def doMolIsoF(molNdx):
componentResults = []
xvalResults = []
portionResults = []
molName = molfiles[molNdx][1] # [molfiles[molNdx].rfind("/", 0, -1)+1:-1]
if molName in done:
return
for portion in datasetPortion:
try:
descTypes = ["usr", "esh", "es5"]
descType = descTypes[2]
if portion <= 1:
print("Loading " + str(portion * 100) + "% of " +
molfiles[molNdx][1])
else:
print("Loading " + str(portion) + " actives from " +
molfiles[molNdx][1])
(test_ds,
test_paths) = cu.loadDescriptors(molfiles[molNdx][0],
portion * 0.2,
dtype=descType,
active_decoy_ratio=-1,
selection_policy="RANDOM",
return_type="SEPERATE")
numcols = test_ds[0][0].shape[1] - 2
folds = 5
(n_fold_ds,
n_fold_paths) = cu.loadDescriptors(molfiles[molNdx][0],
portion * 0.8,
dtype=descType,
active_decoy_ratio=-1,
selection_policy="RANDOM",
return_type="SEPARATE",
exclusion_list=test_paths)
(folds_list, excl_list) = cu.split(n_fold_ds,
folds,
policy="RANDOM")
componentResults = []
for param in params:
foldResults = []
for fold in range(0, folds):
try:
val_ds = folds_list[fold]
train_ds = None
for i in range(0, folds):
if i != fold:
if train_ds is None:
train_ds = [
r[0].iloc[0:1, :]
for r in folds_list[i]
] # Take only LEC
else:
train_ds.append([
r[0].iloc[0:1, :]
for r in folds_list[i]
])
train_ds = cu.joinDataframes(train_ds)
numcols = train_ds.shape[1] - 2
clf = IsolationForest(n_estimators=param, n_jobs=-1)
train_a = train_ds[train_ds["active"] == True]
clf.fit(train_a.iloc[:, 0:numcols], None)
results = pd.DataFrame()
results["score"] = [
max(
clf.decision_function(
x[0].iloc[:, 0:numcols]).ravel())
for x in val_ds
]
results["truth"] = [x[2] for x in val_ds]
auc = eval.plotSimROC(np.array(results["truth"]),
np.array([results["score"]]), "",
None)
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]),
eval_method="sim")
foldResults.append((auc, mean_ef))
except:
foldResults.append((0, {0.01: 0, 0.05: 0}))
print("X-Validation results: ")
print(foldResults)
if len(foldResults) > 0:
mean_auc_sim = np.mean([x[0] for x in foldResults])
std_auc_sim = np.std(np.mean([x[0] for x in foldResults]))
mean_mean_ef_1pc = np.mean(
[x[1][0.01] for x in foldResults])
std_mean_ef_1pc = np.std([x[1][0.01] for x in foldResults])
mean_mean_ef_5pc = np.mean(
[x[1][0.05] for x in foldResults])
std_mean_ef_5pc = np.std([x[1][0.05] for x in foldResults])
print("mean AUC=" + str(mean_auc_sim) + ", std=" +
str(std_auc_sim) + ", mean EF(1%)=" +
str(mean_mean_ef_1pc) + ", std=" +
str(std_mean_ef_1pc) + ", mean EF(5%)=" +
str(mean_mean_ef_5pc) + ", std=" +
str(std_mean_ef_5pc))
componentResults.append(
(molName, portion, param, mean_auc_sim, std_auc_sim,
mean_mean_ef_1pc, std_mean_ef_1pc, mean_mean_ef_5pc,
std_mean_ef_5pc))
else:
print(
"X-Validation returned no results. Skipping training..."
)
componentResults.append(
(molName, portion, param, 0, 0, 0, 0, 0, 0))
except:
componentResults.append(
(molName, portion, param, 0, 0, 0, 0, 0, 0))
xvalResults.extend(componentResults)
# Find best score
aucs_rank = [x[5] for x in componentResults]
best_estimators = params[np.argmax(aucs_rank)]
print("Best-score estimators no.: " + str(best_estimators))
#train_ds = cu.lumpRecords(n_fold_ds)
train_ds = cu.joinDataframes([r[0].iloc[0:1, :] for r in n_fold_ds])
t0 = time.time()
clf = IsolationForest(n_estimators=best_estimators, n_jobs=-1)
train_a = train_ds[train_ds["active"] == True]
clf.fit(train_a.iloc[:, 0:numcols], None)
results = pd.DataFrame()
results["score"] = [
max(clf.decision_function((x[0].iloc[:, 0:numcols])))
for x in test_ds
]
results["truth"] = [x[2] for x in test_ds] #np.array(test_ds)[:, 2]
auc = eval.plotSimROC(
results["truth"], [results["score"]],
molName + "[IsoForest, " + str(portion * 100) + "%]", "results/" +
molName + "_IsoForest_sim_" + str(portion * 100) + ".pdf")
auc_rank = eval.plotRankROC(
results["truth"], [results["score"]],
molName + "[IsoForest, " + str(portion * 100) + "%]", "results/" +
molName + "_IsoForest_rank_" + str(portion * 100) + ".pdf")
auc = eval.plotSimROC(
results["truth"], [results["score"]],
molName + "[IsoForest, " + str(portion * 100) + "%]",
molName + "_IsoForest_sim_" + str(portion * 100) + ".pdf")
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]),
eval_method="sim")
print("AUC(Sim)=" + str(auc))
print("EF: ", mean_ef)
t1 = time.time()
print("Time taken = " + str(t1 - t0))
portionResults.append((molName, portion, best_estimators, auc,
auc_rank, mean_ef, t1 - t0))
print(xvalResults)
print(portionResults)
f1 = open("results/results_isoF_" + molName + ".txt", 'w')
print(xvalResults, file=f1)
print(portionResults, file=f1)
f1.close()
full_train_dss = [x[0].iloc[0:1, :] for x in test_ds]
full_train_dss.append([x[0].iloc[0:1, :] for x in n_fold_ds])
full_train_ds = cu.joinDataframes(full_train_dss)
clf = IsolationForest(n_estimators=best_estimators, n_jobs=-1)
G_a = clf.fit(full_train_ds.iloc[:, 0:numcols],
full_train_ds.iloc[:, numcols])
import pickle
mdlf = open("results/" + molName + "_IsoForest.pkl", "wb")
pickle.dump(G_a, mdlf)
mdlf.close()
print("Saved model for " + molName + " to disk")
map(
filter_decision,
svm1c.decision_function(x[0].iloc[:,
0:numcols]).ravel()))
for x in val_ds
]
results["truth"] = [x[2] for x in val_ds]
#f1 = f1_score(results["truth"], results["score"])
auc = eval.plotSimROC(np.array(results["truth"]),
np.array([results["score"]]), "", None)
auc_rank = eval.plotRankROC(np.array(results["truth"]),
np.array([results["score"]]), "", None)
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]),
eval_method="sim")
foldResults.append(auc)
print("X-Validation results: ")
print(foldResults)
if len(foldResults) > 0:
print("mean F1=" + str(np.mean(foldResults)) + ", std=" +
str(np.std(foldResults)))
componentResults.append((auc, mean_ef))
else:
print("X-Validation returned no results. Skipping training...")
componentResults.append((0, 0))
numcols = train_ds.shape[1] - 2
ann = MLPRegressor()
ann.fit(train_ds.iloc[:, 0:numcols],
((train_ds["active"])).astype(int) * 100)
results = pd.DataFrame()
results["score"] = [
max(ann.predict(x[0].iloc[:, 0:numcols])) for x in val_ds
]
results["truth"] = [x[2] for x in val_ds]
auc = eval.plotSimROC(np.array(results["truth"]),
np.array([results["score"]]), "", None)
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]))
foldResults.append((auc, mean_ef))
print("X-Validation results: ")
print(foldResults)
if len(foldResults) > 0:
mean_auc_sim = np.mean([x[0] for x in foldResults])
std_auc_sim = np.std(np.mean([x[0] for x in foldResults]))
mean_mean_ef_1pc = np.mean([x[1][0.01] for x in foldResults])
std_mean_ef_1pc = np.std([x[1][0.01] for x in foldResults])
mean_mean_ef_5pc = np.mean([x[1][0.05] for x in foldResults])
std_mean_ef_5pc = np.std([x[1][0.05] for x in foldResults])
print("mean AUC=" + str(mean_auc_sim) + ", std=" +
str(std_auc_sim) + ", mean EF(1%)=" + str(mean_mean_ef_1pc) +
simobj_es5 = scls.USRMoleculeSimParallel(sim_es5_ds, sim_paths_es5)
usr_results_es5 = np.array(simobj_es5.runScreening(50)).transpose()
#plotSimROC(sim_es5_ds, usr_results_es5, "es5_plot_"+molfiles[molNdx][1]+".pdf")
# (auc_es5, mean_ef_es5) = eval.plotSimROC([l[2] for l in sim_ds], usr_results_es5,
# molName + "ElectroShape 5-d results",
# "es5_plot_"+molName + ".pdf")
auc_es5 = eval.plotSimROC([l[2] for l in sim_ds], usr_results_es5,
molName + " ElectroShape 5-d Sim ROC",
"results/es5_sim_" + molName + ".pdf")
auc_rank_es5 = eval.plotRankROC(
[l[2] for l in sim_ds], usr_results_es5,
molName + " ElectroShape 5-d Rank ROC",
"results/es5_rank_" + molName + ".pdf")
mean_ef_es5 = eval.getMeanEFs([l[2] for l in sim_ds],
usr_results_es5)
t1 = time.time()
t_es5 = t1 - t0
except:
print("Error processing Electroshape 5-d for " +
molfiles[molNdx][1])
auc_es5 = 0
auc_rank_es5 = 0
mean_ef_es5 = 0
resultLine = [
molName, portion, 0, 0, 0, 0, 0, 0, 0, 0, auc_es5, auc_rank_es5,
mean_ef_es5, t_es5
]
portionResults.append(resultLine)
def doMolGMM(molNdx):
portionResults=[]
xvalResults=[]
molName = molfiles[molNdx][1] # [molfiles[molNdx].rfind("/", 0, -1)+1:-1]
for portion in datasetPortion:
# try:
descTypes = ["usr", "esh", "es5"]
descType = descTypes[1]
if portion <= 1:
print("Loading " + str(portion * 100) + "% of " + molfiles[molNdx][1])
else:
print("Loading " + str(portion) + " actives from " + molfiles[molNdx][1])
(test_ds, test_paths) = cu.loadDescriptors(molfiles[molNdx][0], portion * 0.2, dtype=descType,
active_decoy_ratio=-1, selection_policy="RANDOM",
return_type="SEPERATE")
numcols = test_ds[0][0].shape[1] - 2
componentsValues = [1, 10, 50, 100, 1000]
folds = 5
(n_fold_ds, n_fold_paths) = cu.loadDescriptors(molfiles[molNdx][0], portion * 0.8, dtype=descType,
active_decoy_ratio=-1,
selection_policy="RANDOM", return_type="SEPARATE",
exclusion_list=test_paths)
(folds_list, excl_list) = cu.split(n_fold_ds, folds, policy="RANDOM")
componentResults = []
for components in componentsValues:
foldResults = []
for fold in range(0, folds):
val_ds = folds_list[fold]
train_ds = None;
for i in range(0, folds):
if i != fold:
if train_ds is None:
train_ds = [r[0].iloc[0:1,:] for r in folds_list[i]]
else:
train_ds.append([r[0].iloc[0:1,:] for r in folds_list[i]])
train_ds = cu.joinDataframes(train_ds)
numcols = train_ds.shape[1] - 2
train_a = train_ds[train_ds["active"] == True]
# train_d = train_ds[train_ds["active"]==False]
if len(train_a) > components:
# print("Generating GMM for actives...")
G_a = GaussianMixture(n_components=components, covariance_type="full").fit(
train_a.iloc[:, 0:numcols], train_a.iloc[:, numcols])
results = pd.DataFrame()
print(numcols)
results["a_score"] = [G_a.score(x[0].iloc[:, 0:numcols]) for x in
val_ds] # map(lambda x: G_a.score(x[0].iloc[:, 0:12]), test_ds)
results["truth"] = [x[2] for x in val_ds] # np.array(val_ds)[:,2]
auc = eval.plotSimROC(np.array(results["truth"]), np.array([results["a_score"]]), "", None)
mean_ef = eval.getMeanEFs(np.array(results["truth"]), np.array([results["a_score"]]))
foldResults.append((auc, mean_ef))
else:
print("Training samples(" + str(len(train_a)) + ") < GMM components(" + str(
components) + ") -> cannot train.")
break
# foldResults.append(0)
print("X-Validation results, num components = " + str(components) + ": ")
print(foldResults)
if len(foldResults) > 0:
mean_auc_sim = np.mean([x[0] for x in foldResults])
std_auc_sim = np.std(np.mean([x[0] for x in foldResults]))
mean_mean_ef_1pc = np.mean([x[1][0.01] for x in foldResults])
std_mean_ef_1pc = np.std([x[1][0.01] for x in foldResults])
mean_mean_ef_5pc = np.mean([x[1][0.05] for x in foldResults])
std_mean_ef_5pc = np.std([x[1][0.05] for x in foldResults])
print("mean AUC=" + str(mean_auc_sim) +
", std=" + str(std_auc_sim) +
", mean EF(1%)=" + str(mean_mean_ef_1pc) +
", std=" + str(std_mean_ef_1pc) +
", mean EF(5%)=" + str(mean_mean_ef_5pc) +
", std=" + str(std_mean_ef_5pc))
componentResults.append((molName, portion, components, mean_auc_sim, std_auc_sim, mean_mean_ef_1pc,
std_mean_ef_1pc, mean_mean_ef_5pc, std_mean_ef_5pc))
else:
print(
"X-Validation returned no results for " + str(components) + " components. Skipping training...")
componentResults.append((molName, portion, components, 0, 0, 0, 0, 0, 0))
# print(componentResults)
xvalResults.extend(componentResults)
# Find best score
aucs_rank = [x[5] for x in componentResults]
best_components = componentsValues[np.argmax(aucs_rank)]
print("Best-score compnents no.: " + str(best_components))
(train_ds, train_paths) = cu.loadDescriptors(molfiles[molNdx][0], portion * 0.8, dtype=descType,
active_decoy_ratio=0,
selection_policy="RANDOM", return_type="SEPARATE",
exclusion_list=test_paths)
train_ds = [x[0][0:1,:] for x in train_ds] #extract LECs
# molName = molfiles[molNdx][1]#[molfiles[molNdx].rfind("/", 0, -1)+1:-1]
if len(train_ds) > best_components:
t0 = time.time()
G_a = GaussianMixture(n_components=best_components, covariance_type="full").fit(
train_ds.iloc[:, 0:numcols], train_ds.iloc[:, numcols])
results = pd.DataFrame()
results["a_score"] = [G_a.score(x[0].iloc[:, 0:numcols]) for x in test_ds]
results["truth"] = [x[2] for x in test_ds] # np.array(test_ds)[:, 2]
auc = eval.plotSimROC(results["truth"], [results["a_score"]],
molName + "[GMM-" + str(components) + " components(Similarity), " + str(
portion * 100) + "%]",
"results/"+molName + "_GMM_sim_" + str(components) + "_" + str(portion * 100) + "_LEC.pdf")
auc_rank = eval.plotRankROC(results["truth"], [results["a_score"]],
molName + "[GMM-" + str(components) + " components(Similarity), " + str(
portion * 100) + "%]",
"results/"+molName + "_GMM_sim_" + str(components) + "_" + str(portion * 100) + "_LEC.pdf")
mean_ef = eval.getMeanEFs(np.array(results["truth"]), np.array([results["a_score"]]))
t1 = time.time()
else:
auc = 0
mean_ef = 0
print("Final results, num components = ", str(components) + ": ")
print("AUC=" + str(auc))
print("EF: ", mean_ef)
portionResults.append((molName, portion, best_components, auc, auc_rank, mean_ef, t1 - t0))
# except:
# print("Exception")
# portionResults.append((molName, portion, 0, 0, 0, 0, 0))
f1 = open("results/results_gmm_"+molName+"_LEC.txt", 'w')
print(xvalResults, file=f1)
print(portionResults, file=f1)
f1.close()
full_train_dss = [x[0].iloc[0:1,:] for x in test_ds]
full_train_dss.append([x[0].iloc[0:1,:] for x in n_fold_ds])
full_train_ds = cu.joinDataframes(full_train_dss)
G_a = GaussianMixture(n_components=best_components, covariance_type="full").fit(
full_train_ds.iloc[:, 0:numcols],
full_train_ds.iloc[:, numcols])
import pickle
mdlf = open("results/"+molName + "_GMM_LEC.pkl", "wb")
pickle.dump(G_a, mdlf)
mdlf.close()
print("Saved model for " + molName + " to disk")
def doMolGMM(molNdx):
componentResults = []
portionResults = []
molName = molfiles[molNdx][1] # [molfiles[molNdx].rfind("/", 0, -1)+1:-1]
for portion in datasetPortion:
t0 = time.time()
descTypes = ["usr", "esh", "es5"]
descType = descTypes[1]
if portion <= 1:
print("Loading " + str(portion * 100) + "% of " +
molfiles[molNdx][1])
else:
print("Loading " + str(portion) + " actives from " +
molfiles[molNdx][1])
(test_ds, test_paths) = cu.loadDescriptors(molfiles[molNdx][0],
portion * 0.2,
dtype=descType,
active_decoy_ratio=-1,
selection_policy="RANDOM",
return_type="SEPERATE")
numcols = test_ds[0][0].shape[1] - 2
folds = 3
(n_fold_ds,
n_fold_paths) = cu.loadDescriptors(molfiles[molNdx][0],
portion * 0.8,
dtype=descType,
active_decoy_ratio=-1,
selection_policy="RANDOM",
return_type="SEPARATE",
exclusion_list=test_paths)
(folds_list, excl_list) = cu.split(n_fold_ds, folds, policy="RANDOM")
foldResults = []
for fold in range(0, folds):
val_ds = folds_list[fold]
train_ds = None
for i in range(0, folds):
if i != fold:
if train_ds is None:
train_ds = [r[0] for r in folds_list[i]]
else:
train_ds.append([r[0] for r in folds_list[i]])
train_ds = cu.joinDataframes(train_ds)
numcols = train_ds.shape[1] - 2
ann = MLPRegressor(max_iter=1000, early_stopping=True)
ann.fit(train_ds.iloc[:, 0:numcols],
((train_ds["active"])).astype(int) * 100)
results = pd.DataFrame()
results["score"] = [
max(ann.predict(x[0].iloc[:, 0:numcols])) for x in val_ds
]
results["truth"] = [x[2] for x in val_ds]
auc = eval.plotSimROC(np.array(results["truth"]),
np.array([results["score"]]), "", None)
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]))
foldResults.append((auc, mean_ef))
print("X-Validation results: ")
print(foldResults)
if len(foldResults) > 0:
mean_auc_sim = np.mean([x[0] for x in foldResults])
std_auc_sim = np.std(np.mean([x[0] for x in foldResults]))
mean_mean_ef_1pc = np.mean([x[1][0.01] for x in foldResults])
std_mean_ef_1pc = np.std([x[1][0.01] for x in foldResults])
mean_mean_ef_5pc = np.mean([x[1][0.05] for x in foldResults])
std_mean_ef_5pc = np.std([x[1][0.05] for x in foldResults])
print("mean AUC=" + str(mean_auc_sim) + ", std=" +
str(std_auc_sim) + ", mean EF(1%)=" + str(mean_mean_ef_1pc) +
", std=" + str(std_mean_ef_1pc) + ", mean EF(5%)=" +
str(mean_mean_ef_5pc) + ", std=" + str(std_mean_ef_5pc))
componentResults.append(
(molName, portion, mean_auc_sim, std_auc_sim, mean_mean_ef_1pc,
std_mean_ef_1pc, mean_mean_ef_5pc, std_mean_ef_5pc))
else:
print("X-Validation returned no results. Skipping training...")
componentResults.append((molName, portion, 0, 0, 0, 0, 0, 0))
train_ds = cu.lumpRecords(n_fold_ds)
ann = MLPRegressor(max_iter=1000, early_stopping=True)
ann.fit(train_ds.iloc[:, 0:numcols],
((train_ds["active"])).astype(int) * 100)
results = pd.DataFrame()
results["score"] = [
max(ann.predict(x[0].iloc[:, 0:numcols])) for x in test_ds
]
results["truth"] = [x[2] for x in test_ds] #np.array(test_ds)[:, 2]
auc_sim = eval.plotSimROC(
results["truth"], [results["score"]],
molName + "[ANN, " + str(portion * 100) + "%]",
"results/" + molName + "_ANN_sim_" + str(portion * 100) + ".pdf")
auc_rank = eval.plotRankROC(
results["truth"], [results["score"]],
molName + "[ANN-" + str(portion * 100) + "%]",
"results/" + molName + "_ANN_rank_" + str(portion * 100) + ".pdf")
mean_ef = eval.getMeanEFs(np.array(results["truth"]),
np.array([results["score"]]))
print("AUC(Sim)=" + str(auc))
print("EF: ", mean_ef)
t1 = time.time()
portionResults.append(
(molName, portion, auc_sim, auc_rank, mean_ef, (t1 - t0)))
print("Time taken = " + str(t1 - t0))
print(componentResults)
print(portionResults)
f1 = open("results/results_ann_" + molName + ".txt", 'w')
print(componentResults, file=f1)
print(portionResults, file=f1)
f1.close()