, batch_size=32) :\n", " seqs = []\n", " \n", " with open(file_path, \"rt\") as f :\n", " for l in f.readlines() :\n", " l_strip = l.strip()\n", " seq = l_strip\n", " if split_on_tab :\n", " seq = l_strip.split(\"\\t\")[0]\n", " \n", " if seq_template is not None :\n", " seq = ''.join([\n", " seq_template[j] if seq_template[j] != 'N' else seq[j]\n", " for j in range(len(seq))\n", " ])\n", "\n", " seqs.append(seq)\n", " \n", " if select_best_fitness and predictor is not None :\n", " onehots = np.expand_dims(np.concatenate([\n", " np.expand_dims(acgt_encoder.encode(seq), axis=0) for seq in seqs\n", " ], axis=0), axis=-1)\n", "\n", " #Predict fitness\n", " score_pred = predictor.predict(x=[onehots[..., 0]], batch_size=batch_size)\n", " score_pred = np.ravel(score_pred[:, 5])\n", " \n", " sort_index = np.argsort(score_pred)[::-1]\n", " seqs = [\n", " seqs[sort_index[i]] for i in range(len(seqs))\n", " ]\n", " \n", " return seqs[:max_n_sequences]\n", "\n", "#Evaluate metrics for each model\n", "\n", "def compute_metrics(seqs, n_seqs_to_test=960, batch_size=64, opt_len=90) :\n", " \n", " n_seqs_to_test = min(len(seqs), n_seqs_to_test)\n", " \n", " onehots = np.expand_dims(np.concatenate([\n", " np.expand_dims(acgt_encoder.encode(seq), axis=0) for seq in seqs\n", " ], axis=0), axis=1)\n", "\n", " #Predict fitness\n", " score_pred = saved_predictor.predict(x=[onehots[:n_seqs_to_test]], batch_size=batch_size)\n", " score_pred = np.ravel(score_pred[:, 0])\n", " \n", " return score_pred\n", "\n", "from keras.backend.tensorflow_backend import set_session\n", "\n", "def contain_tf_gpu_mem_usage() :\n", " config = tf.ConfigProto()\n", " config.gpu_options.allow_growth = True\n", " sess = tf.Session(config=config)\n", " set_session(sess)\n", "\n", "contain_tf_gpu_mem_usage()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "\n", "sequence_template = 'N' * 1000\n", "\n", "problem_prefix = \"dragonn_genesis_max_spi1\"\n", "\n", "n_seqs_to_test = 4000\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#Specfiy file path to pre-trained predictor network\n", "\n", "saved_predictor_model_path = \"../../../seqprop/examples/dragonn/SPI1.classification.model.hdf5\"\n", "\n", "def _dummy_min_pred(y_true, y_pred) :\n", " return y_pred\n", "\n", "saved_predictor = load_model(saved_predictor_model_path, custom_objects={\n", " 'ambig_binary_crossentropy' : _dummy_min_pred,\n", " 'precision' : _dummy_min_pred,\n", " 'recall' : _dummy_min_pred,\n", " 'specificity' : _dummy_min_pred,\n", " 'fpr' : _dummy_min_pred,\n", " 'fnr' : _dummy_min_pred,\n", " 'fdr' : _dummy_min_pred,\n", " 'f1' : _dummy_min_pred\n", "})\n", "\n", "saved_predictor = Model(\n", " inputs=saved_predictor.inputs,\n", " outputs = [saved_predictor.get_layer('dense_2').output]\n", ")\n", "\n", "saved_predictor.compile(\n", " loss='mse',\n", " optimizer=keras.optimizers.SGD(lr=0.1)\n", ")\n", "\n", "\n", "acgt_encoder = IdentityEncoder(1000, {'A':0, 'C':1, 'G':2, 'T':3})\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#Trajectory comparison configuration\n", "\n", "traj_dirs = [\n", " \"samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/\",\n", " \n", " \"samples/genesis_dragonn_max_spi1_25000_updates_similarity_margin_02_earthmover_weight_01_target_700_fixed/\",\n", " \"samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/\"\n", "]\n", "\n", "traj_file_funcs = [\n", " lambda i: \"intermediate_iter_\" + str((i+1) * 100) + \".txt\",\n", " \n", " lambda i: \"intermediate_epoch_\" + str(i) + \"_960_sequences.txt\" if i < 10 else \"intermediate_epoch_\" + str((i-9)*10) + \"_960_sequences.txt\",\n", " lambda i: \"intermediate_epoch_\" + str(i) + \"_960_sequences.txt\" if i < 10 else \"intermediate_epoch_\" + str((i-9)*10) + \"_960_sequences.txt\"\n", "]\n", "\n", "traj_scale_generator_touch_funcs = [\n", " lambda i: (i+1) * 100,#4096,\n", " \n", " lambda i: (i+1) * 100 * 64 if i < 11 else (i-10)*10 * 100 * 64,\n", " lambda i: (i+1) * 100 * 64 if i < 11 else (i-10)*10 * 100 * 64\n", "]\n", "\n", "traj_names = [\n", " \"Simulated Annealing (10000 iters)\",\n", " \n", " \"DEN (seq margin 0.2)\",\n", " \"DEN (seq/lat margin 0.2/0.9)\"\n", "]\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing 'samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/intermediate_iter_100.txt'...\n", "Processing 'samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/intermediate_iter_200.txt'...\n", "Processing 'samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/intermediate_iter_300.txt'...\n", "Processing 'samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/intermediate_iter_400.txt'...\n", "Processing 'samples/basinhopping_dragonn_max_spi1_10000_iters_dense_score/intermediate_iter_500.txt'...\n", "Processing 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'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_800_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_810_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_820_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_830_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_840_960_sequences.txt'...\n", "Processing 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'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_900_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_910_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_920_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_930_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_940_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_950_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_960_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_970_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_980_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_990_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1000_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1010_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1020_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1030_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1040_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1050_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1060_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1070_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1080_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1090_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1100_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1110_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1120_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1130_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1140_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1150_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1160_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1170_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1180_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1190_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1200_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1210_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1220_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1230_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1240_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1250_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1260_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1270_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1280_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1290_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1300_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1310_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1320_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1330_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1340_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1350_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1360_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1370_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1380_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1390_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1400_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1410_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1420_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1430_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1440_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1450_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1460_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1470_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1480_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1490_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1500_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1510_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1520_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1530_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1540_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1550_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1560_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1570_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1580_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1590_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1600_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1610_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1620_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1630_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1640_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1650_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1660_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1670_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1680_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1690_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1700_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1710_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1720_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1730_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1740_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1750_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1760_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1770_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1780_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1790_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1800_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1810_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1820_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1830_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1840_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1850_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1860_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1870_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1880_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1890_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1900_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1910_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1920_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1930_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1940_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1950_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1960_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1970_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1980_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_1990_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2000_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2010_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2020_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2030_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2040_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2050_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2060_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2070_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2080_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2090_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2100_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2110_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2120_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2130_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2140_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2150_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2160_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2170_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2180_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2190_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2200_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2210_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2220_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2230_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2240_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2250_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2260_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2270_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2280_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2290_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2300_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2310_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2320_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2330_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2340_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2350_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2360_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2370_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2380_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2390_960_sequences.txt'...\n", "Processing 'samples/genesis_dragonn_max_spi1_25000_updates_similarity_seq_margin_02_lat_margin_09_earthmover_weight_01_target_700/intermediate_epoch_2400_960_sequences.txt'...\n" ] } ], "source": [ "#Load and predict sequence trajectory data\n", "\n", "def load_and_aggregate_score(file_path, agg_mode='mean', perc=50, split_on_tab=True, seq_template=None, predictor=None, batch_size=32, max_n_sequences=960) :\n", " seqs = []\n", " \n", " print(\"Processing '\" + str(file_path) + \"'...\")\n", " \n", " try :\n", " with open(file_path, \"rt\") as f :\n", " for l in f.readlines() :\n", " l_strip = l.strip()\n", " seq = l_strip\n", " if split_on_tab :\n", " seq = l_strip.split(\"\\t\")[0]\n", "\n", " if seq_template is not None :\n", " seq = ''.join([\n", " seq_template[j] if seq_template[j] != 'N' else seq[j]\n", " for j in range(len(seq))\n", " ])\n", "\n", " seqs.append(seq)\n", " \n", " if len(seqs) > max_n_sequences :\n", " seqs = seqs[:max_n_sequences]\n", " \n", " score_pred = compute_metrics(\n", " seqs,\n", " n_seqs_to_test=len(seqs),\n", " batch_size=batch_size,\n", " opt_len=np.sum([1 if seq_template[j] == 'N' else 0 for j in range(len(seq_template))])\n", " )\n", " \n", " if agg_mode == \"mean\" :\n", " return np.mean(score_pred), score_pred\n", " elif agg_mode == \"perc\" :\n", " return np.percentile(score_pred, perc), score_pred\n", " else :\n", " return np.mean(score_pred), score_pred\n", " \n", " except FileNotFoundError :\n", " return np.nan, np.zeros(max_n_sequences)\n", "\n", "max_n_files = 250\n", "\n", "traj_ys = [\n", " [\n", " load_and_aggregate_score(\n", " traj_dirs[model_i] + traj_file_funcs[model_i](file_i),\n", " agg_mode='perc',\n", " seq_template=sequence_template,\n", " predictor=saved_predictor,\n", " batch_size=32,\n", " max_n_sequences=512\n", " )\n", " for file_i in range(max_n_files)\n", " ]\n", " for model_i in range(len(traj_dirs))\n", "]\n", "\n", "traj_gen_xs = [\n", " [\n", " traj_scale_generator_touch_funcs[model_i](file_i)\n", " for file_i in range(max_n_files)\n", " ]\n", " for model_i in range(len(traj_dirs))\n", "]\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "#Clean up trajectories and convert to numpy arrays\n", "\n", "traj_raw = []\n", "\n", "for model_i in range(len(traj_dirs)) :\n", " #traj_ys[model_i] = np.array(list(zip(*traj_ys[model_i])))\n", " traj_ys[model_i] = list(zip(*traj_ys[model_i]))\n", " traj_raw.append(np.array(traj_ys[model_i][1]))\n", " traj_ys[model_i] = np.array(traj_ys[model_i][:1])\n", " \n", " traj_gen_xs[model_i] = np.array(traj_gen_xs[model_i])\n", " \n", " isnan_index = np.nonzero(np.isnan(traj_ys[model_i][0]))[0]\n", " \n", " first_isnan_ix = None\n", " if len(isnan_index) > 0 :\n", " first_isnan_ix = isnan_index[0]\n", " \n", " if first_isnan_ix is not None :\n", " traj_ys[model_i] = traj_ys[model_i][:, :first_isnan_ix]\n", " traj_gen_xs[model_i] = traj_gen_xs[model_i][:first_isnan_ix]\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#Plot overlapping training trajectories\n", "\n", "def plot_trajectories(traj_indices, traj_names, iteration_scales, iteration_constants, add_zeros, iterations, measures, model_names, measure_ix, x_label, y_label, colors=None, figsize=(6, 4), legend_outside=False, save_fig=False, fig_name=\"default_1\", fig_dpi=150, min_x_val=0, max_x_val=None, min_y_val=None, max_y_val=None, log10_scale=False) :\n", " \n", " f = plt.figure(figsize=figsize)\n", " \n", " max_iter_val = 0\n", " \n", " ls = []\n", " for i, [model_ix, traj_name] in enumerate(zip(traj_indices, traj_names)) :\n", " #for model_ix, [iters, all_meas] in enumerate(zip(iterations, measures)) :\n", " \n", " iters, all_meas = iterations[model_ix], measures[model_ix]\n", " \n", " meas = np.zeros(all_meas[measure_ix, :].shape)\n", " meas[:] = all_meas[measure_ix, :]\n", " \n", " iters_copy = np.zeros(iters.shape)\n", " iters_copy[:] = iters[:]\n", " \n", " if add_zeros[i] is not None :\n", " iters_copy = np.concatenate([np.array([0]), iters_copy], axis=0)\n", " meas = np.concatenate([np.array([add_zeros[i]]), meas], axis=0)\n", " \n", " iters_copy[1:] = iters_copy[1:] * iteration_scales[i] + iteration_constants[i]\n", " \n", " if log10_scale :\n", " iters_copy[1:] = np.log10(iters_copy[1:])\n", " \n", " max_iter_val = max(max_iter_val, np.max(iters_copy))\n", " \n", " l1 = None\n", " if colors is not None :\n", " l1 = plt.plot(iters_copy, meas, color=colors[model_ix], linewidth=2, label=traj_name)\n", " else :\n", " l1 = plt.plot(iters_copy, meas, linewidth=2, label=traj_name)\n", " \n", " ls.append(l1[0])\n", "\n", " if log10_scale :\n", " plt.xticks(np.arange(int(max_iter_val) + 1), 10**np.arange(int(max_iter_val) + 1), fontsize=14, rotation=45)\n", " else :\n", " plt.xticks(fontsize=14, rotation=45)\n", " plt.yticks(fontsize=14)\n", " \n", " if max_x_val is not None :\n", " plt.xlim(min_x_val, max_x_val)\n", " else :\n", " plt.xlim(min_x_val, max_iter_val)\n", " \n", " if min_y_val is not None and max_y_val is not None :\n", " plt.ylim(min_y_val, max_y_val)\n", "\n", " plt.xlabel(x_label, fontsize=14)\n", " plt.ylabel(y_label, fontsize=14)\n", " \n", " if not legend_outside :\n", " plt.legend(handles=ls, fontsize=14, loc='upper left')\n", " else :\n", " plt.legend(handles=ls, fontsize=14, bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", " \n", " plt.tight_layout()\n", " \n", " if save_fig :\n", " plt.savefig(fig_name + \".eps\")\n", " plt.savefig(fig_name + \".svg\")\n", " plt.savefig(fig_name + \".png\", dpi=fig_dpi, transparent=True)\n", " \n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot trajectory data\n", "\n", "experiment_suffix = \"_traj_comparisons_4_target_700_seq_penalty\"\n", "\n", "model_colors = ['indigo', 'black', 'dimgrey']\n", "\n", "figsize = (12, 4)\n", "\n", "#Generator time scale\n", "\n", "plot_trajectories(\n", " [0, 0, 0, 1, 1, 1],\n", " [\n", " \"Simulated Annealing (10000 iters) - 1,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 100,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 10,000,000 Seqs\",\n", " \"DEN Earthm - 1,000 Seqs\",\n", " \"DEN Earthm - 100,000 Seqs\",\n", " \"DEN Earthm - 10,000,000 Seqs\",\n", " ],\n", " [1000.0, 100000.0, 10000000.0, 1.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0, 1000.0, 100000.0, 10000000.0],\n", " [0, 0, 0, 0, 0, 0],\n", " traj_gen_xs,\n", " traj_ys,\n", " traj_names,\n", " 0,\n", " 'Generator calls',\n", " 'Fitness score',\n", " colors=model_colors,\n", " min_x_val=3,\n", " #max_x_val=40000,\n", " min_y_val=0,\n", " max_y_val=90,\n", " figsize=figsize,\n", " save_fig=True,\n", " fig_name=problem_prefix + experiment_suffix + \"_fitness_log_logscale\",\n", " legend_outside=True,\n", " log10_scale=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Plot trajectory data\n", "\n", "experiment_suffix = \"_traj_comparisons_4_target_700_lat_penalty\"\n", "\n", "model_colors = ['indigo', 'black', 'dimgrey']\n", "\n", "figsize = (12, 4)\n", "\n", "#Generator time scale\n", "\n", "plot_trajectories(\n", " [0, 0, 0, 2, 2, 2],\n", " [\n", " \"Simulated Annealing (10000 iters) - 1,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 100,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 10,000,000 Seqs\",\n", " \"DEN Earthm - 1,000 Seqs\",\n", " \"DEN Earthm - 100,000 Seqs\",\n", " \"DEN Earthm - 10,000,000 Seqs\",\n", " ],\n", " [1000.0, 100000.0, 10000000.0, 1.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0, 1000.0, 100000.0, 10000000.0],\n", " [0, 0, 0, 0, 0, 0],\n", " traj_gen_xs,\n", " traj_ys,\n", " traj_names,\n", " 0,\n", " 'Generator calls',\n", " 'Fitness score',\n", " colors=model_colors,\n", " min_x_val=3,\n", " #max_x_val=40000,\n", " min_y_val=0,\n", " max_y_val=90,\n", " figsize=figsize,\n", " save_fig=True,\n", " fig_name=problem_prefix + experiment_suffix + \"_fitness_log_logscale_2\",\n", " legend_outside=True,\n", " log10_scale=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.interpolate import interp1d\n", "\n", "def plot_bars(internal_traj_indices, traj_indices, traj_names, iteration_scales, iteration_constants, add_zeros, iterations, measures, model_names, measure_ix, x_label, y_label, colors=None, figsize=(6, 4), legend_outside=False, save_fig=False, fig_name=\"default_1\", fig_dpi=150, min_x_val=0, max_x_val=None, min_y_val=None, max_y_val=None, log10_scale=False) :\n", " \n", "\n", " max_iter_val = 0\n", " max_meas_val = -np.inf\n", " \n", " iter_interps = []\n", " meas_interps = []\n", "\n", " for i, [model_ix, traj_name] in enumerate(zip(traj_indices, traj_names)) :\n", " #for model_ix, [iters, all_meas] in enumerate(zip(iterations, measures)) :\n", "\n", " iters, all_meas = iterations[model_ix], measures[model_ix]\n", "\n", " meas = np.zeros(all_meas[measure_ix, :].shape)\n", " meas[:] = all_meas[measure_ix, :]\n", "\n", " iters_copy = np.zeros(iters.shape)\n", " iters_copy[:] = iters[:]\n", "\n", " if add_zeros[i] is not None :\n", " iters_copy = np.concatenate([np.array([0]), iters_copy], axis=0)\n", " meas = np.concatenate([np.array([add_zeros[i]]), meas], axis=0)\n", "\n", " iters_copy[1:] = iters_copy[1:] * iteration_scales[i] + iteration_constants[i]\n", "\n", " max_iter_val = max(max_iter_val, np.max(iters_copy))\n", " max_meas_val = max(max_meas_val, np.max(meas))\n", " \n", " f_interp = interp1d(iters_copy, meas)\n", " \n", " iter_interp = np.linspace(iters_copy[0], iters_copy[-1], 1000)\n", " meas_interp = f_interp(iter_interp)\n", " \n", " if log10_scale :\n", " iter_interp[1:] = np.log10(iter_interp[1:])\n", " \n", " iter_interps.append(iter_interp)\n", " meas_interps.append(meas_interp)\n", " \n", " \n", " if log10_scale :\n", " max_iter_val = np.log10(max_iter_val)\n", " \n", " meas_perc_50 = 0.5 * max_meas_val\n", " meas_perc_80 = 0.8 * max_meas_val\n", " meas_perc_95 = 0.95 * max_meas_val\n", " meas_perc_99 = 0.99 * max_meas_val\n", " \n", " f = plt.figure(figsize=figsize)\n", " \n", " for i, [model_ix, traj_name] in enumerate(zip(internal_traj_indices, traj_names)) :\n", " \n", " iter_interp = iter_interps[i]\n", " meas_interp = meas_interps[i]\n", " \n", " first_iter_perc_50_ind = np.nonzero(meas_interp >= meas_perc_50)[0]\n", " first_iter_perc_50_ix = iter_interp[first_iter_perc_50_ind[0]] if len(first_iter_perc_50_ind) > 0 else max_iter_val\n", " \n", " first_iter_perc_80_ind = np.nonzero(meas_interp >= meas_perc_80)[0]\n", " first_iter_perc_80_ix = iter_interp[first_iter_perc_80_ind[0]] if len(first_iter_perc_80_ind) > 0 else max_iter_val\n", " \n", " first_iter_perc_95_ind = np.nonzero(meas_interp >= meas_perc_95)[0]\n", " first_iter_perc_95_ix = iter_interp[first_iter_perc_95_ind[0]] if len(first_iter_perc_95_ind) > 0 else max_iter_val\n", " \n", " first_iter_perc_99_ind = np.nonzero(meas_interp >= meas_perc_99)[0]\n", " first_iter_perc_99_ix = iter_interp[first_iter_perc_99_ind[0]] if len(first_iter_perc_99_ind) > 0 else max_iter_val\n", " \n", " if colors is not None :\n", " plt.bar([model_ix + 0.25 * int(i % 3)], [first_iter_perc_99_ix], width=0.25, color=colors[model_ix][3], edgecolor='black', linewidth=1, label=model_names[model_ix])\n", " plt.bar([model_ix + 0.25 * int(i % 3)], [first_iter_perc_95_ix], width=0.25, color=colors[model_ix][2], edgecolor='black', linewidth=1, label=model_names[model_ix])\n", " plt.bar([model_ix + 0.25 * int(i % 3)], [first_iter_perc_80_ix], width=0.25, color=colors[model_ix][1], edgecolor='black', linewidth=1, label=model_names[model_ix])\n", " plt.bar([model_ix + 0.25 * int(i % 3)], [first_iter_perc_50_ix], width=0.25, color=colors[model_ix][0], edgecolor='black', linewidth=1, label=model_names[model_ix])\n", "\n", " plt.xticks([], [])\n", " \n", " if log10_scale :\n", " plt.yticks(np.arange(int(max_iter_val) + 1), 10**np.arange(int(max_iter_val) + 1), fontsize=14, rotation=45)\n", " else :\n", " plt.yticks(fontsize=14, rotation=45)\n", " plt.yticks(fontsize=14)\n", " \n", " if min_x_val is not None and max_x_val :\n", " plt.xlim(min_x_val, max_x_val)\n", " \n", " if min_y_val is not None and max_y_val is not None :\n", " plt.ylim(min_y_val, max_y_val)\n", "\n", " plt.xlabel(x_label, fontsize=14)\n", " plt.ylabel(y_label, fontsize=14)\n", " \n", " if not legend_outside :\n", " plt.legend(fontsize=14, loc='upper left')\n", " else :\n", " plt.legend(fontsize=14, bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", " \n", " plt.tight_layout()\n", " \n", " if save_fig :\n", " plt.savefig(fig_name + \".eps\")\n", " plt.savefig(fig_name + \".svg\")\n", " plt.savefig(fig_name + \".png\", dpi=fig_dpi, transparent=True)\n", " \n", " plt.show()\n", "\n", "\n", "experiment_suffix = \"_traj_comparisons_bars_4_target_700_seq_penalty\"\n", "\n", "model_colors = [\n", " [\n", " 'violet',\n", " 'mediumorchid',\n", " 'darkviolet',\n", " 'indigo'\n", " ],\n", " [\n", " 'yellow',\n", " 'gold',\n", " 'orange',\n", " 'darkorange'\n", " ],\n", " [\n", " 'yellow',\n", " 'gold',\n", " 'orange',\n", " 'darkorange'\n", " ]\n", "]\n", "\n", "figsize = (12, 6)\n", "\n", "#Generator time scale\n", "\n", "plot_bars(\n", " [0, 0, 0, 1, 1, 1],\n", " [0, 0, 0, 1, 1, 1],\n", " [\n", " \"Simulated Annealing (10000 iters) - 1,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 100,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 10,000,000 Seqs\",\n", " \"DEN Earthm - 1,000 Seqs\",\n", " \"DEN Earthm - 100,000 Seqs\",\n", " \"DEN Earthm - 10,000,000 Seqs\",\n", " ],\n", " [1000.0, 100000.0, 10000000.0, 1.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0, 1000.0, 100000.0, 10000000.0],\n", " [0, 0, 0, 0, 0, 0],\n", " traj_gen_xs,\n", " traj_ys,\n", " traj_names,\n", " 0,\n", " 'Generative Algorithm',\n", " 'Generator Calls',\n", " colors=model_colors,\n", " #min_x_val=3,\n", " #max_x_val=40000,\n", " min_y_val=3,\n", " max_y_val=11.5,\n", " figsize=figsize,\n", " save_fig=True,\n", " fig_name=problem_prefix + experiment_suffix + \"_fitness_log_logscale_2\",\n", " legend_outside=True,\n", " log10_scale=True\n", ")\n", "\n", "\n", "experiment_suffix = \"_traj_comparisons_bars_4_target_700_lat_penalty\"\n", "\n", "model_colors = [\n", " [\n", " 'violet',\n", " 'mediumorchid',\n", " 'darkviolet',\n", " 'indigo'\n", " ],\n", " [\n", " 'yellow',\n", " 'gold',\n", " 'orange',\n", " 'darkorange'\n", " ],\n", " [\n", " 'yellow',\n", " 'gold',\n", " 'orange',\n", " 'darkorange'\n", " ]\n", "]\n", "\n", "figsize = (12, 6)\n", "\n", "#Generator time scale\n", "\n", "plot_bars(\n", " [0, 0, 0, 1, 1, 1],\n", " [0, 0, 0, 2, 2, 2],\n", " [\n", " \"Simulated Annealing (10000 iters) - 1,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 100,000 Seqs\",\n", " \"Simulated Annealing (10000 iters) - 10,000,000 Seqs\",\n", " \"DEN Earthm - 1,000 Seqs\",\n", " \"DEN Earthm - 100,000 Seqs\",\n", " \"DEN Earthm - 10,000,000 Seqs\",\n", " ],\n", " [1000.0, 100000.0, 10000000.0, 1.0, 1.0, 1.0],\n", " [0.0, 0.0, 0.0, 1000.0, 100000.0, 10000000.0],\n", " [0, 0, 0, 0, 0, 0],\n", " traj_gen_xs,\n", " traj_ys,\n", " traj_names,\n", " 0,\n", " 'Generative Algorithm',\n", " 'Generator Calls',\n", " colors=model_colors,\n", " #min_x_val=3,\n", " #max_x_val=40000,\n", " min_y_val=3,\n", " max_y_val=11.5,\n", " figsize=figsize,\n", " save_fig=True,\n", " fig_name=problem_prefix + experiment_suffix + \"_fitness_log_logscale_2\",\n", " legend_outside=True,\n", " log10_scale=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_tensorflow_p36)", "language": "python", "name": "conda_tensorflow_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }