Loading deepchem/molnet/__init__.py +6 −0 Original line number Diff line number Diff line Loading @@ -32,6 +32,12 @@ from deepchem.molnet.load_function.thermosol_datasets import load_thermosol from deepchem.molnet.load_function.hppb_datasets import load_hppb from deepchem.molnet.load_function.chembl25_datasets import load_chembl25 from deepchem.molnet.dnasim import simulate_motif_density_localization from deepchem.molnet.dnasim import simulate_motif_counting from deepchem.molnet.dnasim import simple_motif_embedding from deepchem.molnet.dnasim import motif_density from deepchem.molnet.dnasim import simulate_single_motif_detection from deepchem.molnet.run_benchmark import run_benchmark #from deepchem.molnet.run_benchmark_low_data import run_benchmark_low_data from deepchem.molnet import run_benchmark_models deepchem/molnet/dnasim.py 0 → 100644 +404 −0 Original line number Diff line number Diff line from collections import OrderedDict import numpy as np def get_distribution(GC_fraction): from simdna.util import DiscreteDistribution return DiscreteDistribution({ 'A': (1 - GC_fraction) / 2, 'C': GC_fraction / 2, 'G': GC_fraction / 2, 'T': (1 - GC_fraction) / 2 }) def simple_motif_embedding(motif_name, seq_length, num_seqs, GC_fraction): """ Simulates sequences with a motif embedded anywhere in the sequence. Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence num_seqs: int number of sequences GC_fraction : float GC basepair fraction in background sequence Returns ------- sequence_arr : 1darray Array with sequence strings. embedding_arr: 1darray Array of embedding objects. """ import simdna from simdna import synthetic if motif_name is None: embedders = [] else: loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) embedders = [ synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator)) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), embedders) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, embedding_arr def motif_density(motif_name, seq_length, num_seqs, min_counts, max_counts, GC_fraction, central_bp=None): """ Returns sequences with motif density, along with embeddings array. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) if central_bp is not None: position_generator = synthetic.InsideCentralBp(central_bp) else: position_generator = synthetic.UniformPositionGenerator() quantity_generator = synthetic.UniformIntegerGenerator(min_counts, max_counts) embedders = [ synthetic.RepeatedEmbedder( synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator), position_generator), quantity_generator) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), embedders) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, embedding_arr def simulate_single_motif_detection(motif_name, seq_length, num_pos, num_neg, GC_fraction): """ Simulates two classes of seqeuences: - Positive class sequence with a motif embedded anywhere in the sequence - Negative class sequence without the motif Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence num_pos : int number of positive class sequences num_neg : int number of negative class sequences GC_fraction : float GC fraction in background sequence Returns ------- sequence_arr : 1darray Array with sequence strings. y : 1darray Array with positive/negative class labels. embedding_arr: 1darray Array of embedding objects. """ motif_sequence_arr, positive_embedding_arr = simple_motif_embedding( motif_name, seq_length, num_pos, GC_fraction) random_sequence_arr, negative_embedding_arr = simple_motif_embedding( None, seq_length, num_neg, GC_fraction) sequence_arr = np.concatenate((motif_sequence_arr, random_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_motif_counting(motif_name, seq_length, pos_counts, neg_counts, num_pos, num_neg, GC_fraction): """ Generates data for motif counting task. Parameters ---------- motif_name : str seq_length : int pos_counts : list (min_counts, max_counts) for positive set. neg_counts : list (min_counts, max_counts) for negative set. num_pos : int num_neg : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ pos_count_sequence_array, positive_embedding_arr = motif_density( motif_name, seq_length, num_pos, pos_counts[0], pos_counts[1], GC_fraction) neg_count_sequence_array, negative_embedding_arr = motif_density( motif_name, seq_length, num_pos, neg_counts[0], neg_counts[1], GC_fraction) sequence_arr = np.concatenate((pos_count_sequence_array, neg_count_sequence_array)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_motif_density_localization(motif_name, seq_length, center_size, min_motif_counts, max_motif_counts, num_pos, num_neg, GC_fraction): """ Simulates two classes of seqeuences: - Positive class sequences with multiple motif instances in center of the sequence. - Negative class sequences with multiple motif instances anywhere in the sequence. The number of motif instances is uniformly sampled between minimum and maximum motif counts. Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence center_size : int length of central part of the sequence where motifs can be positioned min_motif_counts : int minimum number of motif instances max_motif_counts : int maximum number of motif instances num_pos : int number of positive class sequences num_neg : int number of negative class sequences GC_fraction : float GC fraction in background sequence Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ localized_density_sequence_array, positive_embedding_arr = motif_density( motif_name, seq_length, num_pos, min_motif_counts, max_motif_counts, GC_fraction, center_size) unlocalized_density_sequence_array, negative_embedding_arr = motif_density( motif_name, seq_length, num_neg, min_motif_counts, max_motif_counts, GC_fraction) sequence_arr = np.concatenate((localized_density_sequence_array, unlocalized_density_sequence_array)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs, max_num_motifs, num_seqs, GC_fraction): """ Generates data for multi motif recognition task. Parameters ---------- motif_names : list List of strings. seq_length : int min_num_motifs : int max_num_motifs : int num_seqs : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : ndarray Contains labels for each motif. embedding_arr: 1darray Array of embedding objects. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) def get_embedder(motif_name): substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) return synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator), name=motif_name) embedders = [get_embedder(motif_name) for motif_name in motif_names] quantity_generator = synthetic.UniformIntegerGenerator( min_num_motifs, max_num_motifs) combined_embedder = [ synthetic.RandomSubsetOfEmbedders(quantity_generator, embedders) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), combined_embedder) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) label_generator = synthetic.IsInTraceLabelGenerator(np.asarray(motif_names)) y = np.array( [ label_generator.generateLabels(generated_seq) for generated_seq in generated_sequences ], dtype=bool) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, y, embedding_arr def simulate_differential_accessibility( pos_motif_names, neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, num_neg, GC_fraction): """ Generates data for differential accessibility task. Parameters ---------- pos_motif_names : list List of strings. neg_motif_names : list List of strings. seq_length : int min_num_motifs : int max_num_motifs : int num_pos : int num_neg : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ pos_motif_sequence_arr, _, positive_embedding_arr = simulate_multi_motif_embedding( pos_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, GC_fraction) neg_motif_sequence_arr, _, negative_embedding_arr = simulate_multi_motif_embedding( neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_neg, GC_fraction) sequence_arr = np.concatenate((pos_motif_sequence_arr, neg_motif_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_heterodimer_grammar(motif1, motif2, seq_length, min_spacing, max_spacing, num_pos, num_neg, GC_fraction): """ Simulates two classes of sequences with motif1 and motif2: - Positive class sequences with motif1 and motif2 positioned min_spacing and max_spacing - Negative class sequences with independent motif1 and motif2 positioned anywhere in the sequence, not as a heterodimer grammar Parameters ---------- seq_length : int, length of sequence GC_fraction : float, GC fraction in background sequence num_pos : int, number of positive class sequences num_neg : int, number of negatice class sequences motif1 : str, encode motif name motif2 : str, encode motif name min_spacing : int, minimum inter motif spacing max_spacing : int, maximum inter motif spacing Returns ------- sequence_arr : 1darray Array with sequence strings. y : 1darray Array with positive/negative class labels. embedding_arr: list List of embedding objects. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) motif1_generator = synthetic.ReverseComplementWrapper( synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif1)) motif2_generator = synthetic.ReverseComplementWrapper( synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif2)) separation_generator = synthetic.UniformIntegerGenerator( min_spacing, max_spacing) embedder = synthetic.EmbeddableEmbedder( synthetic.PairEmbeddableGenerator(motif1_generator, motif2_generator, separation_generator)) embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), [embedder]) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_pos).generateSequences()) grammar_sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) positive_embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] nongrammar_sequence_arr, _, negative_embedding_arr = simulate_multi_motif_embedding( [motif1, motif2], seq_length, 2, 2, num_neg, GC_fraction) sequence_arr = np.concatenate((grammar_sequence_arr, nongrammar_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr Loading
deepchem/molnet/__init__.py +6 −0 Original line number Diff line number Diff line Loading @@ -32,6 +32,12 @@ from deepchem.molnet.load_function.thermosol_datasets import load_thermosol from deepchem.molnet.load_function.hppb_datasets import load_hppb from deepchem.molnet.load_function.chembl25_datasets import load_chembl25 from deepchem.molnet.dnasim import simulate_motif_density_localization from deepchem.molnet.dnasim import simulate_motif_counting from deepchem.molnet.dnasim import simple_motif_embedding from deepchem.molnet.dnasim import motif_density from deepchem.molnet.dnasim import simulate_single_motif_detection from deepchem.molnet.run_benchmark import run_benchmark #from deepchem.molnet.run_benchmark_low_data import run_benchmark_low_data from deepchem.molnet import run_benchmark_models
deepchem/molnet/dnasim.py 0 → 100644 +404 −0 Original line number Diff line number Diff line from collections import OrderedDict import numpy as np def get_distribution(GC_fraction): from simdna.util import DiscreteDistribution return DiscreteDistribution({ 'A': (1 - GC_fraction) / 2, 'C': GC_fraction / 2, 'G': GC_fraction / 2, 'T': (1 - GC_fraction) / 2 }) def simple_motif_embedding(motif_name, seq_length, num_seqs, GC_fraction): """ Simulates sequences with a motif embedded anywhere in the sequence. Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence num_seqs: int number of sequences GC_fraction : float GC basepair fraction in background sequence Returns ------- sequence_arr : 1darray Array with sequence strings. embedding_arr: 1darray Array of embedding objects. """ import simdna from simdna import synthetic if motif_name is None: embedders = [] else: loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) embedders = [ synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator)) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), embedders) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, embedding_arr def motif_density(motif_name, seq_length, num_seqs, min_counts, max_counts, GC_fraction, central_bp=None): """ Returns sequences with motif density, along with embeddings array. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) if central_bp is not None: position_generator = synthetic.InsideCentralBp(central_bp) else: position_generator = synthetic.UniformPositionGenerator() quantity_generator = synthetic.UniformIntegerGenerator(min_counts, max_counts) embedders = [ synthetic.RepeatedEmbedder( synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator), position_generator), quantity_generator) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), embedders) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, embedding_arr def simulate_single_motif_detection(motif_name, seq_length, num_pos, num_neg, GC_fraction): """ Simulates two classes of seqeuences: - Positive class sequence with a motif embedded anywhere in the sequence - Negative class sequence without the motif Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence num_pos : int number of positive class sequences num_neg : int number of negative class sequences GC_fraction : float GC fraction in background sequence Returns ------- sequence_arr : 1darray Array with sequence strings. y : 1darray Array with positive/negative class labels. embedding_arr: 1darray Array of embedding objects. """ motif_sequence_arr, positive_embedding_arr = simple_motif_embedding( motif_name, seq_length, num_pos, GC_fraction) random_sequence_arr, negative_embedding_arr = simple_motif_embedding( None, seq_length, num_neg, GC_fraction) sequence_arr = np.concatenate((motif_sequence_arr, random_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_motif_counting(motif_name, seq_length, pos_counts, neg_counts, num_pos, num_neg, GC_fraction): """ Generates data for motif counting task. Parameters ---------- motif_name : str seq_length : int pos_counts : list (min_counts, max_counts) for positive set. neg_counts : list (min_counts, max_counts) for negative set. num_pos : int num_neg : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ pos_count_sequence_array, positive_embedding_arr = motif_density( motif_name, seq_length, num_pos, pos_counts[0], pos_counts[1], GC_fraction) neg_count_sequence_array, negative_embedding_arr = motif_density( motif_name, seq_length, num_pos, neg_counts[0], neg_counts[1], GC_fraction) sequence_arr = np.concatenate((pos_count_sequence_array, neg_count_sequence_array)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_motif_density_localization(motif_name, seq_length, center_size, min_motif_counts, max_motif_counts, num_pos, num_neg, GC_fraction): """ Simulates two classes of seqeuences: - Positive class sequences with multiple motif instances in center of the sequence. - Negative class sequences with multiple motif instances anywhere in the sequence. The number of motif instances is uniformly sampled between minimum and maximum motif counts. Parameters ---------- motif_name : str encode motif name seq_length : int length of sequence center_size : int length of central part of the sequence where motifs can be positioned min_motif_counts : int minimum number of motif instances max_motif_counts : int maximum number of motif instances num_pos : int number of positive class sequences num_neg : int number of negative class sequences GC_fraction : float GC fraction in background sequence Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ localized_density_sequence_array, positive_embedding_arr = motif_density( motif_name, seq_length, num_pos, min_motif_counts, max_motif_counts, GC_fraction, center_size) unlocalized_density_sequence_array, negative_embedding_arr = motif_density( motif_name, seq_length, num_neg, min_motif_counts, max_motif_counts, GC_fraction) sequence_arr = np.concatenate((localized_density_sequence_array, unlocalized_density_sequence_array)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs, max_num_motifs, num_seqs, GC_fraction): """ Generates data for multi motif recognition task. Parameters ---------- motif_names : list List of strings. seq_length : int min_num_motifs : int max_num_motifs : int num_seqs : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : ndarray Contains labels for each motif. embedding_arr: 1darray Array of embedding objects. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) def get_embedder(motif_name): substring_generator = synthetic.PwmSamplerFromLoadedMotifs( loaded_motifs, motif_name) return synthetic.SubstringEmbedder( synthetic.ReverseComplementWrapper(substring_generator), name=motif_name) embedders = [get_embedder(motif_name) for motif_name in motif_names] quantity_generator = synthetic.UniformIntegerGenerator( min_num_motifs, max_num_motifs) combined_embedder = [ synthetic.RandomSubsetOfEmbedders(quantity_generator, embedders) ] embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), combined_embedder) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences()) sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) label_generator = synthetic.IsInTraceLabelGenerator(np.asarray(motif_names)) y = np.array( [ label_generator.generateLabels(generated_seq) for generated_seq in generated_sequences ], dtype=bool) embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] return sequence_arr, y, embedding_arr def simulate_differential_accessibility( pos_motif_names, neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, num_neg, GC_fraction): """ Generates data for differential accessibility task. Parameters ---------- pos_motif_names : list List of strings. neg_motif_names : list List of strings. seq_length : int min_num_motifs : int max_num_motifs : int num_pos : int num_neg : int GC_fraction : float Returns ------- sequence_arr : 1darray Contains sequence strings. y : 1darray Contains labels. embedding_arr: 1darray Array of embedding objects. """ pos_motif_sequence_arr, _, positive_embedding_arr = simulate_multi_motif_embedding( pos_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, GC_fraction) neg_motif_sequence_arr, _, negative_embedding_arr = simulate_multi_motif_embedding( neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_neg, GC_fraction) sequence_arr = np.concatenate((pos_motif_sequence_arr, neg_motif_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr def simulate_heterodimer_grammar(motif1, motif2, seq_length, min_spacing, max_spacing, num_pos, num_neg, GC_fraction): """ Simulates two classes of sequences with motif1 and motif2: - Positive class sequences with motif1 and motif2 positioned min_spacing and max_spacing - Negative class sequences with independent motif1 and motif2 positioned anywhere in the sequence, not as a heterodimer grammar Parameters ---------- seq_length : int, length of sequence GC_fraction : float, GC fraction in background sequence num_pos : int, number of positive class sequences num_neg : int, number of negatice class sequences motif1 : str, encode motif name motif2 : str, encode motif name min_spacing : int, minimum inter motif spacing max_spacing : int, maximum inter motif spacing Returns ------- sequence_arr : 1darray Array with sequence strings. y : 1darray Array with positive/negative class labels. embedding_arr: list List of embedding objects. """ import simdna from simdna import synthetic loaded_motifs = synthetic.LoadedEncodeMotifs( simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001) motif1_generator = synthetic.ReverseComplementWrapper( synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif1)) motif2_generator = synthetic.ReverseComplementWrapper( synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif2)) separation_generator = synthetic.UniformIntegerGenerator( min_spacing, max_spacing) embedder = synthetic.EmbeddableEmbedder( synthetic.PairEmbeddableGenerator(motif1_generator, motif2_generator, separation_generator)) embed_in_background = synthetic.EmbedInABackground( synthetic.ZeroOrderBackgroundGenerator( seq_length, discreteDistribution=get_distribution(GC_fraction)), [embedder]) generated_sequences = tuple( synthetic.GenerateSequenceNTimes(embed_in_background, num_pos).generateSequences()) grammar_sequence_arr = np.array( [generated_seq.seq for generated_seq in generated_sequences]) positive_embedding_arr = [ generated_seq.embeddings for generated_seq in generated_sequences ] nongrammar_sequence_arr, _, negative_embedding_arr = simulate_multi_motif_embedding( [motif1, motif2], seq_length, 2, 2, num_neg, GC_fraction) sequence_arr = np.concatenate((grammar_sequence_arr, nongrammar_sequence_arr)) y = np.array([[True]] * num_pos + [[False]] * num_neg) embedding_arr = positive_embedding_arr + negative_embedding_arr return sequence_arr, y, embedding_arr
