general

the_code/General/Generative_model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3b921d47-d760-4438-b754-8a6d805e9415",
+   "metadata": {},
+   "source": [
+    "### The GAN models were trained using a conda environment. Below you can find how to create the same environment to train GAN models and generate sequences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cd67f38-a1bf-4234-bbad-cf5baa718e59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%bash\n",
+    "# conda create --name deeplearning_py36_tf114_gpu python=3.6 tensorflow-gpu=1.14.0 keras-gpu=2.2.4\n",
+    "# conda activate deeplearning_py36_tf114_gpu\n",
+    "# conda install numpy=1.16.2  matplotlib=3.1.1 shap=0.29.3 ipykernel=5.1.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a03be807-48c6-42c9-90ec-f1a2e16d95f1",
+   "metadata": {},
+   "source": [
+    "### Below you can find explanation of parameters that were used to train model and generate sequences.\n",
+    "#### (These variables can be found in the beginning of __wgan_gp.py__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a272a4a-71da-40ac-9906-a74c62bfe3f8",
+   "metadata": {},
+   "source": [
+    "__BATCH_SIZE__: Batch size (how many regions will be used in each iteration). \\\n",
+    "__ITERS__: Number of batch iterations to train the model. \\\n",
+    "__SEQ_LEN__: Length of the input sequences.  \\\n",
+    "__SEQ_DIM__: Dimension of the input sequences. (4 nucleotides) \\\n",
+    "__DIM__: Dimension of the model. It is used in latent space and convolutional layers.  \\\n",
+    "__CRITIC_ITERS__: How many training iterations will be done for Discriminator for each Generator iteration.  \\\n",
+    "__LAMBDA__: Hyperparameter for gradient penalty.  \\\n",
+    "__loginterval__: Once every N iteration the log will be saved.  \\\n",
+    "__seqinterval__: Once every N iteration the sample sequences will be generated.  \\\n",
+    "__modelinterval__: Once every N iteration the model files will be saved.  \\\n",
+    "__selectedmodel__: When generating sequences, the iteration number of the model you want to use. \\\n",
+    "__suffix__: When generating sequences, the suffix to add to the header of the fasta regions. \\\n",
+    "__ngenerate__: When generating sequences, number of sequences you want to generate relative to batchsize. Example: 1 (128 sequences will be generated if the batch size is 128) \\\n",
+    "__outputdirc__: Path the to output folder. \\\n",
+    "__fastafile__: Path to the fasta file to use as real enhancers "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a4ab1ed-8973-4026-9d66-98f2ca6d764d",
+   "metadata": {},
+   "source": [
+    "### How to run the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "832087a8-fa27-4250-bb4e-066a52ded090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%bash\n",
+    "# conda activate deeplearning_py36_tf114_gpu\n",
+    "# python wgan_gp.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efc65b44-515e-4dd8-9bb9-81d1f0ceaa13",
+   "metadata": {},
+   "source": [
+    "### This will result following outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3d54fd-11d2-4fa8-92c7-b85c4007ad00",
+   "metadata": {},
+   "source": [
+    "__./models/__: Folder containing saved model's weight files. \\\n",
+    "__./samples_ACGT/__: Folder containing sampled sequences during training. \\\n",
+    "__./samples_raw/__: Folder containing sampled sequences (in their raw format) during training.  \\\n",
+    "__./gen_seq/__: Folder containing generated sequences after training. \\\n",
+    "__./disc.json__: Architecture file of the discriminator.  \\\n",
+    "__./gen.json__: Architecture file of the generator.  \\\n",
+    "__./d_g_loss.pkl__: Logged loss values during training. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9aa94e0f-263e-4ad1-967e-059770526381",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Deeplearning tf1.15",
+   "language": "python",
+   "name": "deeplearning_tf115"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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"inbound_nodes": [[["input_8", 0, 0, {}]]]}, {"name": "conv1d_16", "class_name": "Conv1D", "config": {"name": "conv1d_16", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_15", 0, 0, {}]]]}, {"name": "add_7", "class_name": "Add", "config": {"name": "add_7", "trainable": true}, "inbound_nodes": [[["input_8", 0, 0, {}], ["conv1d_16", 0, 0, {}]]]}], "input_layers": [["input_8", 0, 0]], "output_layers": [["add_7", 0, 0]]}}, {"class_name": "Model", "config": {"name": "model_9", "layers": [{"name": "input_9", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 500, 128], "dtype": "float32", "sparse": false, "name": "input_9"}, "inbound_nodes": []}, {"name": "activation_17", "class_name": "Activation", "config": {"name": "activation_17", "trainable": true, "activation": "relu"}, "inbound_nodes": [[["input_9", 0, 0, {}]]]}, {"name": "conv1d_18", "class_name": "Conv1D", "config": {"name": "conv1d_18", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_17", 0, 0, {}]]]}, {"name": "add_8", "class_name": "Add", "config": {"name": "add_8", "trainable": true}, "inbound_nodes": [[["input_9", 0, 0, {}], ["conv1d_18", 0, 0, {}]]]}], "input_layers": [["input_9", 0, 0]], "output_layers": [["add_8", 0, 0]]}}, {"class_name": "Model", "config": {"name": "model_10", "layers": [{"name": "input_10", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 500, 128], "dtype": "float32", "sparse": false, "name": "input_10"}, "inbound_nodes": []}, {"name": "activation_19", "class_name": "Activation", "config": {"name": "activation_19", "trainable": true, "activation": "relu"}, "inbound_nodes": [[["input_10", 0, 0, {}]]]}, {"name": "conv1d_20", "class_name": "Conv1D", "config": {"name": "conv1d_20", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_19", 0, 0, {}]]]}, {"name": "add_9", "class_name": "Add", "config": {"name": "add_9", "trainable": true}, "inbound_nodes": [[["input_10", 0, 0, {}], ["conv1d_20", 0, 0, {}]]]}], "input_layers": [["input_10", 0, 0]], "output_layers": [["add_9", 0, 0]]}}, {"class_name": "Model", "config": {"name": "model_11", "layers": [{"name": "input_11", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 500, 128], "dtype": "float32", "sparse": false, "name": "input_11"}, "inbound_nodes": []}, {"name": "activation_21", "class_name": "Activation", "config": {"name": "activation_21", "trainable": true, "activation": "relu"}, "inbound_nodes": [[["input_11", 0, 0, {}]]]}, {"name": "conv1d_22", "class_name": "Conv1D", "config": {"name": "conv1d_22", "trainable": true, "filters": 128, "kernel_size": [5], "strides": [1], "padding": "same", "dilation_rate": [1], "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["activation_21", 0, 0, {}]]]}, {"name": "add_10", "class_name": "Add", "config": {"name": "add_10", "trainable": true}, "inbound_nodes": [[["input_11", 0, 0, {}], ["conv1d_22", 0, 0, {}]]]}], "input_layers": [["input_11", 0, 0]], "output_layers": [["add_10", 0, 0]]}}, {"class_name": "Flatten", "config": {"name": "flatten_1", "trainable": true}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}], "inbound_nodes": [[["input_12", 0, 0, {}]]]}], "input_layers": [["input_12", 0, 0]], "output_layers": [["sequential_2", 1, 0]]}, "inbound_nodes": [[["model_6", 1, 0, {}]]]}], "input_layers": [["input_15", 0, 0]], "output_layers": [["model_12", 1, 0]]}, "keras_version": "2.1.5", "backend": "tensorflow"}

the_code/General/models/MEL_GAN/MEL_GAN_gen_iter160k_weights.hdf5

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the_code/General/output/testdata_motif_implant_100seqs.pkl

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the_code/General/output/testdata_sequence_evolution_10seqs.pkl

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+size 58025

the_code/General/utils.py

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+import tensorflow as tf
+import numpy as np
+import matplotlib
+
+def one_hot_encode_along_row_axis(sequence):
+    to_return = np.zeros((1, len(sequence), 4), dtype=np.int8)
+    seq_to_one_hot_fill_in_array(zeros_array=to_return[0], sequence=sequence, one_hot_axis=1)
+    return to_return
+
+
+def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):
+    assert one_hot_axis == 0 or one_hot_axis == 1
+    if one_hot_axis == 0:
+        assert zeros_array.shape[1] == len(sequence)
+    elif one_hot_axis == 1:
+        assert zeros_array.shape[0] == len(sequence)
+    for (i, char) in enumerate(sequence):
+        if char == "A" or char == "a":
+            char_idx = 0
+        elif char == "C" or char == "c":
+            char_idx = 1
+        elif char == "G" or char == "g":
+            char_idx = 2
+        elif char == "T" or char == "t":
+            char_idx = 3
+        elif char == "N" or char == "n":
+            continue
+        else:
+            raise RuntimeError("Unsupported character: " + str(char))
+        if one_hot_axis == 0:
+            zeros_array[char_idx, i] = 1
+        elif one_hot_axis == 1:
+            zeros_array[i, char_idx] = 1
+
+
+def readfile(filename):
+    ids = []
+    ids_d = {}
+    seqs = {}
+    f = open(filename, 'r')
+    lines = f.readlines()
+    f.close()
+    seq = []
+    for line in lines:
+        if line[0] == '>':
+            ids.append(line[1:].rstrip('\n'))
+            id_line = line[1:].rstrip('\n').split('_')[0]
+            if id_line not in seqs:
+                seqs[id_line] = []
+            if id_line not in ids_d:
+                ids_d[id_line] = id_line
+            if seq:
+                seqs[ids[-2].split('_')[0]] = ("".join(seq))
+            seq = []
+        else:
+            seq.append(line.rstrip('\n').upper())
+    if seq:
+        seqs[ids[-1].split('_')[0]] = ("".join(seq))
+
+    return ids, ids_d, seqs
+
+
+def prepare_data(filename):
+    ids, ids_d, seqs, = readfile(filename)
+    X = np.array([one_hot_encode_along_row_axis(seqs[id_]) for id_ in ids_d]).squeeze(axis=1)
+    data = X
+    return data, ids
+
+
+def plot_prediction_givenax(model, fig, ntrack, track_no, seq_onehot):
+    NUM_CLASSES = model.output_shape[1]
+    real_score = model.predict(seq_onehot)[0]
+    ax = fig.add_subplot(ntrack, 2, track_no*2-1)
+    ax.margins(x=0)
+    ax.set_ylabel('Prediction', color='red')
+    ax.plot(real_score, '--', color='gray', linewidth=3)
+    ax.scatter(range(NUM_CLASSES), real_score, marker='o', color='red', linewidth=11)
+    ax.tick_params(axis='y', labelcolor='red')
+    ax.set_xticks(range(NUM_CLASSES),)
+    ax.set_xticklabels(range(1, NUM_CLASSES+1))
+    ax.grid(True)
+    return ax
+
+
+def create_saturation_mutagenesis_x(onehot):
+    mutagenesis_X = {"X":[],"ids":[]}
+    onehot = onehot.squeeze()
+    for mutloc,nt in enumerate(onehot):
+        new_X = np.copy(onehot)
+        if list(nt) == [1, 0, 0, 0]:
+            new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            mutagenesis_X["ids"].append(str(mutloc)+"_C")
+            mutagenesis_X["ids"].append(str(mutloc)+"_G")
+            mutagenesis_X["ids"].append(str(mutloc)+"_T")
+        if list(nt) == [0, 1, 0, 0]:
+            new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            mutagenesis_X["ids"].append(str(mutloc)+"_A")
+            mutagenesis_X["ids"].append(str(mutloc)+"_G")
+            mutagenesis_X["ids"].append(str(mutloc)+"_T")
+        if list(nt) == [0, 0, 1, 0]:
+            new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 0, 1], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            mutagenesis_X["ids"].append(str(mutloc)+"_A")
+            mutagenesis_X["ids"].append(str(mutloc)+"_C")
+            mutagenesis_X["ids"].append(str(mutloc)+"_T")
+        if list(nt) == [0, 0, 0, 1]:
+            new_X[mutloc,:] = np.array([1, 0, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 1, 0, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            new_X[mutloc,:] = np.array([0, 0, 1, 0], dtype='int8')
+            mutagenesis_X["X"].append(np.copy(new_X))
+            mutagenesis_X["ids"].append(str(mutloc)+"_A")
+            mutagenesis_X["ids"].append(str(mutloc)+"_C")
+            mutagenesis_X["ids"].append(str(mutloc)+"_G")
+            
+    mutagenesis_X["X"] = np.array(mutagenesis_X["X"])
+    return mutagenesis_X 
+
+
+def plot_mutagenesis_givenax(model, fig, ntrack, track_no, seq_onehot, class_no):
+    
+    mutagenesis_X = create_saturation_mutagenesis_x(seq_onehot)
+    prediction_mutagenesis_X = model.predict(mutagenesis_X["X"])
+    original_prediction = model.predict(seq_onehot)
+    class_no = class_no-1
+    seq_shape = (seq_onehot.shape[1],seq_onehot.shape[2])
+    
+    arr_a = np.zeros(seq_shape[0])
+    arr_c = np.zeros(seq_shape[0])
+    arr_g = np.zeros(seq_shape[0])
+    arr_t = np.zeros(seq_shape[0])
+    delta_pred = original_prediction[:,class_no] - prediction_mutagenesis_X[:,class_no]
+    for i,mut in enumerate(mutagenesis_X["ids"]):
+        if mut.endswith("A"):
+            arr_a[int(mut.split("_")[0])]=delta_pred[i]
+        if mut.endswith("C"):
+            arr_c[int(mut.split("_")[0])]=delta_pred[i]
+        if mut.endswith("G"):
+            arr_g[int(mut.split("_")[0])]=delta_pred[i]
+        if mut.endswith("T"):
+            arr_t[int(mut.split("_")[0])]=delta_pred[i]
+
+    arr_a[arr_a == 0] = None
+    arr_c[arr_c == 0] = None
+    arr_g[arr_g == 0] = None
+    arr_t[arr_t == 0] = None
+    
+    ax = fig.add_subplot(ntrack, 1, track_no)
+    ax.set_ylabel('In silico\nMutagenesis')
+    ax.scatter(range(seq_shape[0]), -1*arr_a, label='A', color='green')
+    ax.scatter(range(seq_shape[0]), -1*arr_c, label='C', color='blue')
+    ax.scatter(range(seq_shape[0]), -1*arr_g, label='G', color='orange')
+    ax.scatter(range(seq_shape[0]), -1*arr_t, label='T', color='red')
+    ax.legend()
+    ax.axhline(y=0, linestyle='--', color='gray')
+    ax.set_xlim((0, seq_shape[0]))
+    _ = ax.set_xticks(np.arange(0, seq_shape[0]+1, 10))
+
+    return ax
+
+
+def insilico_evolution(regions, model, class_no, n_mutation):
+    #from scipy.stats import zscore
+    nuc_to_onehot = {"A":[1, 0, 0, 0],"C":[0, 1, 0, 0],"G":[0, 0, 1, 0],"T":[0, 0, 0, 1]}
+    mutation_pred = []
+    mutation_loc = []
+    print("Sequence index:",end=" ")
+    for id_ in range(len(regions)):
+        start_x = np.copy(regions[id_:id_+1])
+        pred = []
+        mut = []
+        for i in range(n_mutation):
+            mutagenesis_X = create_saturation_mutagenesis_x(start_x)
+            prediction_mutagenesis_X = model.predict(mutagenesis_X["X"])
+            original_prediction = model.predict(start_x)
+            ## To use max z-score
+            # next_one = mutagenesis_X["ids"][np.argmax(zscore(prediction_mutagenesis_X-original_prediction,axis=1)[:,class_no-1])]
+            ## To use max score
+            next_one = mutagenesis_X["ids"][np.argmax(prediction_mutagenesis_X[:,class_no-1]-original_prediction[:,class_no-1])]
+            pred.append(original_prediction)
+            mut.append(next_one)
+            start_x[0][int(next_one.split("_")[0]),:] = np.array(nuc_to_onehot[next_one.split("_")[1]], dtype='int8')
+        original_prediction = model.predict(start_x)
+        pred.append(original_prediction)
+        mutation_pred.append(pred)
+        mutation_loc.append(mut)
+        print(id_,end=",")
+    mutation_pred = np.array(mutation_pred).squeeze()
+    mutation_loc = np.array(mutation_loc)
+    return mutation_pred, mutation_loc
+
+
+def random_sequence_by_shuffling(seq_to_shuffle, number_of_random_regions):
+    seq_to_shuffle_onehot = one_hot_encode_along_row_axis(seq_to_shuffle)
+    shuffled_regions = []
+    for i in range(number_of_random_regions):
+        np.random.shuffle(seq_to_shuffle_onehot[0])
+        shuffled_regions.append(np.copy(seq_to_shuffle_onehot[0]))
+    shuffled_regions = np.array(shuffled_regions)
+    return shuffled_regions
+
+
+def random_sequence(seq_len, number_of_random_regions):
+    random_regions = []
+    for k in range(number_of_random_regions):
+        seq = []
+        for i in range(seq_len):
+            seq.append(np.random.choice(["A","C","G","T"]))
+        random_regions.append(one_hot_encode_along_row_axis("".join(seq)).squeeze())
+    random_regions = np.array(random_regions)
+    return random_regions
+
+
+def random_sequence_gc_adjusted(seq_len, number_of_random_regions, path_to_use_GC_content):
+    regions_to_use_GC = prepare_data(path_to_use_GC_content)
+    ACGT_dist = np.sum(regions_to_use_GC[0],axis=0)/len(regions_to_use_GC[0])
+    random_regions = []
+    for k in range(number_of_random_regions):
+        seq = []
+        for i in range(seq_len):
+            seq.append(np.random.choice(["A","C","G","T"],p=list(ACGT_dist[i])))
+        random_regions.append(one_hot_encode_along_row_axis("".join(seq)).squeeze())
+    random_regions = np.array(random_regions)
+    return random_regions
+
+
+def plot_deepexplainer_givenax(explainer, fig, ntrack, track_no, seq_onehot):
+    shap_values_ = explainer.shap_values(seq_onehot,ranked_outputs=1,check_additivity=False)
+    _, ax1 = plot_weights(shap_values_[0]*seq_onehot,
+                          fig, ntrack, 1, track_no,
+                          title="", subticks_frequency=10, ylab="")
+    return ax1
+
+
+def load_model(path_json, path_hdf5):
+    model_json_file = open(path_json)
+    model_json = model_json_file.read()
+    model = tf.keras.models.model_from_json(model_json)
+    model.load_weights(path_hdf5)
+    return model
+
+
+def add_pattern_to_best_location(pattern, regions, model, class_no):
+    pattern_added_regions =  np.zeros(regions.shape,dtype="int")
+    pattern_locations = np.zeros(regions.shape[0],dtype="int")
+    print("Sequence index:",end=" ")
+    for r, region in enumerate(regions):
+        tmp_array = np.zeros((regions.shape[1]-pattern.shape[1]+1,regions.shape[1],regions.shape[2]))
+        for nt in range(tmp_array.shape[0]):
+            tmp_array[nt] = np.copy(region)
+            tmp_array[nt,nt:nt+pattern.shape[1],:] = pattern[0]
+        prediction = model.predict(tmp_array)[:,class_no-1]
+        pattern_locations[r] = np.argmax(prediction)    
+        pattern_added_regions[r] = tmp_array[pattern_locations[r]]
+        print(r,end=",")
+    print("")
+    return {"regions":pattern_added_regions, "locations":pattern_locations}
+
+
+def plot_a(ax, base, left_edge, height, color):
+    a_polygon_coords = [
+        np.array([
+            [0.0, 0.0],
+            [0.5, 1.0],
+            [0.5, 0.8],
+            [0.2, 0.0],
+        ]),
+        np.array([
+            [1.0, 0.0],
+            [0.5, 1.0],
+            [0.5, 0.8],
+            [0.8, 0.0],
+        ]),
+        np.array([
+            [0.225, 0.45],
+            [0.775, 0.45],
+            [0.85, 0.3],
+            [0.15, 0.3],
+        ])
+    ]
+    for polygon_coords in a_polygon_coords:
+        ax.add_patch(matplotlib.patches.Polygon((np.array([1, height])[None, :] * polygon_coords
+                                                 + np.array([left_edge, base])[None, :]),
+                                                facecolor=color, edgecolor=color))
+
+
+def plot_c(ax, base, left_edge, height, color):
+    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=1.3, height=height,
+                                            facecolor=color, edgecolor=color))
+    ax.add_patch(
+        matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=0.7 * 1.3, height=0.7 * height,
+                                   facecolor='white', edgecolor='white'))
+    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 1, base], width=1.0, height=height,
+                                              facecolor='white', edgecolor='white', fill=True))
+
+
+def plot_g(ax, base, left_edge, height, color):
+    ax.add_patch(matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=1.3, height=height,
+                                            facecolor=color, edgecolor=color))
+    ax.add_patch(
+        matplotlib.patches.Ellipse(xy=[left_edge + 0.65, base + 0.5 * height], width=0.7 * 1.3, height=0.7 * height,
+                                   facecolor='white', edgecolor='white'))
+    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 1, base], width=1.0, height=height,
+                                              facecolor='white', edgecolor='white', fill=True))
+    ax.add_patch(
+        matplotlib.patches.Rectangle(xy=[left_edge + 0.825, base + 0.085 * height], width=0.174, height=0.415 * height,
+                                     facecolor=color, edgecolor=color, fill=True))
+    ax.add_patch(
+        matplotlib.patches.Rectangle(xy=[left_edge + 0.625, base + 0.35 * height], width=0.374, height=0.15 * height,
+                                     facecolor=color, edgecolor=color, fill=True))
+
+
+def plot_t(ax, base, left_edge, height, color):
+    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge + 0.4, base],
+                                              width=0.2, height=height, facecolor=color, edgecolor=color, fill=True))
+    ax.add_patch(matplotlib.patches.Rectangle(xy=[left_edge, base + 0.8 * height],
+                                              width=1.0, height=0.2 * height, facecolor=color, edgecolor=color,
+                                              fill=True))
+
+
+default_colors = {0: 'green', 1: 'blue', 2: 'orange', 3: 'red'}
+default_plot_funcs = {0: plot_a, 1: plot_c, 2: plot_g, 3: plot_t}
+
+
+def plot_weights_given_ax(ax, array,
+                          height_padding_factor,
+                          length_padding,
+                          subticks_frequency,
+                          highlight,
+                          colors=default_colors,
+                          plot_funcs=default_plot_funcs):
+    if len(array.shape) == 3:
+        array = np.squeeze(array)
+    assert len(array.shape) == 2, array.shape
+    if array.shape[0] == 4 and array.shape[1] != 4:
+        array = array.transpose(1, 0)
+    assert array.shape[1] == 4
+    max_pos_height = 0.0
+    min_neg_height = 0.0
+    heights_at_positions = []
+    depths_at_positions = []
+    for i in range(array.shape[0]):
+        acgt_vals = sorted(enumerate(array[i, :]), key=lambda x: abs(x[1]))
+        positive_height_so_far = 0.0
+        negative_height_so_far = 0.0
+        for letter in acgt_vals:
+            plot_func = plot_funcs[letter[0]]
+            color = colors[letter[0]]
+            if letter[1] > 0:
+                height_so_far = positive_height_so_far
+                positive_height_so_far += letter[1]
+            else:
+                height_so_far = negative_height_so_far
+                negative_height_so_far += letter[1]
+            plot_func(ax=ax, base=height_so_far, left_edge=i, height=letter[1], color=color)
+        max_pos_height = max(max_pos_height, positive_height_so_far)
+        min_neg_height = min(min_neg_height, negative_height_so_far)
+        heights_at_positions.append(positive_height_so_far)
+        depths_at_positions.append(negative_height_so_far)
+
+    for color in highlight:
+        for start_pos, end_pos in highlight[color]:
+            assert start_pos >= 0.0 and end_pos <= array.shape[0]
+            min_depth = np.min(depths_at_positions[start_pos:end_pos])
+            max_height = np.max(heights_at_positions[start_pos:end_pos])
+            ax.add_patch(
+                matplotlib.patches.Rectangle(xy=[start_pos, min_depth],
+                                             width=end_pos - start_pos,
+                                             height=max_height - min_depth,
+                                             edgecolor=color, fill=False))
+
+    ax.set_xlim(-length_padding, array.shape[0] + length_padding)
+    ax.xaxis.set_ticks(np.arange(0.0, array.shape[0] + 1, subticks_frequency))
+    height_padding = max(abs(min_neg_height) * (height_padding_factor),
+                         abs(max_pos_height) * (height_padding_factor))
+    ax.set_ylim(min_neg_height - height_padding, max_pos_height + height_padding)
+    return ax
+
+
+def plot_weights(array, fig, n, n1, n2, title='', ylab='',
+                 height_padding_factor=0.2,
+                 length_padding=1.0,
+                 subticks_frequency=20,
+                 colors=default_colors,
+                 plot_funcs=default_plot_funcs,
+                 highlight={}):
+    ax = fig.add_subplot(n, n1, n2)
+    ax.set_title(title)
+    ax.set_ylabel(ylab)
+    y = plot_weights_given_ax(ax=ax, array=array,
+                              height_padding_factor=height_padding_factor,
+                              length_padding=length_padding,
+                              subticks_frequency=subticks_frequency,
+                              colors=colors,
+                              plot_funcs=plot_funcs,
+                              highlight=highlight)
+    return fig, ax

the_code/General/wgan_gp.py

@@ -0,0 +1,344 @@
+# Following links were used to prepare this script. 
+# https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py
+# https://github.com/igul222/improved_wgan_training
+# https://arxiv.org/abs/1712.06148
+
+from __future__ import print_function, division
+import os
+import errno
+from keras.layers.merge import _Merge
+from keras.layers import Input, Dense, Reshape, Flatten, add, Activation
+from keras.layers.convolutional import Conv1D
+from keras.models import Sequential, Model
+from keras.optimizers import Adam
+from functools import partial
+import keras.backend as K
+import numpy as np
+
+
+BATCH_SIZE = 128
+ITERS = 400001
+SEQ_LEN = 500
+SEQ_DIM = 4
+DIM = 128
+CRITIC_ITERS = 10
+LAMBDA = 1
+loginterval = 1000
+seqinterval = 10000
+modelinterval = 10000
+selectedmodel = 400000
+suffix = "generated"
+ngenerate = 10
+outputdirc = "./output/"
+fastafile = "./data/KC_regions.fa"
+
+
+for file in [outputdirc,
+             os.path.join(outputdirc, 'models'),
+             os.path.join(outputdirc, 'samples_ACGT'),
+             os.path.join(outputdirc, 'samples_raw')]:
+    try:
+        os.makedirs(file)
+    except OSError as exc:
+        if exc.errno == errno.EEXIST:
+            pass
+
+
+def readfile(filename):
+    ids = []
+    seqs = []
+    f = open(filename, 'r')
+    lines = f.readlines()
+    f.close()
+    seq = []
+    for line in lines:
+        if line[0] == '>':
+            ids.append(line[1:].rstrip('\n'))
+            if seq != []: seqs.append("".join(seq))
+            seq = []
+        else:
+            seq.append(line.rstrip('\n').upper())
+    if seq != []:
+        seqs.append("".join(seq))
+
+    return ids, seqs
+
+
+def one_hot_encode_along_row_axis(sequence):
+    to_return = np.zeros((1, len(sequence), 4), dtype=np.int8)
+    seq_to_one_hot_fill_in_array(zeros_array=to_return[0],
+                                 sequence=sequence, one_hot_axis=1)
+    return to_return
+
+
+def seq_to_one_hot_fill_in_array(zeros_array, sequence, one_hot_axis):
+    assert one_hot_axis == 0 or one_hot_axis == 1
+    if one_hot_axis == 0:
+        assert zeros_array.shape[1] == len(sequence)
+    elif one_hot_axis == 1:
+        assert zeros_array.shape[0] == len(sequence)
+    for (i, char) in enumerate(sequence):
+        if char == "A" or char == "a":
+            char_idx = 0
+        elif char == "C" or char == "c":
+            char_idx = 1
+        elif char == "G" or char == "g":
+            char_idx = 2
+        elif char == "T" or char == "t":
+            char_idx = 3
+        elif char == "N" or char == "n":
+            continue
+        else:
+            raise RuntimeError("Unsupported character: "+str(char))
+        if one_hot_axis == 0:
+            zeros_array[char_idx, i] = 1
+        elif one_hot_axis == 1:
+            zeros_array[i, char_idx] = 1
+
+
+class RandomWeightedAverage(_Merge):
+    """Provides a (random) weighted average between real and generated image samples"""
+    def _merge_function(self, inputs):
+        alpha = K.random_uniform((BATCH_SIZE, 1, 1))
+        return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])
+
+
+class WGANGP():
+    def __init__(self):
+        self.img_rows = SEQ_LEN
+        self.img_cols = SEQ_DIM
+        self.img_shape = (self.img_rows, self.img_cols)
+        self.latent_dim = DIM
+
+        # Following parameter and optimizer set as recommended in paper
+        self.n_critic = CRITIC_ITERS
+        optimizer = Adam(lr=1e-4, beta_1=0.5, beta_2=0.9)
+
+        # Build the generator and critic
+        self.generator = self.build_generator()
+        self.critic = self.build_critic()
+
+        # -------------------------------
+        # Construct Computational Graph
+        #       for the Critic
+        # -------------------------------
+
+        # Freeze generator's layers while training critic
+        self.generator.trainable = False
+
+        # Image input (real sample)
+        real_img = Input(shape=self.img_shape)
+
+        # Noise input
+        z_disc = Input(shape=(DIM,))
+        # Generate image based of noise (fake sample)
+        fake_img = self.generator(z_disc)
+
+        # Discriminator determines validity of the real and fake images
+        fake = self.critic(fake_img)
+        valid = self.critic(real_img)
+
+        # Construct weighted average between real and fake images
+        interpolated_img = RandomWeightedAverage()([real_img, fake_img])
+        # Determine validity of weighted sample
+        validity_interpolated = self.critic(interpolated_img)
+
+        # Use Python partial to provide loss function with additional
+        # 'averaged_samples' argument
+        partial_gp_loss = partial(self.gradient_penalty_loss, averaged_samples=interpolated_img)
+        partial_gp_loss.__name__ = 'gradient_penalty'  # Keras requires function names
+
+        self.critic_model = Model(inputs=[real_img, z_disc],
+                                  outputs=[valid, fake, validity_interpolated])
+        self.critic_model.compile(loss=[self.wasserstein_loss, self.wasserstein_loss, partial_gp_loss],
+                                  optimizer=optimizer,
+                                  loss_weights=[1, 1, 10])
+
+        # -------------------------------
+        # Construct Computational Graph
+        #         for Generator
+        # -------------------------------
+
+        # For the generator we freeze the critic's layers
+        self.critic.trainable = False
+        self.generator.trainable = True
+
+        # Sampled noise for input to generator
+        z_gen = Input(shape=(DIM,))
+        # Generate images based of noise
+        img = self.generator(z_gen)
+        # Discriminator determines validity
+        valid = self.critic(img)
+        # Defines generator model
+        self.generator_model = Model(z_gen, valid)
+        self.generator_model.compile(loss=self.wasserstein_loss, optimizer=optimizer)
+
+    def gradient_penalty_loss(self, y_true, y_pred, averaged_samples):
+        """
+        Computes gradient penalty based on prediction and weighted real / fake samples
+        """
+        gradients = K.gradients(y_pred, averaged_samples)[0]
+        # compute the euclidean norm by squaring ...
+        gradients_sqr = K.square(gradients)
+        #   ... summing over the rows ...
+        gradients_sqr_sum = K.sum(gradients_sqr,
+                                  axis=np.arange(1, len(gradients_sqr.shape)))
+        #   ... and sqrt
+        gradient_l2_norm = K.sqrt(gradients_sqr_sum)
+        # compute lambda * (1 - ||grad||)^2 still for each single sample
+        gradient_penalty = LAMBDA * K.square(1 - gradient_l2_norm)
+        # return the mean as loss over all the batch samples
+        return K.mean(gradient_penalty)
+
+    def wasserstein_loss(self, y_true, y_pred):
+        return K.mean(y_true * y_pred)
+
+    def res_cnn(self):
+        input_tensor = Input(shape=(SEQ_LEN, DIM))
+        x = Activation('relu')(input_tensor)
+        x = Conv1D(DIM, 5, padding='same')(x)
+        output = add([input_tensor, x])
+        res_1d = Model(inputs=[input_tensor], outputs=[output])
+        return res_1d
+
+    def build_generator(self):
+        model = Sequential()
+        model.add(Dense(SEQ_LEN * DIM, activation='elu', input_shape=(DIM,)))
+        model.add(Reshape((SEQ_LEN, DIM)))
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(Conv1D(SEQ_DIM, 1, padding='same'))
+        model.add(Activation('softmax'))
+        model.summary()
+        noise = Input(shape=(self.latent_dim,))
+        img = model(noise)
+        return Model(noise, img)
+
+    def build_critic(self):
+        model = Sequential()
+        model.add(Conv1D(DIM, 1, padding='same', input_shape=(SEQ_LEN, SEQ_DIM)))
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(self.res_cnn())
+        model.add(Flatten())
+        model.add(Dense(1))
+        model.summary()
+        img = Input(shape=self.img_shape)
+        validity = model(img)
+        return Model(img, validity)
+
+    def train(self, foldername, filename, epochs, batch_size,
+              log_interval=1000, seq_interval=10000, model_interval=10000):
+
+        ids, seqs = readfile(filename)
+        X_train = np.array([one_hot_encode_along_row_axis(seq) for seq in seqs]).squeeze(axis=1)
+
+        # Adversarial ground truths
+        valid = -np.ones((batch_size, 1))
+        fake = np.ones((batch_size, 1))
+        dummy = np.zeros((batch_size, 1))
+
+        disc_json = self.critic_model.to_json()
+        with open(foldername + '/disc.json', "w") as disc_json_file:
+            disc_json_file.write(disc_json)
+
+        gen_json = self.generator_model.to_json()
+        with open(foldername + '/gen.json', "w") as gen_json_file:
+            gen_json_file.write(gen_json)
+            
+        d_loss_list = []
+        g_loss_list = []
+        for epoch in range(epochs):
+            for _ in range(self.n_critic):
+                # ---------------------
+                #  Train Discriminator
+                # ---------------------
+                # Select a random batch of images
+                idx = np.random.randint(0, X_train.shape[0], batch_size)
+                imgs = X_train[idx]
+                # Sample generator input
+                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
+                # Train the critic
+                d_loss = self.critic_model.train_on_batch([imgs, noise],
+                                                          [valid, fake, dummy])
+            # ---------------------
+            #  Train Generator
+            # ---------------------
+            g_loss = self.generator_model.train_on_batch(noise, valid)
+
+            if epoch % log_interval == 0:
+                d_loss_list.append(d_loss)
+                g_loss_list.append(g_loss)
+
+            if epoch % seq_interval == 0:
+                samples = []
+                for i in range(1):
+                    samples.extend(self.generate_samples())
+                with open(foldername + '/samples_ACGT/samples_ACGT_{}.fa'.format(epoch), 'w') as f:
+                    for line_number, s in enumerate(samples[0]):
+                        f.write(">" + str(line_number+1) + "\n")
+                        s = "".join(s)
+                        f.write(s + "\n")
+                with open((foldername + '/samples_raw/samples_{}.txt').format(epoch), 'w') as f2:
+                    print(samples[1], file=f2)
+
+            if epoch % model_interval == 0:
+                self.critic_model.save_weights(foldername + '/models/disc_{}.hdf5'.format(epoch))
+                self.critic_model.save(foldername + '/models/disc_{}.h5'.format(epoch))
+                self.generator_model.save_weights(foldername + '/models/gen_{}.hdf5'.format(epoch))
+                self.generator_model.save(foldername + '/models/gen_{}.h5'.format(epoch))
+        
+        
+        import pickle
+        f = open(foldername + '/d_g_loss.pkl', "wb")
+        pickle.dump(d_loss_list,f)
+        pickle.dump(g_loss_list,f)
+        f.close()
+        
+
+    def generate_samples(self):
+        char_ACGT={0:'A' , 1:'C' , 2:'G' , 3:'T'}
+        noise = np.random.normal(0, 1, (BATCH_SIZE, self.latent_dim))
+        gen_imgs = self.generator.predict(noise)
+        samples = np.argmax(gen_imgs, axis=2)
+        decoded_samples = []
+        for i in range(len(samples)):
+            decoded = ''
+            for j in range(len(samples[i])):
+                decoded += char_ACGT[samples[i][j]]
+            decoded_samples.append(decoded)
+        return decoded_samples, gen_imgs
+
+    def generate(self, nb=1, model_number=0, result_number=0):
+        hdf5_filename = outputdirc + "/models/disc_" + str(model_number) + ".hdf5"
+        self.generator_model.load_weights(hdf5_filename)
+        samples = []
+        for i in range(nb):
+            samples.extend(self.generate_samples()[0])
+        with open(outputdirc + '/gen_seq/generated_{}_iter_{}.fa'.format(nb*BATCH_SIZE, model_number), 'w') as f:
+            counter = 0
+            for s in samples:
+                counter += 1
+                s = "".join(s)
+                f.write(">" + str(counter) + "_" + str(result_number) + "_" + str(model_number) + "\n" + s + "\n")
+
+
+if __name__ == '__main__':
+    wgan = WGANGP()
+    # Train the model
+    wgan.train(outputdirc, fastafile, epochs=ITERS, batch_size=BATCH_SIZE,
+               log_interval=loginterval, seq_interval=seqinterval, model_interval=modelinterval)
+    
+    # Generate sequences after training
+    try:
+        os.makedirs(os.path.join(outputdirc, 'gen_seq'))
+    except OSError as exc:
+        if exc.errno == errno.EEXIST:
+            pass
+    for i in range(0, selectedmodel+1, modelinterval):
+        wgan.generate(nb=ngenerate, model_number=i, result_number=suffix)