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efficacy_scripts.py
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efficacy_scripts.py
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from plots import *
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
from interpret_tICs import *
import pickle
from msm_resampled import *
from sklearn.preprocessing import StandardScaler
import sklearn.preprocessing as preprocessing
from sklearn.metrics import auc as calculate_auc
import statsmodels
import scipy
from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.preprocessing import label_binarize, scale, StandardScaler
from scipy.stats import pearsonr
from msmbuilder.utils import verbosedump, verboseload
from analysis import *
from grids import *
from docking_analysis import *
from custom_msm import *
from custom_clusterer import *
from subsampling import *
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.preprocessing import scale
from random import shuffle
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, auc
from detect_intermediates import *
from interpret_tICs import *
from custom_tica import *
from sklearn.svm import SVR
from sklearn.metrics import roc_auc_score, precision_score, recall_score, accuracy_score, precision_recall_curve
from sklearn.preprocessing import StandardScaler
from pandas.tools.plotting import table
from msm_resampled import *
import random
from rdkit.ML.Scoring.Scoring import CalcBEDROC, CalcROC
import pybel as pb
import pubchempy as pc
matplotlib.style.use('ggplot')
def make_importances_df(importances, titles, scaled=False):
if scaled:
return pd.DataFrame(np.mean(np.vstack(list(importances)), axis=0), index = titles + ["%s_scaled" %n for n in titles], columns=["importance"]).sort("importance", inplace=False, ascending=False)
else:
return pd.DataFrame(np.mean(np.vstack(list(importances)), axis=0), index = titles, columns=["importance"]).sort("importance", inplace=False, ascending=False)
def calculate_cluster_averages_per_feature(clusterer, features):
n_clusters = clusterer.n_clusters
concatenated_clusters = np.concatenate(clusterer.labels_)
concatenated_features = np.concatenate(features)
cluster_averages = np.zeros((n_clusters, concatenated_features.shape[1]))
for i in range(0, n_clusters):
rows = np.where(concatenated_clusters == i)[0]
means = np.mean(concatenated_features[rows,:], axis=0)
cluster_averages[i,:] = means
return cluster_averages
def get_sample_coords(sample_indices, coords):
sample_coords = []
for cluster in range(0, np.shape(sample_indices)[0]):
print("Looking at cluster %d" %cluster)
cluster_coords = []
if sample_indices[cluster][0].shape[0] == 1:
traj_index_frame_tuples = [sample_indices[cluster]]
else:
traj_index_frame_tuples = sample_indices[cluster]
for traj_index_frame_tuple in traj_index_frame_tuples:
traj_index = traj_index_frame_tuple[0]
frame = traj_index_frame_tuple[1]
cluster_coords.append(coords[traj_index][frame])
cluster_coords = np.vstack(cluster_coords)
sample_coords.append(cluster_coords)
return sample_coords
def analyze_docking_results_in_dir(docking_dir, ligands_dir, precision="SP", redo=False, write_to_disk=False, ligands=None):
summary = "%s/all_docking_summary.csv" %docking_dir
df = analyze_docking_results_multiple(docking_dir, precision=precision,
summary=summary, ligands=None,
redo=redo, write_to_disk=write_to_disk)
return df
def initialize_analysis(clusterer_dir, user_defined_coords, user_defined_names, biased_agonist_dir, agonist_dir, inverse_agonist_dir, docking_dir,
precision, docking_multiple_ligands, aggregate_docking, feature_residues_pkl, n_components, top_features,
lag_time, n_clusters, projected_features_dir, traj_dir, traj_ext, tica_dir,
prior_counts, msm_object, analysis_dir, n_samples):
clusterer = compat_verboseload(clusterer_dir)
cluster_averages = calculate_cluster_averages_per_feature(clusterer, user_defined_coords)
cluster_averages = pd.DataFrame(cluster_averages, columns=user_defined_names)
active_clusters = cluster_averages.loc[(cluster_averages["rmsd_npxxy_active"] < 0.5) & (cluster_averages["tm6_tm3_dist"] > 12.) & (cluster_averages["tm6_tm3_dist"] < 15.)]
inactive_clusters = cluster_averages.loc[(cluster_averages["rmsd_npxxy_active"] > 0.5) & (cluster_averages["tm6_tm3_dist"] <10.)]
biased_ligands = get_ligands(biased_agonist_dir)
agonist_ligands = get_ligands(agonist_dir)
inverse_ligands = get_ligands(inverse_agonist_dir)
all_ligands = get_ligands("/home/enf/b2ar_analysis/all_ligands")
if not os.path.exists(docking_multiple_ligands):
analyze_docking_results_multiple(docking_dir, precision = precision, ligands = all_ligands, summary = docking_multiple_ligands, redo = True)
c = compute_cluster_averages(None, csv_filename=docking_multiple_ligands, save_csv=aggregate_docking)
with open(feature_residues_pkl, "rb") as f:
feature_residues = pickle.load(f)
tica_coords = compat_verboseload(projected_features_dir)
pp_n_components = n_components
apriori_dfs = []
for array in user_defined_coords:
apriori_dfs.append(pd.DataFrame(array, columns=user_defined_names))
tica_dfs = []
for array in tica_coords:
tica_dfs.append(pd.DataFrame(array, columns=["tIC.%d" %i for i in range(1,n_components+1)]))
cluster_pnas_averages = calculate_cluster_averages_per_feature(clusterer, user_defined_coords)
cluster_pnas_averages = pd.DataFrame(cluster_pnas_averages, columns=user_defined_names)
cluster_tica_averages = calculate_cluster_averages_per_feature(clusterer, tica_coords)
cluster_tica_averages = pd.DataFrame(cluster_tica_averages, columns=["tIC.%d" %i for i in range(1, n_components+1)])
cluster_tica_pnas = pd.concat([cluster_pnas_averages, cluster_tica_averages], axis=1).dropna()
clusters_map = make_clusters_map(clusterer)
tica_resampled_file = os.path.join(tica_dir, "tica_msm_lag-time%d_clusters%d_resampled.h5" %(lag_time, n_clusters))
projected_features = compat_verboseload(projected_features_dir)
num_trajs = len(get_trajectory_files(traj_dir, traj_ext))
def reweight_features_by_msm(msm_object):
total_samples = 10000
resampled_traj_to_frames_file = os.path.join(tica_dir, "msm_lag-time%d_prior-counts%s_clusters%d_resampled_%d.h5" %(lag_time, str(prior_counts), n_clusters, total_samples))
resampled_traj_to_frames = resample_by_msm(total_samples, msm_object, clusters_map, num_trajs, resampled_traj_to_frames_file)
resample_features_by_msm_equilibirum_pop(projected_features, resampled_traj_to_frames, tica_resampled_file)
tica_resampled = compat_verboseload(tica_resampled_file)
pnas_resampled_file = os.path.join(tica_dir, "pnas_resampled.h5")
resample_features_by_msm_equilibirum_pop(user_defined_coords, resampled_traj_to_frames, pnas_resampled_file)
pnas_resampled = compat_verboseload(pnas_resampled_file)
resampled_traj_index_pairs = []
for traj in resampled_traj_to_frames.keys():
[resampled_traj_index_pairs.append((traj, frame)) for frame in resampled_traj_to_frames[traj]]
features_eq = resample_features_by_msm_trajectory(top_features, resampled_traj_index_pairs)*10.
tica_eq = pd.DataFrame(tica_resampled, columns=["tIC.%d" %i for i in range(1,n_components+1)])
pnas_eq = pd.DataFrame(pnas_resampled, columns=user_defined_names)
features_eq = pd.concat([features_eq, tica_eq, pnas_eq], axis=1)
features_eq.columns = [str(f) for f in features_eq.columns.values.tolist()]
f0 = pd.concat([f for f in top_features], axis=0)
f2 = pd.concat([f for f in tica_dfs])
f3 = pd.concat([f for f in apriori_dfs])
prot_lig_features = pd.concat([f0,f2,f3],axis=1)
all_traj_features = [pd.concat([top_features[i]*10., tica_dfs[i], apriori_dfs[i]], axis=1) for i in range(0, len(tica_dfs))]
return features_eq, all_traj_features
n_steps = 10000
save_file = "%s/msm_traj_index_pairs.h5" % (tica_dir)
#msm_traj_index_pairs = generate_msm_traj_index_series(msm_object, random.choice(active_clusters.index.values.tolist()), n_steps, bu72_pp_clusters_map, save_file)
#msm_traj_index_pairs = compat_verboseload(save_file)
features_eq, all_traj_features = reweight_features_by_msm(msm_object)
samples_indices_file = "%s/samples_indices.h5" %analysis_dir
samples_dir = "%s/clusterer_%dclusters_%dsamples" %(tica_dir, n_clusters, n_samples)
if not os.path.exists(samples_dir):
os.makedirs(samples_dir)
sample_from_clusterer(clusterer_dir, projected_features_dir, get_trajectory_files(traj_dir, ".h5"),
n_samples, samples_dir, samples_indices_file, structure=None,
residue_cutoff=10000, parallel=True,
worker_pool=None)
clusters_map = make_clusters_map(compat_verboseload(clusterer_dir))
with open(feature_residues_pkl, "rb") as f:
feature_names = pickle.load(f)
samples_indices = compat_verboseload(samples_indices_file)
tica_coords = compat_verboseload(projected_features_dir)
samples_tica = []
samples_pnas = []
samples_features = []
samples_tica_file = "%s/clusterer_%dclusters_%dsamples_samples_kdtree_tica.h5" %(tica_dir, n_clusters, n_samples)
if not os.path.exists(samples_tica_file):
samples_tica = get_sample_coords(samples_indices, tica_coords)
verbosedump(samples_tica, samples_tica_file)
else:
samples_tica = compat_verboseload(samples_tica_file)
samples_tica_avg_df = pd.DataFrame([np.mean(t, axis=0) for t in samples_tica], index=["cluster%d" %i for i in range(0,n_clusters)], columns=["tIC.%d" %i for i in range(1, n_components+1)])
samples_pnas_file = "%s/clusterer_%dclusters_%dsamples_samples_kdtree_pnas.h5" %(tica_dir, n_clusters, n_samples)
#if not os.path.exists(samples_pnas_file):
samples_pnas = get_sample_coords(samples_indices, user_defined_coords)
verbosedump(samples_pnas, samples_pnas_file)
#else:
# samples_pnas = compat_verboseload(samples_pnas_file)
samples_pnas_avg_df = pd.DataFrame([np.mean(t, axis=0) for t in samples_pnas], index=["cluster%d" %i for i in range(0,n_clusters)], columns=user_defined_names)
samples_features_file = "%s/clusterer_%dclusters_%dsamples_samples_kdtree_features.h5" %(tica_dir, n_clusters, n_samples)
#if not os.path.exists(samples_features_file):
samples_features = get_sample_coords(samples_indices, [x.values for x in top_features])
#verbosedump(samples_features, samples_features_file)
#else:
# samples_features = compat_verboseload(samples_features_file)
samples_features_avg_df = pd.DataFrame([np.mean(t, axis=0) for t in samples_features], index=["cluster%d" %i for i in range(0,n_clusters)], columns=[str(f) for f in top_features[0].columns.values.tolist()])
"""
samples_normalized_features_file = "%s/clusterer_%dclusters_%dsamples_samples_kdtree_features_normalized.h5" %(tica_dir, n_clusters, n_samples)
if not os.path.exists(samples_normalized_features_file):
features = load_file_list(get_trajectory_files(features_dir, ".dataset"), directory = None, ext = None)
n = StandardScaler()
n.fit(np.concatenate(features))
normalized_features = [n.transform(f) for f in features]
samples_normalized_features = get_sample_coords(samples_indices, normalized_features)
verbosedump(samples_normalized_features, samples_normalized_features_file)
else:
samples_normalized_features = compat_verboseload(samples_normalized_features_file)
samples_normalized_features_avg_df = pd.DataFrame([np.mean(t, axis=0) for t in samples_normalized_features], index=["cluster%d" %i for i in range(0,n_clusters)], columns=[str(f) for f in feature_names])
"""
feature_strings = [str(feature_name) for feature_name in feature_names]
#samples_normalized_features_averages = [np.mean(f, axis=0) for f in samples_normalized_features]
#samples_normalized_features_averages_df = pd.DataFrame(samples_normalized_features_averages, columns=feature_strings)
samples_normalized_features_avg_df = pd.DataFrame(StandardScaler().fit_transform(samples_features_avg_df.values), columns=samples_features_avg_df.columns, index=samples_features_avg_df.index)
samples_pnas_tica = pd.concat([samples_pnas_avg_df, samples_tica_avg_df], axis=1)
samples_pnas_avg_df.sort("rmsd_npxxy_active", inplace=False)
docking_multiple_ligands = "%s/all_docking_scores.csv" % docking_dir
aggregate_docking = "%s/aggregate_docking.csv" % docking_dir
if not os.path.exists(docking_multiple_ligands):
analyze_docking_results_multiple(docking_dir, precision = precision, ligands = all_ligands, summary = docking_multiple_ligands, redo = True)
#if not os.path.exists(aggregate_docking):
# print(docking_multiple_ligands)
# compute_cluster_averages(None, csv_filename=docking_multiple_ligands, save_csv=aggregate_docking)
reference_docking_dir = "/home/enf/b2ar_analysis/reference_docking/docking_%s" %precision
reference_ligand_docking = "%s/all_docking_scores.csv" % reference_docking_dir
if not os.path.exists(reference_ligand_docking):
analyze_docking_results_multiple(reference_docking_dir, precision = precision, ligands = all_ligands, summary = reference_ligand_docking, redo = True)
reference_docking = pd.read_csv(reference_ligand_docking, index_col=0).dropna()
reference_docking.columns = [''.join(e for e in lig if e.isalnum() or e=='-' or e=='_') for lig in reference_docking.columns.values]
reference_docking.loc["null_scores"] = reference_docking.iloc[1].subtract(reference_docking.iloc[0])
return [clusterer, cluster_averages, active_clusters, inactive_clusters, biased_ligands, agonist_ligands, inverse_ligands, all_ligands, c, feature_residues, tica_coords, user_defined_coords, pp_n_components, apriori_dfs, tica_dfs,
cluster_pnas_averages, cluster_tica_averages, cluster_tica_pnas, top_features, clusters_map, tica_resampled_file, projected_features, num_trajs, features_eq, all_traj_features, samples_indices_file, samples_dir,
samples_tica_avg_df, samples_pnas_avg_df, samples_features_avg_df, samples_normalized_features_avg_df, feature_names, feature_strings, samples_pnas_tica, reference_docking]
def msm_reweighted_features_per_ligand(feature_dfs,
ligand_populations_df,
total_samples,
clusters_map,
msm_object,
save_dir="",
ligand_subset=None,
redo=False):
num_trajs = len(feature_dfs)
lig_features_eq = {}
lig_features_eq_filename = "%s/lig_features_eq.h5" %save_dir
if os.path.exists(lig_features_eq_filename) and not redo:
return(compat_verboseload(lig_features_eq_filename))
for ligand in ligand_populations_df.index.values.tolist():
if ligand_subset is not None:
if ligand not in ligand_subset:
continue
lig_msm_resampled_file = "%s/%s_msm_eq_resampled.h5" %(save_dir, ligand)
eq_pops = ligand_populations_df.loc[ligand][list(range(0, ligand_populations_df.shape[1]))].values
new_msm = copy.deepcopy(msm_object)
new_msm.populations_ = eq_pops
lig_traj_to_frames = resample_by_msm(total_samples,
msm_object=new_msm,
clusters_map=clusters_map,
num_trajs=num_trajs,
save_file=None,
equilibrium_populations=new_msm.populations_)
lig_features_eq[ligand] = resample_features_by_msm_equilibirum_pop(feature_dfs,
lig_traj_to_frames, None)
verbosedump(lig_features_eq, lig_features_eq_filename)
return lig_features_eq
def compute_docking_ddg(full_docking_df, md_lig_name, msm_object):
docking_df = copy.deepcopy(full_docking_df)
col_inds = []
cols = []
msm_state_ids = []
for i, cluster in enumerate(docking_df.columns.values.tolist()):
if "state" in cluster.lower():
cluster_id = int(cluster[5:])
print(cluster_id)
try:
print(msm_object.mapping_[cluster_id])
msm_state_ids.append(msm_object.mapping_[cluster_id])
col_inds.append(i)
cols.append(cluster)
except:
continue
msm_docking_df = docking_df[cols]
eq_pops = msm_object.populations_[msm_state_ids]
dg_md = np.log(eq_pops) / (-0.61)
Boltzmann_per_state = np.exp(-0.61 * (dg_md - (msm_docking_df.loc[md_lig_name].values)))
Z_apo = np.sum(Boltzmann_per_state)
dg_apo = np.log(Boltzmann_per_state / Z_apo) / (-0.61)
Boltzmanns_per_ligand = np.exp(-0.61*(dg_apo + msm_docking_df.values))
Z_per_ligand = np.sum(Boltzmanns_per_ligand, axis=1)
eq_pops_ligs = Boltzmanns_per_ligand / Z_per_ligand.reshape((-1,1))
dg = np.log(eq_pops_ligs) / (-0.61)
dg_dg_apo = np.vstack([dg, dg_apo.reshape((1, -1))])
all_eq_pops = np.exp(dg_dg_apo * (-0.61))
eq_pops_df = pd.DataFrame(all_eq_pops,
index=full_docking_df.index.values.tolist() + ["apo"],
columns=msm_state_ids)
print(msm_state_ids)
ddg = dg - dg_apo
docking_df[cols] = ddg
return(docking_df, eq_pops_df)
def compute_docking_dg(docking_cluster_averages, msm_object, samples_tica_avg_df, samples_pnas_avg_df,
samples_normalized_features_avg_df, important_contact_features, traj_dir, traj_ext,
tica_dir, ligands, reference_docking, clusters_map, feature_dfs, save_dir):
df_agg = docking_cluster_averages
#df_agg = pd.read_csv(aggregate_docking, index_col=0).dropna()
#df_agg = pd.read_csv(docking_multiple_ligands, index_col=0).dropna()
df_agg.index = [n.split("_")[0] for n in df_agg.index.values]
df_agg.columns = [''.join(e for e in lig if e.isalnum() or e=='-' or e=='_') for lig in df_agg.columns.values]
msm_obj =msm_object
msm_clusters = msm_obj.mapping_.keys()
msm_cluster_names = []
msm_cluster_eq_pops = []
for cluster_id in msm_clusters:
cluster_name = "cluster%d" %cluster_id
if cluster_name in df_agg.index.values:
state_id = msm_obj.mapping_[cluster_id]
msm_cluster_eq_pops.append(msm_obj.populations_[state_id])
msm_cluster_names.append(cluster_name)
msm_cluster_eq_pops = np.array(msm_cluster_eq_pops)
msm_cluster_deltaG = -0.61 * np.log(msm_cluster_eq_pops)
msm_cluster_eq_pops_df = pd.DataFrame(msm_cluster_eq_pops, index=msm_cluster_names)
aggregate_docking_msm = df_agg.loc[msm_cluster_names]
samples_tica_avg_df = samples_tica_avg_df.loc[msm_cluster_names]
samples_pnas_avg_df = samples_pnas_avg_df.loc[msm_cluster_names]
samples_top_features_avg_df = samples_normalized_features_avg_df.loc[msm_cluster_names]
print(aggregate_docking_msm.columns)
ligand = "3p0g_lig"
apo_deltaG = msm_cluster_deltaG - (-1.0 * aggregate_docking_msm[ligand].values)
apo_populations = np.exp(-1.0*apo_deltaG / 0.61)
Z_apo = np.sum(apo_populations)
apo_populations = apo_populations / Z_apo
apo_eq_pops_df = copy.deepcopy(msm_cluster_eq_pops_df)
apo_eq_pops_df[apo_eq_pops_df.columns] = apo_populations.reshape((-1,1))
apo_deltaG = -.61 * np.log(apo_populations)
msm_cluster_eq_pops = apo_populations
msm_cluster_deltaG = apo_deltaG
msm_cluster_eq_pops_df = apo_eq_pops_df
new_populations = copy.deepcopy(aggregate_docking_msm)
for ligand in aggregate_docking_msm.columns.values:
new_populations[ligand] = np.exp(-1.0*(-1.0*aggregate_docking_msm[ligand].values+msm_cluster_deltaG)/0.61)
Z = np.sum(new_populations.values, axis=0)
for j, ligand in enumerate(aggregate_docking_msm.columns.values):
new_populations[ligand] = new_populations[ligand].values / Z[j]
population_deltas = copy.deepcopy(new_populations)
for ligand in aggregate_docking_msm.columns.values:
population_deltas[ligand] = population_deltas[ligand].values / msm_cluster_eq_pops
new_energies = copy.deepcopy(new_populations)
for ligand in aggregate_docking_msm.columns.values:
new_energies[ligand] = -.61 * np.log(new_populations[ligand])
delta_delta_g = copy.deepcopy(new_energies)
for ligand in aggregate_docking_msm.columns.values:
delta_delta_g[ligand] = new_energies[ligand].values - msm_cluster_deltaG
docking_normalized = copy.deepcopy(aggregate_docking_msm)
#docking_normalized[docking_normalized.columns.values] = scale(docking_normalized.values)
ddg_scaled = copy.deepcopy(delta_delta_g)
#ddg_scaled[delta_delta_g.columns.values] = scale(delta_delta_g.values)
deltas_tica = pd.concat([delta_delta_g, samples_tica_avg_df, samples_pnas_avg_df, samples_top_features_avg_df], axis=1)
total_samples = 10000
bi_msm = msm_obj
num_trajs = len(get_trajectory_files(traj_dir, traj_ext))
lig_features_eq = {}
#lig_features_eq = msm_reweighted_features_per_ligand(feature_dfs, new_populations, bi_msm,
# total_samples, clusters_map, num_trajs, apo_populations, save_dir)
features = delta_delta_g.transpose()
null_features = reference_docking.transpose().loc[features.index]
classes = pd.read_csv("/home/enf/b2ar_analysis/b2ar_antagonists_agonists3.csv", header=None)
agonists = classes.iloc[1].dropna().values.tolist()
agonists = [''.join(e for e in lig if e.isalnum() or e=='-' or e=='_') for lig in agonists]
antagonists = classes.iloc[0].dropna().values.tolist()
antagonists = [''.join(e for e in lig if e.isalnum() or e=='-' or e=='_') for lig in antagonists]
labels = np.zeros((features.shape[0], 1), dtype=object)
for agonist in agonists:
try:
labels[features.index.values.tolist().index(agonist), 0] = "agonist"
except:
print(agonist)
continue
for agonist in antagonists:
try:
labels[features.index.values.tolist().index(agonist), 0] = "antagonist"
except:
print(agonist)
continue
non_zero_inds = np.where(labels != 0)[0]
X = features.values[non_zero_inds,:]
N = -1.0 * null_features.values[non_zero_inds,2]
C = N
y = labels[non_zero_inds, :]
y = label_binarize(y, ["agonist", "antagonist"])
return apo_populations, df_agg, aggregate_docking_msm, docking_normalized, ddg_scaled, deltas_tica, delta_delta_g, lig_features_eq, new_populations, bi_msm, num_trajs, features, null_features, classes, agonists, antagonists, labels, X, N, C, y
def make_clustermap(delta_delta_g, tica_dir, n_clusters, msm_lag_time, n_components, precision, null_features):
"""
samples_tica = pd.read_csv(tica_coords_csv, index_col=0)
samples_docking = pd.read_csv(docking_multiple_ligands, index_col=0)
common_indices = list(set(samples_docking.index.values).intersection(samples_tica.index.values))
samples_tica = samples_tica.loc[common_indices]
samples_docking = samples_docking.loc[common_indices]
pearson_matrix = np.zeros((samples_docking.shape[1], samples_tica.shape[1]))
for i in range(0, pearson_matrix.shape[0]):
for j in range(0, pearson_matrix.shape[1]):
pearson_matrix[i][j] = pearsonr(samples_docking.values[:,i], samples_tica.values[:,j])[0]
MI_matrix = np.abs(compute_sr_matrix(samples_docking.values, samples_tica.values))
"""
plt.clf()
#first_entries = ["nebivolol", "s-carvedilol", "s-carazolol", "s-atenolol", "xamoterol", "3p0g_lig", "isoetharine", "ethylnorepinephrine", "salbutamol", "norepinephrine"]
secret_compounds = [c for c in delta_delta_g.columns.values if "Compound" in c]
#drug_order = first_entries + list(set(delta_delta_g.columns.values).difference(set(first_entries)).difference(set(secret_compounds)))
#delta_delta_g = delta_delta_g[drug_order]
#delta_delta_g.sort("nebivolol", inplace=True)
#plot_heatmap(scale(delta_delta_g.values).T, delta_delta_g.columns.values, delta_delta_g.index.values, save_file="%s/msm_n-clusters%d_lag-time%d_n-heatmap.eps" %(tica_dir, n_clusters, msm_lag_time))
#plot_heatmap(MI_matrix, samples_docking.columns.values, samples_tica.columns.values, save_file="%s/msm_n-clusters%d_lag-time%d_tICs%d.eps" %(tica_dir, n_clusters, msm_lag_time, n_components))
ddg_scaled = copy.deepcopy(delta_delta_g)
ddg_scaled[delta_delta_g.columns.values] = scale(delta_delta_g.values)
#ddg_scaled.index = [n.split("cluster")[1] for n in ddg_scaled.index.values]
#plot_clustermap(docking_normalized[["nebivolol", "terbutaline", "s-carvedilol", "Ici118551", "s-atenolol", "propranolol", "bisoprolol", "s-carazolol", "timolol", "procaterol", "r_isopreterenol", "norepinephrine", "r_epinephrine", "ethylnorepinephrine", "isoetharine", "N-Cyclopentylbutanephrine", "3p0g_lig", "fenoterol", "formoterol"]].loc[["cluster80", "cluster16", "cluster99", "cluster90", "cluster43", "cluster62", "cluster9", "cluster89", "cluster58", "cluster74", "cluster6"]].transpose(), save_file="%s/msm_n-clusters%d_lag-time%d_tICs%d.eps" %(tica_dir, n_clusters, msm_lag_time, n_components), method='average')
#plot_clustermap(ddg_scaled[["s-carvedilol", "s-carazolol", "alprenalol", "norepinephrine", "nebivolol", "clenbuterol", "Tulobuterol", "r_isopreterenol", "isoetharine", "formoterol", "r_epinephrine", "ethylnorepinephrine", "N-Cyclopentylbutanephrine"]].loc[importances_df.index.values.tolist()[:10]].transpose(), save_file="%s/msm_n-clusters%d_lag-time%d_tICs%d.eps" %(tica_dir, n_clusters, msm_lag_time, n_components), method='average')
plot_clustermap(pd.concat([ddg_scaled.transpose(), null_features], axis=1), save_file="%s/msm_n-clusters%d_lag-time%d_tICs%d_%s.eps" %(tica_dir, n_clusters, msm_lag_time, n_components, precision), method='average', z_score=1)
return secret_compounds, ddg_scaled
#print(deltas_tica.iloc[0:10])
"""
docking_normalized[docking_normalized.columns.values] = scale(population_deltas.values)
train_biased_antagonists = ["s-carvedilol", "nebivolol"]
train_inverse_agonists = [] #["s-carazolol", "Ici118551"]"
train_arrestin_agonists = ["isoetharine", "3p0g_lig"]
train_gprot_agonists = ["procaterol"]
train_agonists = ["r_isopreterenol"] + train_arrestin_agonists + train_gprot_agonists
indices = []
for biased_antagonist in (train_biased_antagonists):# + train_arrestin_agonists):
for inverse_agonist in train_inverse_agonists:
bias_antagonist_minus_antagonists = delta_delta_g[biased_antagonist].values - delta_delta_g[inverse_agonist].values
#bias_antagonist_minus_antagonists = scale(bias_antagonist_minus_antagonists)
indices.append(set(np.where(bias_antagonist_minus_antagonists < -0.)[0]))
indices.append(set(np.where(scale(delta_delta_g[biased_antagonist].values) <-1.)[0]))
#if train_gprot_agonists is not None:
# for biased_antagonist in train_arrestin_agonists:
# for inverse_agonist in (train_gprot_agonists):
# bias_antagonist_minus_antagonists = delta_delta_g[biased_antagonist].values - delta_delta_g[inverse_agonist].values
# #bias_antagonist_minus_antagonists = scale(bias_antagonist_minus_antagonists)
# indices.append(set(np.where(bias_antagonist_minus_antagonists < -0.)[0]))
# indices.append(set(np.where(delta_delta_g[biased_antagonist].values <0)[0]))
indices = set.intersection(*indices)
#bias_antagonist_minus_agonists = deltas_tica[[" 3p0g_lig"]].mean(axis=1).values - deltas_tica[train_agonists].mean(axis=1).values
#bias_antagonist_minus_agonists = scale(bias_antagonist_minus_agonists)
#indices = list(set(np.where(bias_antagonist_minus_antagonists < -.5)[0]))#.tolist()).intersection(set(np.where(bias_antagonist_minus_antagonists > 1.)[0].tolist())))
biased_antagonist_states = deltas_tica.iloc[list(indices)]#.intersection(set(np.where(np.max(scale(deltas_tica[train_biased_antagonists].values),axis=1) < -.5)[0])))]
print("biased antagonist states")
print(indices)
#biased_antagonist_states = biased_antagonist_states.loc[biased_antagonist_states["tm6_tm3_dist"] > 12.]
indices = []
for biased_antagonist in train_agonists:
for inverse_agonist in (train_inverse_agonists):
bias_antagonist_minus_antagonists = delta_delta_g[biased_antagonist].values - delta_delta_g[inverse_agonist].values
bias_antagonist_minus_antagonists = scale(bias_antagonist_minus_antagonists)
indices.append(set(np.where(bias_antagonist_minus_antagonists < -0.)[0]))
#indices.append(set(np.where(delta_delta_g[inverse_antagonist].values > -.5)[0]))
indices.append(set(np.where(delta_delta_g[biased_antagonist].values <-0.)[0]))
indices = set.intersection(*indices)
agonist_states = deltas_tica.iloc[list(indices)]#.intersection(set(np.where(np.max(scale(deltas_tica[train_biased_antagonists].values),axis=1) < -.5)[0])))]
#agonist_states = agonist_states.loc[agonist_states["tm6_tm3_dist"] > 12.]
print("agonist states:")
print(indices)
indices = []
for biased_antagonist in train_arrestin_agonists:
for inverse_agonist in (train_gprot_agonists):
bias_antagonist_minus_antagonists = agonist_states[biased_antagonist].values - agonist_states[inverse_agonist].values
bias_antagonist_minus_antagonists = scale(bias_antagonist_minus_antagonists)
indices.append(set(np.where(bias_antagonist_minus_antagonists < -0.)[0]))
#indices.append(set(np.where(delta_delta_g[inverse_antagonist].values > -.5)[0]))
indices.append(set(np.where(agonist_states[biased_antagonist].values <-0.)[0]))
indices = set.intersection(*indices)
arrestin_agonist_states = agonist_states.iloc[list(indices)]#.intersection(set(np.where(np.max(scale(deltas_tica[train_biased_antagonists].values),axis=1) < -.5)[0])))]
print("arrestin agonist states:")
print(indices)
indices = []
for biased_antagonist in train_gprot_agonists:
for inverse_agonist in (train_arrestin_agonists):
bias_antagonist_minus_antagonists = agonist_states[biased_antagonist].values - agonist_states[inverse_agonist].values
bias_antagonist_minus_antagonists = scale(bias_antagonist_minus_antagonists)
indices.append(set(np.where(bias_antagonist_minus_antagonists < -0.)[0]))
#indices.append(set(np.where(delta_delta_g[inverse_antagonist].values > -.5)[0]))
indices.append(set(np.where(agonist_states[biased_antagonist].values <-0.)[0]))
indices = set.intersection(*indices)
gprot_agonist_states = agonist_states.iloc[list(indices)]#.intersection(set(np.where(np.max(scale(deltas_tica[train_biased_antagonists].values),axis=1) < -.5)[0])))]
print("gprot agonist states:")
print(indices)
"""
def make_ligand_plots(lig_features_eq):
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["norepinephrine"]["Thr66_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["ethylnorepinephrine"]["Thr66_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["r_epinephrine"]["Asn148_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(0,3.,500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["rmsd_npxxy_active"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["s-carazolol"]["rmsd_npxxy_active"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["isoetharine"]["Asn148_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["N-Cyclopentylbutanephrine"]["Asn148_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["ethylnorepinephrine"]["Asn148_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["salbutamol"]["Asn148_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 20, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["N-Cyclopentylbutanephrine"]["tm6_tm3_dist"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["tm6_tm3_dist"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
from scipy import stats
thr_x = np.linspace(5, 50, 500)
thr_kde1 = stats.gaussian_kde(lig_features_eq["3p0g_lig"]["Thr66_Leu266"])
thr_kde2 = stats.gaussian_kde(lig_features_eq["r_isopreterenol"]["Asn148_Leu266"])
thr_dx1 = thr_kde1(thr_x)
thr_dx2 = thr_kde2(thr_x)
plt.plot(thr_x,thr_dx1-thr_dx2)
def custom_lim_finder(values):
mins = np.min(values, axis=0)
maxs = np.max(values, axis=0)
stds = np.std(values, axis=0)
custom_lims = [[mins[i] - 0.5*stds[i], maxs[i] + 0.5*stds[i]] for i in range(0,len(mins))]
return custom_lims
def construct_difference_plots():
for measurement in ["Ala59_Leu266", "Thr66_Leu266", "Asn148_Leu266"]:
"""
iso = lig_features_eq["r_isopreterenol"][measurement]
x = np.linspace(np.min(iso.values), np.max(iso.values), 500)
kde2 = stats.gaussian_kde(iso)
dx2 = kde2(x)
plt.clf()
plt.plot(x, dx2)
plt.title("Isopreterenol Eq. Population Frequency")
plt.xlabel("%s closest heavy atom distance" %str(measurement))
plt.ylabel("Eq. Population")
save_file = "%s/%s_isopreterenol_kde.eps" %(analysis_dir, measurement)
plt.savefig(save_file)
plt.clf()
plt.hist(iso, range=[np.min(iso.values), np.max(iso.values)], bins=100)
plt.title("Isopreterenol Eq. Population Frequency")
plt.xlabel("%s closest heavy atom distance" %str(measurement))
plt.ylabel("Eq. Population")
save_file = "%s/%s_isopreterenol_hist.eps" %(analysis_dir, measurement)
plt.savefig(save_file)
"""
cara = lig_features_eq["s-carazolol"][measurement]
c = np.linspace(np.min(cara.values), np.max(cara.values), 500)
kde2 = stats.gaussian_kde(cara)
dc2 = kde2(c)
"""
plt.clf()
plt.plot(c, dc2)
plt.title("s-carazolol Eq. Population Frequency")
plt.xlabel("%s closest heavy atom distance" %str(measurement))
plt.ylabel("Eq. Population")
save_file = "%s/%s_carazolol_kde.eps" %(analysis_dir, measurement)
plt.savefig(save_file)
plt.clf()
plt.hist(cara, range=[np.min(iso.values), np.max(iso.values)], bins=100)
plt.title("Carazolol Eq. Population Frequency")
plt.xlabel("%s closest heavy atom distance" %str(measurement))
plt.ylabel("Eq. Population")
save_file = "%s/%s_carazolol_hist.eps" %(analysis_dir, measurement)
plt.savefig(save_file)
"""
for ligand in ["3p0g_lig", "salbutamol", "salmeterol", "s-carvedilol", "isoetharine", "norepinephrine", "r_epinephrine", "ethylnorepinephrine", "nebivolol", "N-Cyclopentylbutanephrine"]:
save_file = "%s/%s_%s_minus_carazolol_frequency.eps" %(analysis_dir, measurement, ligand)
if os.path.exists(save_file):
continue
plt.clf()
print(ligand)
print(measurement)
kde1 = stats.gaussian_kde(lig_features_eq[ligand][measurement].dropna())
dc1 = kde1(c)
plt.plot(c,dc1-dc2)
if ligand == "3p0g_lig":
lig_title = "BI"
else:
lig_title = ligand
plt.title("%s Frequency minus Carazolol Frequency" %lig_title)
plt.xlabel("%s closest heavy atom distance" %str(measurement))
plt.ylabel("Equilibrium Population Change")
plt.savefig(save_file)
def plot_ligand_observable_difference_kde(lig_features_eq, reference_lig, observable, save_dir):
return
def hi():
return
def construct_2d_distance_plots():
#deer_distances = ["Ala59_Leu266", "Thr66_Leu266", "Asn148_Leu266", "tm6_tm3_dist", "rmsd_npxxy_active"]
ligands = ["3p0g_lig", "salbutamol", "salmeterol", "s-carvedilol", "isoetharine", "norepinephrine", "r_epinephrine", "ethylnorepinephrine", "nebivolol", "N-Cyclopentylbutanephrine"]
deer_distances = ["tm6_tm3_dist", "rmsd_npxxy_active", "rmsd_npxxy_inactive", "Ala59_Leu266", "Thr66_Leu266", "Asn148_Leu266"]
#deer_distances = ["tm6_tm3_dist", "rmsd_npxxy_active"]
all_apo_data = lig_features_eq["s-carazolol"][deer_distances].values
for ligand in ligands:
jointplots(lig_features_eq[ligand][deer_distances].values, analysis_dir, titles = deer_distances, main = "%s Minus Carazolol" %ligand, refcoords = None, refcoords_j=None,
axes=None, reshape=True, data_j=None, titles_j=None, max_tIC=100, min_density=None,
custom_lims=custom_lim_finder(all_apo_data), max_diff=2.5, tpt_paths=None, tpt_paths_j=None,
n_levels=10, worker_pool=None, parallel=True, n_pts=200j, all_apo_data=all_apo_data)
def convert_compound_on_disk_to_smiles(filename):
if 1==1:
if ".sdf" in filename or ".mol" in filename:
return(''.join(convert_sdf_to_smiles(filename).replace('\'', '').replace("\\n", "")))
elif ".smi" in filename:
with open(filename, "r") as f:
smiles = ''.join(f.read().replace('\n', '').replace('\'', '').replace("\\n", ''))
return(smiles)
else:
return("")
def convert_compound_in_dir_to_smiles(compound_name, directory):
sdf_file = "%s/%s.sdf" %(directory, compound_name)
mol_file = "%s/%s.mol" %(directory, compound_name)
smi_file = "%s/%s.smi" %(directory, compound_name)
if os.path.exists(sdf_file):
return(convert_compound_on_disk_to_smiles(sdf_file))
elif os.path.exists(mol_file):
return(convert_compound_on_disk_to_smiles(mol_file))
elif os.path.exists(smi_file):
return(convert_compound_on_disk_to_smiles(smi_file))
else:
return("")
def convert_compounds_in_dir_to_smiles(compound_names, directory, parallel=False, worker_pool=None):
convert_compound_in_dir_to_smiles_partial = partial(convert_compound_in_dir_to_smiles,
directory=directory)
smiles_strings = function_mapper(convert_compound_in_dir_to_smiles_partial,
parallel=parallel,
worker_pool=worker_pool,
var_list=compound_names)
return(smiles_strings)
def convert_sdf_to_smiles(sdf_file):
try:
for mol in pb.readfile("sdf", sdf_file):
return(mol.write("can"))
except:
return("")
def convert_sdfs_to_smiles(sdfs, parallel=False, worker_pool=None):
smiles_list = function_mapper(convert_sdf_to_smiles, worker_pool, parallel, sdfs)
return(smiles_list)
def convert_smiles_to_compound(smiles):
try:
c = pc.get_compounds(smiles, namespace='smiles')
pc_smiles = c[0].canonical_smiles
c2 = pc.get_compounds(pc_smiles, namespace='smiles')[0]
cid = c[0].cid
return((c[0].synonyms[0], c[0].canonical_smiles, c2.synonyms[0], cid))
except:
return(("", "", "", ""))
def convert_smiles_to_compounds(smiles, parallel=False, worker_pool=None):
compound_names_smiles = function_mapper(convert_smiles_to_compound, worker_pool, parallel, smiles)
return(compound_names_smiles)
def write_smiles_to_disk():
return
def convert_sdf_to_compound(sdf):
smiles = convert_sdf_to_smiles(sdf)
name, pc_smiles, pc_name, cid = convert_smiles_to_compound(smiles)
return((smiles, name, pc_smiles, pc_name, cid))
def convert_sdfs_to_compounds(sdfs, parallel=False, worker_pool=None):
print("Getting SMILES from SDFs...")
print("Done. Now getting compound names from SMILES...")
results = function_mapper(convert_sdf_to_compound, worker_pool, parallel, sdfs)
smiles_list = [t[0] for t in results]
names = [t[1] for t in results]
pc_smiles = [t[2] for t in results]
pc_names = [t[3] for t in results]
cids = [t[4] for t in results]
print("Done. returning compound names.")
return(smiles_list, names, pc_smiles, pc_names, cids)
"""
adapted from RDKit since there is no AUC
calculator for BedROC
"""
def calc_auc_from_roc(roc, scores, col):
TNR = roc[0]
precision = roc[1]
numMol = len(scores)
AUC = 0
# loop over score list
for i in range(0, numMol-1):
AUC += (TNR[i+1]-TNR[i]) * (precision[i+1]+precision[i])
return 0.5*AUC
def compute_auc(y_train, y_score):
fdr, precision, _ = roc_curve(y_train, y_score[:,1])
roc_auc = calculate_auc(fdr, precision)
log_auc = logauc2(fdr, precision)
return roc_auc, log_auc
def b(x, y, i):
return y[i+1] - x[i+1] * (y[i+1] - y[i]) / (x[i+1] - x[i])
def logauc(x, y, lam=0.001):
num = 0.
for i in range(0, len(x)-1):
if x[i] >= lam:
num += ((y[i+1]-y[i])/np.log(10) + b(x, y, i) * (np.log10(x[i+1]) - np.log10(x[i])))
return num / (np.log10(1./lam))
def logauc2(x, y, lam=0.001):
num = 0.
for i in range(0, len(x)-1):
if x[i] >= lam:
num += (np.log10(x[i+1]) - np.log10(x[i])) * (y[i+1]+y[i]) /2.
return num / (np.log10(1./lam))
def do_regression_experiment(features, y, feature_names, n_trials,
train_size=0.8, regularize=False, model="rfr",
normalize=False, normalize_axis0=True, worker_pool=None,
parallel=True):
test_r2s = []
feature_importances = []
results_dict = {}
do_single_regression_experiment_partial = partial(do_single_regression_experiment, features=features,
y=y, n_estimators=1000, train_size=train_size,
model=model, normalize=normalize, normalize_axis0=normalize_axis0)
model_results = function_mapper(do_single_regression_experiment_partial, worker_pool, parallel, list(range(0,n_trials)))
print("Finished fitting models")
results_dict['feature_importances'] = [t[0] for t in model_results]
results_dict['test_r2s'] = [t[1] for t in model_results]
return results_dict
def do_single_regression_experiment(trial, features, y,
n_estimators,
train_size=0.8,
regularize=False,
model="rfr",
normalize=True,
normalize_axis0=True):
features_y = features + [y]
train_test_arrays = train_test_split(*features_y, train_size=train_size)
y_train = train_test_arrays[2*len(features)]
y_test = train_test_arrays[2*len(features) + 1]
feature_importance = []
test_r2s = []
kendall_coefficients = []
kendall_pvalues = []
feature_importances = []
kendall_scores = []
r2_scores = []
for i in range(0, len(features)):
X_train = train_test_arrays[2*i]
X_test = train_test_arrays[2*i+1]
if normalize:
sc = StandardScaler()
sc.fit(X_train)
else:
sc = None
X_train = custom_normalize(X_train, sc, normalize_axis0)
X_test = custom_normalize(X_test, sc, normalize_axis0)
if model == "rfr":
rfr = RandomForestRegressor(n_estimators=100, max_depth=None, max_features='sqrt', n_jobs=-1)
rfr.fit(X_train, y_train)
feature_importance.append(rfr.feature_importances_)
elif model == "LassoCV":
rfr = linear_model.LassoCV(n_jobs=-1)
rfr.fit(X_train, y_train)
feature_importance.append(rfr.coef_)
elif model == "RidgeCV":
rfr = linear_model.RidgeCV()
rfr.fit(X_train, y_train)
feature_importance.append(rfr.coef_)
elif model == "SVR":
rfr = SVR()
rfr.fit(X_train, y_train)
feature_importance.append([0. for i in range(0, X_train.shape[1])])
coef, pval = scipy.stats.kendalltau(rfr.predict(X_test).ravel(), y_test.ravel())
kendall_scores.append(coef)
kendall_pvalues.append(pval)
r2_score = rfr.score(X_test, y_test)
r2_scores.append(r2_score)
feature_importances.append(feature_importance)
return((feature_importances, r2_scores))
class RegularizedModel(object):
def __init__(self, sklearn_model, input_transformer=None,
normalize_axis0=False, retained_features=None):
self.sklearn_model = sklearn_model
self.retained_features = retained_features
self.input_transformer = input_transformer
self.normalize_axis0 = normalize_axis0
def predict(self, X):
X = self.pre_regularize(X)
y_pred = self.sklearn_model.predict(X)
return(y_pred)
def predict_proba(self, X):
X = self.pre_regularize(X)
proba = self.sklearn_model.predict_proba(X)
return(proba)
def pre_regularize(self, X):
X = custom_normalize(X, self.input_transformer, self.normalize_axis0)
if self.retained_features is not None:
X = X[:, self.retained_features]
return X
class CustomSplitter(object):
def __init__(self, antagonist_inds, a_agonist_inds,
b_agonist_inds,
proportion=0.9):
self.antagonist_inds = antagonist_inds
self.a_agonist_inds = a_agonist_inds
self.b_agonist_inds = b_agonist_inds
self.proportion = proportion
def split(self, array_list):
random.shuffle(self.antagonist_inds)
random.shuffle(self.a_agonist_inds)
n_ligands = len(self.antagonist_inds) + len(self.a_agonist_inds) + len(self.b_agonist_inds)
antagonist_prop = float(len(self.antagonist_inds)) / float(n_ligands)
n_test_ligands = float(len(self.b_agonist_inds))/(1.-antagonist_prop)
n_test_antagonists = int(np.round(n_test_ligands * antagonist_prop))
test_inds = self.b_agonist_inds + self.antagonist_inds[:n_test_antagonists]
train_inds = self.antagonist_inds[n_test_antagonists:] + self.a_agonist_inds
print("train inds:")
print(train_inds,end="")
print("test_inds:")