import numpy as np
import sklearn
from sklearn.metrics import roc_curve, auc
import sklearn.metrics
import sklearn.cross_validation
import copy
import util
import time
import metrics as ranking_metrics
import azimuth.models.regression
import azimuth.models.ensembles
import azimuth.models.DNN
import azimuth.models.baselines
import multiprocessing
def fill_in_truth_and_predictions(truth, predictions, fold, y_all, y_pred, learn_options, test):
truth[fold]['ranks'] = np.hstack((truth[fold]['ranks'],
y_all[learn_options['rank-transformed target name']].values[test].flatten()))
truth[fold]['thrs'] = np.hstack((truth[fold]['thrs'],
y_all[learn_options['binary target name']].values[test].flatten()))
if 'raw_target_name' in learn_options.keys():
truth[fold]['raw'] = np.hstack((truth[fold]['raw'],
y_all[learn_options['raw target name']].values[test].flatten()))
predictions[fold] = np.hstack((predictions[fold], y_pred.flatten()))
return truth, predictions
def construct_filename(learn_options, TEST):
if learn_options.has_key("V"):
filename = "V%s" % learn_options["V"]
else:
filename = "offV1"
if TEST:
filename = "TEST."
filename += learn_options["method"]
filename += '.order%d' % learn_options["order"]
# try:
# learn_options["target_name"] = ".%s" % learn_options["target_name"].split(" ")[1]
# except:
# pass
filename += learn_options["target_name"]
if learn_options["method"] == "GPy":
pass
# filename += ".R%d" % opt_options['num_restarts']
# filename += ".K%s" % learn_options['kerntype']
# if learn_options.has_key('degree'):
# filename += "d%d" % learn_options['degree']
# if learn_options['warped']:
# filename += ".Warp"
elif learn_options["method"] == "linreg":
filename += "." + learn_options["penalty"]
filename += "." + learn_options["cv"]
if learn_options["training_metric"] == "NDCG":
filename += ".NDGC_%d" % learn_options["NDGC_k"]
elif learn_options["training_metric"] == "AUC":
filename += ".AUC"
elif learn_options["training_metric"] == 'spearmanr':
filename += ".spearman"
print "filename = %s" % filename
return filename
def print_summary(global_metric, results, learn_options, feature_sets, flags):
print "\nSummary:"
print learn_options
print "\t\tglobal %s=%.2f" % (learn_options['metric'], global_metric)
print "\t\tmedian %s across folds=%.2f" % (learn_options['metric'], np.median(results[0]))
print "\t\torder=%d" % learn_options["order"]
if learn_options.has_key('kerntype'): "\t\tkern type = %s" % learn_options['kerntype']
if learn_options.has_key('degree'): print "\t\tdegree=%d" % learn_options['degree']
print "\t\ttarget_name=%s" % learn_options["target_name"]
for k in flags.keys():
print '\t\t' + k + '=' + str(learn_options[k])
print "\t\tfeature set:"
for set in feature_sets.keys():
print "\t\t\t%s" % set
print "\t\ttotal # features=%d" % results[4]
def extract_fpr_tpr_for_fold(aucs, fold, i, predictions, truth, y_binary, test, y_pred):
assert len(np.unique(y_binary))<=2, "if using AUC need binary targets"
fpr, tpr, _ = roc_curve(y_binary[test], y_pred)
roc_auc = auc(fpr, tpr)
aucs.append(roc_auc)
def extract_NDCG_for_fold(metrics, fold, i, predictions, truth, y_ground_truth, test, y_pred, learn_options):
NDCG_fold = ranking_metrics.ndcg_at_k_ties(y_ground_truth[test].flatten(), y_pred.flatten(), learn_options["NDGC_k"])
metrics.append(NDCG_fold)
def extract_spearman_for_fold(metrics, fold, i, predictions, truth, y_ground_truth, test, y_pred, learn_options):
spearman = util.spearmanr_nonan(y_ground_truth[test].flatten(), y_pred.flatten())[0]
assert not np.isnan(spearman), "found nan spearman"
metrics.append(spearman)
def get_train_test(test_gene, y_all, train_genes=None):
# this is a bit convoluted because the train_genes+test_genes may not add up to all genes
# for e.g. when we load up V3, but then use only V2, etc.
not_test = (y_all.index.get_level_values('Target gene').values != test_gene)
if train_genes is not None:
in_train_genes = np.zeros(not_test.shape, dtype=bool)
for t_gene in train_genes:
in_train_genes = np.logical_or(in_train_genes, (y_all.index.get_level_values('Target gene').values == t_gene))
train = np.logical_and(not_test, in_train_genes)
else:
train = not_test
#y_all['test'] as to do with extra pairs in V2
if test_gene == 'dummy':
test = train
else:
test = (y_all.index.get_level_values('Target gene').values== test_gene)
# convert to indices
test = np.where(test == True)[0]
train = np.where(train == True)[0]
return train, test
def cross_validate(y_all, feature_sets, learn_options=None, TEST=False, train_genes=None, CV=True):
'''
feature_sets is a dictionary of "set name" to pandas.DataFrame
one set might be single-nucleotide, position-independent features of order X, for e.g.
Method: "GPy" or "linreg"
Metric: NDCG (learning to rank metric, Normalized Discounted Cumulative Gain); AUC
Output: cv_score_median, gene_rocs
When CV=False, it trains on everything (and tests on everything, just to fit the code)
'''
print "range of y_all is [%f, %f]" % (np.min(y_all[learn_options['target_name']].values), np.max(y_all[learn_options['target_name']].values))
allowed_methods = ["GPy", "linreg", "AdaBoostRegressor", "AdaBoostClassifier",
"DecisionTreeRegressor", "RandomForestRegressor",
"ARDRegression", "GPy_fs", "mean", "random", "DNN",
"lasso_ensemble", "doench", "logregL1", "sgrna_from_doench", 'SVC', 'xu_et_al']
assert learn_options["method"] in allowed_methods,"invalid method: %s" % learn_options["method"]
assert learn_options["method"] == "linreg" and learn_options['penalty'] == 'L2' or learn_options["weighted"] is None, "weighted only works with linreg L2 right now"
# construct filename from options
filename = construct_filename(learn_options, TEST)
print "Cross-validating genes..."
t2 = time.time()
y = np.array(y_all[learn_options["target_name"]].values[:,None],dtype=np.float64)
# concatenate feature sets in to one nparray, and get dimension of each
inputs, dim, dimsum, feature_names = util.concatenate_feature_sets(feature_sets)
#import pickle; pickle.dump([y, inputs, feature_names, learn_options], open("saved_models/inputs.p", "wb" )); import ipdb; ipdb.set_trace()
if not CV:
assert learn_options['cv'] == 'gene', 'Must use gene-CV when CV is False (I need to use all of the genes and stratified complicates that)'
# set-up for cross-validation
## for outer loop, the one Doench et al use genes for
if learn_options["cv"] == "stratified":
assert not learn_options.has_key("extra_pairs") or learn_options['extra pairs'], "can't use extra pairs with stratified CV, need to figure out how to properly account for genes affected by two drugs"
label_encoder = sklearn.preprocessing.LabelEncoder()
label_encoder.fit(y_all['Target gene'].values)
gene_classes = label_encoder.transform(y_all['Target gene'].values)
if 'n_folds' in learn_options.keys():
n_folds = learn_options['n_folds']
elif learn_options['train_genes'] is not None and learn_options["test_genes"] is not None:
n_folds = len(learn_options["test_genes"])
else:
n_folds = len(learn_options['all_genes'])
cv = sklearn.cross_validation.StratifiedKFold(gene_classes, n_folds=n_folds, shuffle=True)
fold_labels = ["fold%d" % i for i in range(1,n_folds+1)]
if learn_options['num_genes_remove_train'] is not None:
raise NotImplementedException()
elif learn_options["cv"]=="gene":
cv = []
if not CV:
train_test_tmp = get_train_test('dummy', y_all) # get train, test split using a dummy gene
#train_tmp, test_tmp = train_test_tmp
# not a typo, using training set to test on as well, just for this case. Test set is not used
# for internal cross-val, etc. anyway.
#train_test_tmp = (train_tmp, train_tmp)
cv.append(train_test_tmp)
fold_labels = ["dummy_for_no_cv"]#learn_options['all_genes']
elif learn_options['train_genes'] is not None and learn_options["test_genes"] is not None:
assert learn_options['train_genes'] is not None and learn_options['test_genes'] is not None, "use both or neither"
for i, gene in enumerate(learn_options['test_genes']):
cv.append(get_train_test(gene, y_all, learn_options['train_genes']))
fold_labels = learn_options["test_genes"]
# if train and test genes are seperate, there should be only one fold
train_test_disjoint = set.isdisjoint(set(learn_options["train_genes"].tolist()), set(learn_options["test_genes"].tolist()))
else:
for i, gene in enumerate(learn_options['all_genes']):
train_test_tmp = get_train_test(gene, y_all)
cv.append(train_test_tmp)
fold_labels = learn_options['all_genes']
if learn_options['num_genes_remove_train'] is not None:
for i, (train,test) in enumerate(cv):
unique_genes = np.random.permutation(np.unique(np.unique(y_all['Target gene'][train])))
genes_to_keep = unique_genes[0:len(unique_genes) - learn_options['num_genes_remove_train']]
guides_to_keep = []
filtered_train = []
for j, gene in enumerate(y_all['Target gene']):
if j in train and gene in genes_to_keep:
filtered_train.append(j)
cv_i_orig = copy.deepcopy(cv[i])
cv[i] = (filtered_train, test)
if learn_options['num_genes_remove_train']==0:
assert np.all(cv_i_orig[0]==cv[i][0])
assert np.all(cv_i_orig[1]==cv[i][1])
print "# train/train after/before is %s, %s" % (len(cv[i][0]), len(cv_i_orig[0]))
print "# test/test after/before is %s, %s" % (len(cv[i][1]), len(cv_i_orig[1]))
else:
raise Exception("invalid cv options given: %s" % learn_options["cv"])
cv = [c for c in cv] #make list from generator, so can subset for TEST case
if TEST:
ind_to_use = [0]#[0,1]
cv = [cv[i] for i in ind_to_use]
fold_labels = [fold_labels[i] for i in ind_to_use]
truth = dict([(t, dict([(m, np.array([])) for m in ['raw', 'ranks', 'thrs']])) for t in fold_labels])
predictions = dict([(t, np.array([])) for t in fold_labels])
m = {}
metrics = []
#do the cross-validation
num_proc = learn_options["num_proc"]
if num_proc > 1:
num_proc = np.min([num_proc,len(cv)])
print "using multiprocessing with %d procs--one for each fold" % num_proc
jobs = []
pool = multiprocessing.Pool(processes=num_proc)
for i,fold in enumerate(cv):
train,test = fold
print "working on fold %d of %d, with %d train and %d test" % (i, len(cv), len(train), len(test))
if learn_options["method"]=="GPy":
job = pool.apply_async(azimuth.models.GP.gp_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"]=="linreg":
job = pool.apply_async(azimuth.models.regression.linreg_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"]=="logregL1":
job = pool.apply_async(azimuth.models.regression.logreg_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"]=="AdaBoostRegressor":
job = pool.apply_async(azimuth.models.ensembles.adaboost_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options, False))
elif learn_options["method"]=="AdaBoostClassifier":
job = pool.apply_async(azimuth.models.ensembles.adaboost_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options, True))
elif learn_options["method"]=="DecisionTreeRegressor":
job = pool.apply_async(azimuth.models.ensembles.decisiontree_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"]=="RandomForestRegressor":
job = pool.apply_async(azimuth.models.ensembles.randomforest_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"]=="ARDRegression":
job = pool.apply_async(azimuth.models.regression.ARDRegression_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "random":
job = pool.apply_async(azimuth.models.baselines.random_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "mean":
job = pool.apply_async(azimuth.models.baselines.mean_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "SVC":
job = pool.apply_async(azimuth.models.baselines.SVC_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "DNN":
job = pool.apply_async(azimuth.models.DNN.DNN_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "lasso_ensemble":
job = pool.apply_async(azimuth.models.ensembles.LASSOs_ensemble_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "doench":
job = pool.apply_async(azimuth.models.baselines.doench_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "sgrna_from_doench":
job = pool.apply_async(azimuth.models.baselines.sgrna_from_doench_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
elif learn_options["method"] == "xu_et_al":
job = pool.apply_async(azimuth.models.baselines.xu_et_al_on_fold, args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options))
else:
raise Exception("did not find method=%s" % learn_options["method"])
jobs.append(job)
pool.close()
pool.join()
for i,fold in enumerate(cv):#i in range(0,len(jobs)):
y_pred, m[i] = jobs[i].get()
train,test = fold
if learn_options["training_metric"]=="AUC":
extract_fpr_tpr_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options["ground_truth_label"]].values, test, y_pred)
elif learn_options["training_metric"]=="NDCG":
extract_NDCG_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options["ground_truth_label"]].values, test, y_pred, learn_options)
elif learn_options["training_metric"] == 'spearmanr':
extract_spearman_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options["ground_truth_label"]].values, test, y_pred, learn_options)
else:
raise Exception("invalid 'training_metric' in learn_options: %s" % learn_options["training_metric"])
truth, predictions = fill_in_truth_and_predictions(truth, predictions, fold_labels[i], y_all, y_pred, learn_options, test)
pool.terminate()
else:
# non parallel version
for i,fold in enumerate(cv):
train,test = fold
if learn_options["method"]=="GPy":
y_pred, m[i] = gp_on_fold(azimuth.models.GP.feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="linreg":
y_pred, m[i] = azimuth.models.regression.linreg_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="logregL1":
y_pred, m[i] = azimuth.models.regression.logreg_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="AdaBoostRegressor":
y_pred, m[i] = azimuth.models.ensembles.adaboost_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options, classification=False)
elif learn_options["method"]=="AdaBoostClassifier":
y_pred, m[i] = azimuth.models.ensembles.adaboost_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options, classification=True)
elif learn_options["method"]=="DecisionTreeRegressor":
y_pred, m[i] = azimuth.models.ensembles.decisiontree_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="RandomForestRegressor":
y_pred, m[i] = azimuth.models.ensembles.randomforest_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="ARDRegression":
y_pred, m[i] = azimuth.models.regression.ARDRegression_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"]=="GPy_fs":
y_pred, m[i] = azimuth.models.GP.gp_with_fs_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "random":
y_pred, m[i] = azimuth.models.baselines.random_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "mean":
y_pred, m[i] = azimuth.models.baselines.mean_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "SVC":
y_pred, m[i] = azimuth.models.baselines.SVC_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "DNN":
y_pred, m[i] = azimuth.models.DNN.DNN_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "lasso_ensemble":
y_pred, m[i] = azimuth.models.ensembles.LASSOs_ensemble_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "doench":
y_pred, m[i] = azimuth.models.baselines.doench_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "sgrna_from_doench":
y_pred, m[i] = azimuth.models.baselines.sgrna_from_doench_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
elif learn_options["method"] == "xu_et_al":
y_pred, m[i] = azimuth.models.baselines.xu_et_al_on_fold(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options)
else:
raise Exception("invalid method found: %s" % learn_options["method"])
if learn_options["training_metric"]=="AUC":
# fills in truth and predictions
extract_fpr_tpr_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options['ground_truth_label']].values, test, y_pred)
elif learn_options["training_metric"]=="NDCG":
extract_NDCG_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options["ground_truth_label"]].values, test, y_pred, learn_options)
elif learn_options["training_metric"] == 'spearmanr':
extract_spearman_for_fold(metrics, fold_labels[i], i, predictions, truth, y_all[learn_options["ground_truth_label"]].values, test, y_pred, learn_options)
truth, predictions = fill_in_truth_and_predictions(truth, predictions, fold_labels[i], y_all, y_pred, learn_options, test)
print "\t\tRMSE: ", np.sqrt(((y_pred - y[test])**2).mean())
print "\t\tSpearman correlation: ", util.spearmanr_nonan(y[test], y_pred)[0]
print "\t\tfinished fold/gene %i of %i" % (i+1, len(fold_labels))
cv_median_metric =[np.median(metrics)]
gene_pred = [(truth, predictions)]
print "\t\tmedian %s across gene folds: %.3f" % (learn_options["training_metric"], cv_median_metric[-1])
t3 = time.time()
print "\t\tElapsed time for cv is %.2f seconds" % (t3-t2)
return metrics, gene_pred, fold_labels, m, dimsum, filename, feature_names
