import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Concatenate, Reshape, Softmax, Conv2DTranspose, Embedding, Multiply
from keras.callbacks import ModelCheckpoint, EarlyStopping, Callback
from keras import regularizers
from keras import backend as K
import keras.losses
import tensorflow as tf
from tensorflow.python.framework import ops
import isolearn.keras as iso
import numpy as np
import tensorflow as tf
import logging
logging.getLogger('tensorflow').setLevel(logging.ERROR)
import pandas as pd
import os
import pickle
import numpy as np
import scipy.sparse as sp
import scipy.io as spio
import matplotlib.pyplot as plt
import isolearn.keras as iso
from seqprop.visualization import *
from seqprop.generator import *
from seqprop.predictor import *
from seqprop.optimizer import *
from definitions.dragonn import load_saved_predictor
import warnings
warnings.simplefilter("ignore")
from keras.backend.tensorflow_backend import set_session
def contain_tf_gpu_mem_usage() :
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
set_session(sess)
contain_tf_gpu_mem_usage()
Using TensorFlow backend.
#Download DragoNN Tutorial 4 models
#These saved models are broken/deleted...
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/case1_spi1_model.hdf5
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/case1_ctcf_model.hdf5
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/old/case2_spi1_model.hdf5
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/case2_ctcf_model.hdf5
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/case3_model.hdf5
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/case4_spi1_model.hdf5
## Download SPI1 classification model
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/SPI1.classification.model.hdf5
#spi1_classification_model=load_dragonn_model("SPI1.classification.model.hdf5")
## Download SPI1 regression model
#!wget http://mitra.stanford.edu/kundaje/projects/dragonn/SPI1.regression.model.hdf5
#spi1_regression_model=load_dragonn_model("SPI1.regression.model.hdf5")
#Define target isoform loss function
def get_earthmover_loss(pwm_start=0, pwm_end=70, pwm_target_bits=1.8, pwm_entropy_weight=0.0) :
punish_c = 0.0
punish_g = 0.0
punish_aa = 0.0
entropy_mse = get_margin_entropy(pwm_start=pwm_start, pwm_end=pwm_end, min_bits=pwm_target_bits)
punish_c_func = get_punish_c(pwm_start=pwm_start, pwm_end=pwm_end)
punish_g_func = get_punish_g(pwm_start=pwm_start, pwm_end=pwm_end)
punish_aa_func = get_punish_aa(pwm_start=pwm_start, pwm_end=pwm_end)
def loss_func(predictor_outputs) :
pwm_logits, pwm, sampled_pwm, pred_bind, pred_score = predictor_outputs
#Specify costs
fitness_loss = -1.0 * K.mean(pred_score[..., 0], axis=0)
seq_loss = 0.0
seq_loss += punish_c * K.mean(punish_c_func(sampled_pwm), axis=0)
seq_loss += punish_g * K.mean(punish_g_func(sampled_pwm), axis=0)
seq_loss += punish_aa * K.mean(punish_aa_func(sampled_pwm), axis=0)
entropy_loss = pwm_entropy_weight * entropy_mse(pwm)
#Compute total loss
total_loss = fitness_loss + seq_loss + entropy_loss
return K.reshape(K.sum(total_loss, axis=0), (1,))
def val_loss_func(predictor_outputs) :
pwm_logits, pwm, sampled_pwm, pred_bind, pred_score = predictor_outputs
#Specify costs
fitness_loss = -1.0 * K.mean(pred_score[..., 0], axis=0)
seq_loss = 0.0
seq_loss += punish_c * K.mean(punish_c_func(sampled_pwm), axis=0)
seq_loss += punish_g * K.mean(punish_g_func(sampled_pwm), axis=0)
seq_loss += punish_aa * K.mean(punish_aa_func(sampled_pwm), axis=0)
entropy_loss = pwm_entropy_weight * entropy_mse(pwm)
#Compute total loss
total_loss = fitness_loss + seq_loss + entropy_loss
return K.reshape(K.mean(total_loss, axis=0), (1,))
return loss_func, val_loss_func
def get_nop_transform() :
def _transform_func(pwm) :
return pwm
return _transform_func
class ValidationCallback(Callback):
def __init__(self, val_name, val_loss_model, val_steps) :
self.val_name = val_name
self.val_loss_model = val_loss_model
self.val_steps = val_steps
self.val_loss_history = []
#Track val loss
self.val_loss_history.append(self.val_loss_model.predict(x=None, steps=self.val_steps)[0])
def on_batch_end(self, batch, logs={}) :
#Track val loss
val_loss_value = self.val_loss_model.predict(x=None, steps=self.val_steps)[0]
self.val_loss_history.append(val_loss_value)
#Function for running SeqProp on a set of objectives to optimize
def run_seqprop(sequence_templates, loss_funcs, val_loss_funcs, transform_funcs, n_sequences=1, n_samples=1, n_valid_samples=1, eval_mode='sample', normalize_logits=False, n_epochs=10, steps_per_epoch=100) :
n_objectives = len(sequence_templates)
seqprop_predictors = []
valid_monitors = []
train_histories = []
valid_histories = []
for obj_ix in range(n_objectives) :
print("Optimizing objective " + str(obj_ix) + '...')
sequence_template = sequence_templates[obj_ix]
loss_func = loss_funcs[obj_ix]
val_loss_func = val_loss_funcs[obj_ix]
transform_func = transform_funcs[obj_ix]
#Build Generator Network
_, seqprop_generator = build_generator(seq_length=len(sequence_template), n_sequences=n_sequences, n_samples=n_samples, sequence_templates=[sequence_template * n_sequences], batch_normalize_pwm=normalize_logits, pwm_transform_func=transform_func, validation_sample_mode=eval_mode)
#for layer in seqprop_generator.layers :
# if 'policy' not in layer.name :
# layer.name += "_trainversion"
_, valid_generator = build_generator(seq_length=len(sequence_template), n_sequences=n_sequences, n_samples=n_valid_samples, sequence_templates=[sequence_template * n_sequences], batch_normalize_pwm=normalize_logits, pwm_transform_func=None, validation_sample_mode='sample', master_generator=seqprop_generator)
for layer in valid_generator.layers :
#if 'policy' not in layer.name :
layer.name += "_valversion"
#Build Predictor Network and hook it on the generator PWM output tensor
_, seqprop_predictor = build_predictor(seqprop_generator, load_saved_predictor(model_path, library_context=None), n_sequences=n_sequences, n_samples=n_samples, eval_mode='pwm' if eval_mode == 'pwm' else 'sample')
#for layer in seqprop_predictor.layers :
# if '_trainversion' not in layer.name and 'policy' not in layer.name :
# layer.name += "_trainversion"
_, valid_predictor = build_predictor(valid_generator, load_saved_predictor(model_path, library_context=None), n_sequences=n_sequences, n_samples=n_valid_samples, eval_mode='sample')
for layer in valid_predictor.layers :
if '_valversion' not in layer.name :# and 'policy' not in layer.name :
layer.name += "_valversion"
#Build Loss Model (In: Generator seed, Out: Loss function)
_, loss_model = build_loss_model(seqprop_predictor, loss_func)
_, valid_loss_model = build_loss_model(valid_predictor, val_loss_func)
#Specify Optimizer to use
#opt = keras.optimizers.SGD(lr=0.5)
#opt = keras.optimizers.SGD(lr=0.1, momentum=0.9, decay=0, nesterov=True)
opt = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
#Compile Loss Model (Minimize self)
loss_model.compile(loss=lambda true, pred: pred, optimizer=opt)
def get_logit(p) :
return np.log(p / (1. - p))
#Specify callback entities
#measure_func = lambda pred_outs: np.mean(get_logit(np.expand_dims(pred_outs[0], axis=0) if len(pred_outs[0].shape) <= 2 else pred_outs[0]), axis=0)
measure_func = lambda pred_outs: np.mean(np.expand_dims(pred_outs[1], axis=0) if len(pred_outs[1].shape) <= 2 else pred_outs[1], axis=0)
#train_monitor = FlexibleSeqPropMonitor(predictor=seqprop_predictor, plot_on_train_end=False, plot_every_epoch=False, track_every_step=True, measure_func=measure_func, measure_name='Binding Log Odds', plot_pwm_start=500, plot_pwm_end=700, sequence_template=sequence_template, plot_pwm_indices=np.arange(n_sequences).tolist(), figsize=(12, 1.0))
valid_monitor = FlexibleSeqPropMonitor(predictor=valid_predictor, plot_on_train_end=True, plot_every_epoch=False, track_every_step=True, measure_func=measure_func, measure_name='Binding Log Odds', plot_pwm_start=500, plot_pwm_end=600, sequence_template=sequence_template, plot_pwm_indices=np.arange(n_sequences).tolist(), figsize=(12, 1.0))
train_history = ValidationCallback('loss', loss_model, 1)
valid_history = ValidationCallback('val_loss', valid_loss_model, 1)
callbacks =[
#EarlyStopping(monitor='loss', min_delta=0.001, patience=5, verbose=0, mode='auto'),
valid_monitor,
train_history,
valid_history
]
#Fit Loss Model
_ = loss_model.fit(
[], np.ones((1, 1)), #Dummy training example
epochs=n_epochs,
steps_per_epoch=steps_per_epoch,
callbacks=callbacks
)
valid_monitor.predictor = None
train_history.val_loss_model = None
valid_history.val_loss_model = None
seqprop_predictors.append(seqprop_predictor)
valid_monitors.append(valid_monitor)
train_histories.append(train_history)
valid_histories.append(valid_history)
return seqprop_predictors, valid_monitors, train_histories, valid_histories
#Specfiy file path to pre-trained predictor network
save_dir = os.path.join(os.getcwd(), '')
model_name = 'SPI1.classification.model.hdf5'
model_path = os.path.join(save_dir, model_name)
import random
def set_seed(seed_value) :
# 1. Set the `PYTHONHASHSEED` environment variable at a fixed value
os.environ['PYTHONHASHSEED']=str(seed_value)
# 2. Set the `python` built-in pseudo-random generator at a fixed value
random.seed(seed_value)
# 3. Set the `numpy` pseudo-random generator at a fixed value
np.random.seed(seed_value)
# 4. Set the `tensorflow` pseudo-random generator at a fixed value
tf.set_random_seed(seed_value)
# 5. Configure a new global `tensorflow` session
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)
seq_template = 'N' * 1000
rand_seed = 1177#14755
#Run SeqProp Optimization
print("Running optimization experiment 'DragoNN SPI1 Maximization'")
#Number of PWMs to generate per objective
n_sequences = 10
#Number of One-hot sequences to sample from the PWM at each grad step
n_samples = 1
#Number of epochs per objective to optimize
n_epochs = 1
#Number of steps (grad updates) per epoch
steps_per_epoch = 200
#Number of One-hot validation sequences to sample from the PWM
n_valid_samples = 10
n_samples_list = [1, 10, 1, 10]
experiment_name_list = ['Simple-ST-IN-1x', 'Simple-ST-IN-10x', 'Sampled-IN-1x', 'Sampled-IN-10x']
eval_mode_list = ['simple_sample', 'simple_sample', 'sample', 'sample']
normalize_logits_list = [True, True, True, True]
result_dict = {
'Simple-ST-IN-1x' : {},
'Simple-ST-IN-10x' : {},
'Sampled-IN-1x' : {},
'SSampled-IN-10x' : {}
}
for experiment_name, n_samples, eval_mode, normalize_logits in zip(experiment_name_list, n_samples_list, eval_mode_list, normalize_logits_list) :
print("Experiment name = " + str(experiment_name))
print("N samples = " + str(n_samples))
print("Eval mode = " + str(eval_mode))
print("Normalize logits = " + str(normalize_logits))
K.clear_session()
set_seed(rand_seed)
sequence_templates = [
seq_template
]
losses, val_losses = zip(*[
get_earthmover_loss(
pwm_start=0,
pwm_end=1000,
pwm_target_bits=1.8,
pwm_entropy_weight=0.0
)
])
transforms = [
None
]
seqprop_predictors, valid_monitors, train_histories, valid_histories = run_seqprop(sequence_templates, losses, val_losses, transforms, n_sequences, n_samples, n_valid_samples, eval_mode, normalize_logits, n_epochs, steps_per_epoch)
seqprop_predictor, valid_monitor, train_history, valid_history = seqprop_predictors[0], valid_monitors[0], train_histories[0], valid_histories[0]
result_dict[experiment_name] = {
'seqprop_predictor' : seqprop_predictor,
'valid_monitor' : valid_monitor,
'train_history' : train_history,
'valid_history' : valid_history,
}
Running optimization experiment 'DragoNN SPI1 Maximization' Experiment name = Simple-ST-IN-1x N samples = 1 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 200/200 [==============================] - 4s 21ms/step - loss: -193.6830
Experiment name = Simple-ST-IN-10x N samples = 10 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 200/200 [==============================] - 5s 27ms/step - loss: -244.7122
Experiment name = Sampled-IN-1x N samples = 1 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 200/200 [==============================] - 4s 20ms/step - loss: -290.6786
Experiment name = Sampled-IN-10x N samples = 10 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 200/200 [==============================] - 5s 26ms/step - loss: -453.5182
save_figs = True
fig_prefix = "eval_seqprop_dragonn_spi1_earthmover_tweaked_samplers_experiment_200_updates_"
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
print("Experiment name = " + str(experiment_name))
seqprop_predictor = result_dict[experiment_name]['seqprop_predictor']
valid_monitor = result_dict[experiment_name]['valid_monitor']
train_history = result_dict[experiment_name]['train_history']
valid_history = result_dict[experiment_name]['valid_history']
#Store statistics for optimized sequences
fig_name = fig_prefix + experiment_name + "_" if save_figs else None
valid_monitor.plot_metrics_and_pwm(fig_name=fig_name)
f = plt.figure(figsize=(6, 4))
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(train_history.val_loss_history) / n_sequences, color='darkgreen', linewidth=2, linestyle='-', label='Train')
l2 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(valid_history.val_loss_history), color='darkorange', linewidth=2, linestyle='--', label='Valid')
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min(np.min(train_history.val_loss_history) / n_sequences, np.min(valid_history.val_loss_history)), max(np.max(train_history.val_loss_history) / n_sequences, np.max(valid_history.val_loss_history)))
plt.legend(handles=[l1[0], l2[0]], fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_name + '_loss.png', transparent=True, dpi=150)
plt.savefig(fig_name + '_loss.svg')
plt.savefig(fig_name + '_loss.eps')
plt.show()
print("--- Comparison of loss convergence ---")
for history_prefix in ['train', 'valid'] :
loss_normalizer = n_sequences if history_prefix == 'train' else 1.
y_label_prefix = 'Train' if history_prefix == 'train' else 'Validation'
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -65.
max_y_val = 10.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_history = result_dict[experiment_name][history_prefix + '_history']
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(curr_history.val_loss_history) / loss_normalizer, linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
max_y_val = max(max_y_val, np.max(curr_history.val_loss_history) / loss_normalizer)
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel(y_label_prefix + " Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.svg')
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.eps')
plt.show()
print("--- Comparison of log odds convergence ---")
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -10.
max_y_val = 65.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_monitor = result_dict[experiment_name]['valid_monitor']
meas_history = curr_monitor.measure_history
meas_history = [np.mean(meas_history[k]) for k in range(len(meas_history))]
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(meas_history), linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
#max_y_val = max(max_y_val, np.max(meas_history))
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Validation Binding Log Odds", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + '_valid_logodds_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + '_valid_logodds_cmp.svg')
plt.savefig(fig_prefix + '_valid_logodds_cmp.eps')
plt.show()
Experiment name = Simple-ST-IN-1x
Experiment name = Simple-ST-IN-10x
Experiment name = Sampled-IN-1x
Experiment name = Sampled-IN-10x
--- Comparison of loss convergence ---
--- Comparison of log odds convergence ---
seq_template = 'N' * 1000
rand_seed = 1177#14755
#Run SeqProp Optimization
print("Running optimization experiment 'DragoNN SPI1 Maximization'")
#Number of PWMs to generate per objective
n_sequences = 10
#Number of One-hot sequences to sample from the PWM at each grad step
n_samples = 1
#Number of epochs per objective to optimize
n_epochs = 1
#Number of steps (grad updates) per epoch
steps_per_epoch = 2000
#Number of One-hot validation sequences to sample from the PWM
n_valid_samples = 10
n_samples_list = [1, 10, 1, 10]
experiment_name_list = ['Simple-ST-IN-1x', 'Simple-ST-IN-10x', 'Sampled-IN-1x', 'Sampled-IN-10x']
eval_mode_list = ['simple_sample', 'simple_sample', 'sample', 'sample']
normalize_logits_list = [True, True, True, True]
result_dict = {
'Simple-ST-IN-1x' : {},
'Simple-ST-IN-10x' : {},
'Sampled-IN-1x' : {},
'SSampled-IN-10x' : {}
}
for experiment_name, n_samples, eval_mode, normalize_logits in zip(experiment_name_list, n_samples_list, eval_mode_list, normalize_logits_list) :
print("Experiment name = " + str(experiment_name))
print("N samples = " + str(n_samples))
print("Eval mode = " + str(eval_mode))
print("Normalize logits = " + str(normalize_logits))
K.clear_session()
set_seed(rand_seed)
sequence_templates = [
seq_template
]
losses, val_losses = zip(*[
get_earthmover_loss(
pwm_start=0,
pwm_end=1000,
pwm_target_bits=1.8,
pwm_entropy_weight=0.0
)
])
transforms = [
None
]
seqprop_predictors, valid_monitors, train_histories, valid_histories = run_seqprop(sequence_templates, losses, val_losses, transforms, n_sequences, n_samples, n_valid_samples, eval_mode, normalize_logits, n_epochs, steps_per_epoch)
seqprop_predictor, valid_monitor, train_history, valid_history = seqprop_predictors[0], valid_monitors[0], train_histories[0], valid_histories[0]
result_dict[experiment_name] = {
'seqprop_predictor' : seqprop_predictor,
'valid_monitor' : valid_monitor,
'train_history' : train_history,
'valid_history' : valid_history,
}
Running optimization experiment 'DragoNN SPI1 Maximization' Experiment name = Simple-ST-IN-1x N samples = 1 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 2000/2000 [==============================] - 32s 16ms/step - loss: -340.8526
Experiment name = Simple-ST-IN-10x N samples = 10 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 2000/2000 [==============================] - 43s 21ms/step - loss: -328.8261
Experiment name = Sampled-IN-1x N samples = 1 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 2000/2000 [==============================] - 32s 16ms/step - loss: -708.8356
Experiment name = Sampled-IN-10x N samples = 10 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 2000/2000 [==============================] - 43s 21ms/step - loss: -742.0773
save_figs = True
fig_prefix = "eval_seqprop_dragonn_spi1_earthmover_tweaked_samplers_experiment_2000_updates_"
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
print("Experiment name = " + str(experiment_name))
seqprop_predictor = result_dict[experiment_name]['seqprop_predictor']
valid_monitor = result_dict[experiment_name]['valid_monitor']
train_history = result_dict[experiment_name]['train_history']
valid_history = result_dict[experiment_name]['valid_history']
#Store statistics for optimized sequences
fig_name = fig_prefix + experiment_name + "_" if save_figs else None
valid_monitor.plot_metrics_and_pwm(fig_name=fig_name)
f = plt.figure(figsize=(6, 4))
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(train_history.val_loss_history) / n_sequences, color='darkgreen', linewidth=2, linestyle='-', label='Train')
l2 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(valid_history.val_loss_history), color='darkorange', linewidth=2, linestyle='--', label='Valid')
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min(np.min(train_history.val_loss_history) / n_sequences, np.min(valid_history.val_loss_history)), max(np.max(train_history.val_loss_history) / n_sequences, np.max(valid_history.val_loss_history)))
plt.legend(handles=[l1[0], l2[0]], fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_name + '_loss.png', transparent=True, dpi=150)
plt.savefig(fig_name + '_loss.svg')
plt.savefig(fig_name + '_loss.eps')
plt.show()
print("--- Comparison of loss convergence ---")
for history_prefix in ['train', 'valid'] :
loss_normalizer = n_sequences if history_prefix == 'train' else 1.
y_label_prefix = 'Train' if history_prefix == 'train' else 'Validation'
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -90.
max_y_val = 10.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_history = result_dict[experiment_name][history_prefix + '_history']
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(curr_history.val_loss_history) / loss_normalizer, linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
max_y_val = max(max_y_val, np.max(curr_history.val_loss_history) / loss_normalizer)
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel(y_label_prefix + " Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.svg')
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.eps')
plt.show()
print("--- Comparison of log odds convergence ---")
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -10.
max_y_val = 90.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_monitor = result_dict[experiment_name]['valid_monitor']
meas_history = curr_monitor.measure_history
meas_history = [np.mean(meas_history[k]) for k in range(len(meas_history))]
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(meas_history), linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
#max_y_val = max(max_y_val, np.max(meas_history))
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Validation Binding Log Odds", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + '_valid_logodds_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + '_valid_logodds_cmp.svg')
plt.savefig(fig_prefix + '_valid_logodds_cmp.eps')
plt.show()
Experiment name = Simple-ST-IN-1x
Experiment name = Simple-ST-IN-10x
Experiment name = Sampled-IN-1x
Experiment name = Sampled-IN-10x
--- Comparison of loss convergence ---
--- Comparison of log odds convergence ---
seq_template = 'N' * 1000
rand_seed = 1177#14755
#Run SeqProp Optimization
print("Running optimization experiment 'DragoNN SPI1 Maximization'")
#Number of PWMs to generate per objective
n_sequences = 10
#Number of One-hot sequences to sample from the PWM at each grad step
n_samples = 1
#Number of epochs per objective to optimize
n_epochs = 1
#Number of steps (grad updates) per epoch
steps_per_epoch = 20000
#Number of One-hot validation sequences to sample from the PWM
n_valid_samples = 10
n_samples_list = [1, 10, 1, 10]
experiment_name_list = ['Simple-ST-IN-1x', 'Simple-ST-IN-10x', 'Sampled-IN-1x', 'Sampled-IN-10x']
eval_mode_list = ['simple_sample', 'simple_sample', 'sample', 'sample']
normalize_logits_list = [True, True, True, True]
result_dict = {
'Simple-ST-IN-1x' : {},
'Simple-ST-IN-10x' : {},
'Sampled-IN-1x' : {},
'SSampled-IN-10x' : {}
}
for experiment_name, n_samples, eval_mode, normalize_logits in zip(experiment_name_list, n_samples_list, eval_mode_list, normalize_logits_list) :
print("Experiment name = " + str(experiment_name))
print("N samples = " + str(n_samples))
print("Eval mode = " + str(eval_mode))
print("Normalize logits = " + str(normalize_logits))
K.clear_session()
set_seed(rand_seed)
sequence_templates = [
seq_template
]
losses, val_losses = zip(*[
get_earthmover_loss(
pwm_start=0,
pwm_end=1000,
pwm_target_bits=1.8,
pwm_entropy_weight=0.0
)
])
transforms = [
None
]
seqprop_predictors, valid_monitors, train_histories, valid_histories = run_seqprop(sequence_templates, losses, val_losses, transforms, n_sequences, n_samples, n_valid_samples, eval_mode, normalize_logits, n_epochs, steps_per_epoch)
seqprop_predictor, valid_monitor, train_history, valid_history = seqprop_predictors[0], valid_monitors[0], train_histories[0], valid_histories[0]
result_dict[experiment_name] = {
'seqprop_predictor' : seqprop_predictor,
'valid_monitor' : valid_monitor,
'train_history' : train_history,
'valid_history' : valid_history,
}
Running optimization experiment 'DragoNN SPI1 Maximization' Experiment name = Simple-ST-IN-1x N samples = 1 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 20000/20000 [==============================] - 312s 16ms/step - loss: -472.8669
Experiment name = Simple-ST-IN-10x N samples = 10 Eval mode = simple_sample Normalize logits = True Optimizing objective 0... Epoch 1/1 20000/20000 [==============================] - 421s 21ms/step - loss: -472.1349
Experiment name = Sampled-IN-1x N samples = 1 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 20000/20000 [==============================] - 315s 16ms/step - loss: -842.0206
Experiment name = Sampled-IN-10x N samples = 10 Eval mode = sample Normalize logits = True Optimizing objective 0... Epoch 1/1 20000/20000 [==============================] - 418s 21ms/step - loss: -852.0868
save_figs = True
fig_prefix = "eval_seqprop_dragonn_spi1_earthmover_tweaked_samplers_experiment_20000_updates_"
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
print("Experiment name = " + str(experiment_name))
seqprop_predictor = result_dict[experiment_name]['seqprop_predictor']
valid_monitor = result_dict[experiment_name]['valid_monitor']
train_history = result_dict[experiment_name]['train_history']
valid_history = result_dict[experiment_name]['valid_history']
#Store statistics for optimized sequences
fig_name = fig_prefix + experiment_name + "_" if save_figs else None
valid_monitor.plot_metrics_and_pwm(fig_name=fig_name)
f = plt.figure(figsize=(6, 4))
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(train_history.val_loss_history) / n_sequences, color='darkgreen', linewidth=2, linestyle='-', label='Train')
l2 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(valid_history.val_loss_history), color='darkorange', linewidth=2, linestyle='--', label='Valid')
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min(np.min(train_history.val_loss_history) / n_sequences, np.min(valid_history.val_loss_history)), max(np.max(train_history.val_loss_history) / n_sequences, np.max(valid_history.val_loss_history)))
plt.legend(handles=[l1[0], l2[0]], fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_name + '_loss.png', transparent=True, dpi=150)
plt.savefig(fig_name + '_loss.svg')
plt.savefig(fig_name + '_loss.eps')
plt.show()
print("--- Comparison of loss convergence ---")
for history_prefix in ['train', 'valid'] :
loss_normalizer = n_sequences if history_prefix == 'train' else 1.
y_label_prefix = 'Train' if history_prefix == 'train' else 'Validation'
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -90.
max_y_val = 10.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_history = result_dict[experiment_name][history_prefix + '_history']
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(curr_history.val_loss_history) / loss_normalizer, linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
max_y_val = max(max_y_val, np.max(curr_history.val_loss_history) / loss_normalizer)
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel(y_label_prefix + " Loss", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.svg')
plt.savefig(fig_prefix + history_prefix + '_loss_cmp.eps')
plt.show()
print("--- Comparison of log odds convergence ---")
f = plt.figure(figsize=(6, 4))
ls = []
min_y_val = -10.
max_y_val = 90.
for experiment_ix, experiment_name in enumerate(experiment_name_list) :
curr_monitor = result_dict[experiment_name]['valid_monitor']
meas_history = curr_monitor.measure_history
meas_history = [np.mean(meas_history[k]) for k in range(len(meas_history))]
l1 = plt.plot(np.arange(n_epochs * steps_per_epoch + 1), np.array(meas_history), linewidth=2, linestyle='-', label=experiment_name)
ls.append(l1[0])
#max_y_val = max(max_y_val, np.max(meas_history))
plt.xlabel("Weight Updates", fontsize=16)
plt.ylabel("Validation Binding Log Odds", fontsize=16)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0, n_epochs * steps_per_epoch)
plt.ylim(min_y_val, max_y_val)
plt.legend(handles=ls, fontsize=14)
plt.tight_layout()
if save_figs :
plt.savefig(fig_prefix + '_valid_logodds_cmp.png', transparent=True, dpi=150)
plt.savefig(fig_prefix + '_valid_logodds_cmp.svg')
plt.savefig(fig_prefix + '_valid_logodds_cmp.eps')
plt.show()
Experiment name = Simple-ST-IN-1x
Experiment name = Simple-ST-IN-10x
Experiment name = Sampled-IN-1x
Experiment name = Sampled-IN-10x
--- Comparison of loss convergence ---
--- Comparison of log odds convergence ---