def predict(seq, aa_cut=0, percent_peptide=0, model=None, model_file=None):
assert not (model is None and model_file is None
), "you have to specify either a model or a model_file"
if model is None:
try:
with open(model_file, 'rb') as f:
model, learn_options = pickle.load(f)
except:
raise Exception("could not find model stored to file %s" %
model_file)
else:
model, learn_options = model
learn_options["V"] = 2
# Y, feature_sets, target_genes, learn_options, num_proc = setup(test=False, order=2, learn_options=learn_options, data_file=test_filename)
# inputs, dim, dimsum, feature_names = pd.concatenate_feature_sets(feature_sets)
Xdf = pandas.DataFrame(columns=[u'30mer', u'Strand'], data=[[seq, 'NA']])
gene_position = pandas.DataFrame(
columns=[u'Percent Peptide', u'Amino Acid Cut position'],
data=[[percent_peptide, aa_cut]])
feature_sets = feat.featurize_data(Xdf, learn_options, pandas.DataFrame(),
gene_position)
inputs, dim, dimsum, feature_names = util.concatenate_feature_sets(
feature_sets)
# call to scikit-learn, returns a vector of predicted values
return model.predict(inputs)[0]
def predict(seq, aa_cut=0, percent_peptide=0, model=None, model_file=None):
assert not (model is None and model_file is None), "you have to specify either a model or a model_file"
if model is None:
try:
with open(model_file, 'rb') as f:
model, learn_options = pickle.load(f)
except:
raise Exception("could not find model stored to file %s" % model_file)
else:
model, learn_options = model
learn_options["V"] = 2
# Y, feature_sets, target_genes, learn_options, num_proc = setup(test=False, order=2, learn_options=learn_options, data_file=test_filename)
# inputs, dim, dimsum, feature_names = pd.concatenate_feature_sets(feature_sets)
Xdf = pandas.DataFrame(columns=[u'30mer', u'Strand'], data=[[seq, 'NA']])
gene_position = pandas.DataFrame(columns=[u'Percent Peptide', u'Amino Acid Cut position'], data=[[percent_peptide, aa_cut]])
feature_sets = feat.featurize_data(Xdf, learn_options, pandas.DataFrame(), gene_position)
inputs, dim, dimsum, feature_names = util.concatenate_feature_sets(feature_sets)
# call to scikit-learn, returns a vector of predicted values
return model.predict(inputs)[0]
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
"""
allowed_methods = [
"GPy",
"linreg",
"AdaBoostRegressor",
"DecisionTreeRegressor",
"RandomForestRegressor",
"ARDRegression",
"GPy_fs",
"mean",
"random",
"DNN",
"lasso_ensemble",
"doench",
"logregL1",
"sgrna_from_doench",
]
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)
if not CV:
assert (
learn_options["cv"] == "gene"
), "Can only 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[
"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 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, indices=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 = 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(
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(
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(
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(
models.ensembles.adaboost_on_fold,
args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options),
)
elif learn_options["method"] == "DecisionTreeRegressor":
job = pool.apply_async(
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(
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(
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(
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(
models.baselines.mean_on_fold,
args=(feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options),
)
elif learn_options["method"] == "DNN":
job = pool.apply_async(
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(
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(
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(
models.baselines.sgrna_from_doench_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(
models.GP.feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options
)
elif learn_options["method"] == "linreg":
y_pred, m[i] = 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] = 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] = models.ensembles.adaboost_on_fold(
feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options
)
elif learn_options["method"] == "DecisionTreeRegressor":
y_pred, m[i] = 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] = 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] = 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] = 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] = 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] = models.baselines.mean_on_fold(
feature_sets, train, test, y, y_all, inputs, dim, dimsum, learn_options
)
elif learn_options["method"] == "DNN":
y_pred, m[i] = 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] = 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] = 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] = models.baselines.sgrna_from_doench_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, 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
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_splits = learn_options['n_folds']
elif learn_options['train_genes'] is not None and learn_options[
"test_genes"] is not None:
n_splits = len(learn_options["test_genes"])
else:
n_splits = len(learn_options['all_genes'])
skf = sklearn.model_selection.StratifiedKFold(n_splits=n_splits,
shuffle=True)
cv = skf.split(np.zeros(len(gene_classes), dtype=np.bool),
gene_classes)
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
proximal_5mer_counts = proximal_5mer.groupby(["proximal_5mers"]).size().reset_index()
proximal_5mer = proximal_5mer.merge(proximal_5mer_counts, on="proximal_5mers")
proximal_5mer = proximal_5mer.rename(columns={0: 'proximal_5mer_counts'})
return proximal_5mer
if __name__ == '__main__':
feature_df = pd.read_csv("../../../../../results/cleaned_c_elegans_30mers_energies.csv")
features = featurize_data(feature_df,
learn_options=learn_options,
Y=feature_df,
gene_position=feature_df)
features['proximal_5mer'] = get_proximal_5mer_feature(feature_df)
inputs, dim, dimsum, feature_names = concatenate_feature_sets(features)
doensch_df = pd.DataFrame(inputs, columns=feature_names)
feature_df = feature_df.join(doensch_df)
feature_df = feature_df.drop(axis=1, labels=['sgRNA', 'Gene target', '30mer', 'WormsInjected', 'SuccessfulInjections'])
feature_df = pd.get_dummies(feature_df).dropna(axis=0)
if any(feature_df.columns.duplicated()):
feature_df = feature_df.loc[:, ~feature_df.columns.duplicated()]
feature_df = feature_df.rename(columns={"SuccessRate": "target"})
print(feature_df.shape)
cols = feature_df.columns.tolist()
cols.append(cols.pop(cols.index('target')))
feature_df = feature_df.reindex(columns=cols)
def predict(seq, aa_cut=-1, percent_peptide=-1, model=None, model_file=None, pam_audit=True, length_audit=False, learn_options_override=None):
"""
if pam_audit==False, then it will not check for GG in the expected position
this is useful if predicting on PAM mismatches, such as with off-target
"""
print "predict function running"
# assert not (model is None and model_file is None), "you have to specify either a model or a model_file"
assert isinstance(seq, (np.ndarray)), "Please ensure seq is a numpy array"
assert len(seq[0]) > 0, "Make sure that seq is not empty"
assert isinstance(seq[0], str), "Please ensure input sequences are in string format, i.e. 'AGAG' rather than ['A' 'G' 'A' 'G'] or alternate representations"
if aa_cut is not None:
assert len(aa_cut) > 0, "Make sure that aa_cut is not empty"
assert isinstance(aa_cut, (np.ndarray)), "Please ensure aa_cut is a numpy array"
assert np.all(np.isreal(aa_cut)), "amino-acid cut position needs to be a real number"
if percent_peptide is not None:
assert len(percent_peptide) > 0, "Make sure that percent_peptide is not empty"
assert isinstance(percent_peptide, (np.ndarray)), "Please ensure percent_peptide is a numpy array"
assert np.all(np.isreal(percent_peptide)), "percent_peptide needs to be a real number"
if model_file is None:
azimuth_saved_model_dir = os.path.join(os.path.dirname(__file__), 'saved_models')
if np.any(percent_peptide == -1) or (percent_peptide is None and aa_cut is None):
print("No model file specified, using V3_model_nopos")
model_name = 'V3_model_nopos.pickle'
else:
print("No model file specified, using V3_model_full")
model_name = 'V3_model_full.pickle'
model_file = os.path.join(azimuth_saved_model_dir, model_name)
if model is None:
with open(model_file, 'rb') as f:
model, learn_options = pickle.load(f)
print model_file
print learn_options
else:
model, learn_options = model
learn_options["V"] = 2
learn_options = override_learn_options(learn_options_override, learn_options)
# Y, feature_sets, target_genes, learn_options, num_proc = setup(test=False, order=2, learn_options=learn_options, data_file=test_filename)
# inputs, dim, dimsum, feature_names = pd.concatenate_feature_sets(feature_sets)
Xdf = pandas.DataFrame(columns=[u'30mer', u'Strand'], data=zip(seq, ['NA' for x in range(len(seq))]))
if np.all(percent_peptide != -1) and (percent_peptide is not None and aa_cut is not None):
gene_position = pandas.DataFrame(columns=[u'Percent Peptide', u'Amino Acid Cut position'], data=zip(percent_peptide, aa_cut))
else:
gene_position = pandas.DataFrame(columns=[u'Percent Peptide', u'Amino Acid Cut position'], data=zip(np.ones(seq.shape[0])*-1, np.ones(seq.shape[0])*-1))
feature_sets = feat.featurize_data(Xdf, learn_options, pandas.DataFrame(), gene_position, pam_audit=pam_audit, length_audit=length_audit)
inputs, dim, dimsum, feature_names = util.concatenate_feature_sets(feature_sets)
# call to scikit-learn, returns a vector of predicted values
preds = model.predict(inputs)
# also check that predictions are not 0/1 from a classifier.predict() (instead of predict_proba() or decision_function())
unique_preds = np.unique(preds)
ok = False
for pr in preds:
if pr not in [0,1]:
ok = True
assert ok, "model returned only 0s and 1s"
return preds