def filter_features_2bit(args):
from utils import read_hdf5_dataset
logger.info('read genotypes from: ' + args.genotype_file)
genotypes = read_hdf5_dataset(args.genotype_file)
indices = None
if args.indices_file is not None:
logger.info('read indices from: ' + args.indices_file)
indices = read_hdf5_dataset(args.indices_file)
genotypes = np.take(genotypes, indices, axis=0)
logger.info('number of samples: %d' % indices.shape[0])
pvalues = {}
for phenotype_file in args.phenotype_file:
logger.info('read phenotypes from: ' + phenotype_file)
phenotypes, dataset = read_hdf5_dataset(phenotype_file,
return_name=True)
if indices is not None:
phenotypes = np.take(phenotypes, indices)
if args.metric == 'anova':
logger.info('calculate ANOVA p-values')
pvalues[dataset] = fast_anova_2bit(genotypes, phenotypes)
logger.info('save p-values to file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
for dataset in pvalues.keys():
f.create_dataset(dataset, data=pvalues[dataset])
def random_cv_split(args):
import numpy as np
import h5py
from utils import read_hdf5_single, cv_split_emaize, get_indices_table, prepare_output_file, read_hdf5_dataset
logger.info('read training indices file: ' + args.train_index_file)
train_indices_all = read_hdf5_dataset(args.train_index_file)
logger.info('read parent table file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
indices_table, mask = get_indices_table(train_indices_all, parent_table)
logger.info('create output file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
for k in range(args.n_datasets):
row_indices = np.random.choice(indices_table.shape[0],
5,
replace=False)
col_indices = np.random.choice(indices_table.shape[1],
5,
replace=False)
test_indices = np.union1d(
indices_table[row_indices, :].reshape((-1, )),
indices_table[:, col_indices].reshape((-1, )))
train_indices = np.setdiff1d(train_indices_all, test_indices)
test_indices = np.intersect1d(test_indices, train_indices_all)
train_indices = np.intersect1d(train_indices, train_indices_all)
g = f.create_group(str(k))
g.create_dataset('train', data=train_indices)
g.create_dataset('test', data=test_indices)
def test_single_snp(args):
import fastlmm
from pysnptools.snpreader import SnpData, Pheno, SnpReader
from fastlmm.association import single_snp
from utils import read_hdf5_dataset
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import fastlmm.util.util as flutil
logger.info('read phenotypes from file: ' + args.phenotype_file)
phenotypes = pd.read_table(args.phenotype_file)
iid = np.repeat(phenotypes['id'].values.astype('S')[:, np.newaxis],
2,
axis=1)
if args.sample_indices_file is not None:
logger.info('read indices from file: ' + args.sample_indices_file)
sample_indices = read_hdf5_dataset(args.sample_indices_file)
else:
sample_indices = np.nonzero(
(phenotypes['type'] == 'training').values)[0]
logger.info('read SNP file (for test): ' + args.snp_file)
test_snps = get_snpdata(iid, args.snp_file, sample_indices=sample_indices)
logger.info('read SNP file (for K0): ' + args.k0_file)
K0 = get_snpdata(iid, args.k0_file)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
df_pheno = phenotypes[phenotypes['type'] == 'training'].copy()
df_pheno['fid'] = df_pheno['id']
df_pheno['iid'] = df_pheno['id']
traits = ('trait1', 'trait2', 'trait3')
for trait in traits:
pheno_file = os.path.join(args.output_dir, 'pheno.%s.txt' % trait)
logger.info('create Pheno file: ' + pheno_file)
df_pheno[['fid', 'iid', trait]].to_csv(pheno_file,
index=False,
sep='\t',
header=False)
pheno = Pheno(pheno_file)
logger.info('run FastLMM for single SNP test for %s' % trait)
results_df = single_snp(test_snps,
pheno,
K0=K0,
count_A1=True,
GB_goal=args.GB_goal)
result_file = os.path.join(args.output_dir, 'single_snp.' + trait)
logger.info('save results to file: ' + result_file)
results_df.to_hdf(result_file, trait)
if args.manhattan:
plot_file = os.path.join(args.output_dir,
'manhattan.%s.pdf' % trait)
logger.info('create Manhattan plot: ' + plot_file)
plt.clf()
flutil.manhattan_plot(results_df.as_matrix(
["Chr", "ChrPos", "PValue"]),
pvalue_line=1e-5,
xaxis_unit_bp=False)
plt.savefig(plot_file)
def anova_linregress(args):
from utils import read_hdf5_dataset
from tqdm import tqdm
from statsmodels.sandbox.stats.multicomp import multipletests
logger.info('read genotypes from: ' + args.genotype_file)
genotypes = read_hdf5_dataset(args.genotype_file)
indices = None
if args.sample_indices_file is not None:
logger.info('read indices from: ' + args.sample_indices_file)
indices = read_hdf5_dataset(args.sample_indices_file)
genotypes = np.take(genotypes, indices, axis=1)
logger.info('number of samples: %d' % indices.shape[0])
logger.info('read phenotypes from: ' + args.phenotype_file)
phenotypes, dataset = read_hdf5_dataset(args.phenotype_file,
return_name=True)
logger.info('perform ANOVA for dataset: %s' % dataset)
if indices is not None:
phenotypes = np.take(phenotypes, indices)
if args.batch_size is not None:
slicegen = BatchSliceGenerator(genotypes.shape[0],
batch_size=args.batch_size)
outputs = []
for start, stop in tqdm(slicegen(), total=slicegen.n_batches):
outputs.append(fast_linregress(genotypes[start:stop], phenotypes))
w, b, pvalues = [np.concatenate(a) for a in zip(*outputs)]
del outputs
else:
w, b, pvalues = fast_linregress(genotypes, phenotypes)
reject, qvalues, _, _ = multipletests(pvalues,
alpha=args.alpha,
method='fdr_bh')
reject = np.nonzero(reject)[0]
logger.info('save results to file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
f.create_dataset('pvalue', data=pvalues)
f.create_dataset('slope', data=w.astype('float32'))
f.create_dataset('intercept', data=b.astype('float32'))
f.create_dataset('qvalue', data=qvalues)
f.create_dataset('reject', data=reject)
def normalize_genotypes(args):
from utils import read_hdf5_dataset, prepare_output_file
import numpy as np
import h5py
logger.info('read input file: ' + args.input_file)
X, dataset = read_hdf5_dataset(args.input_file, return_name=True)
n_snps, n_samples = X.shape
# allele frequencies
p = X.sum(axis=1).astype('float32')/n_samples
multiplier = 1.0/np.sqrt(2.0*p*(1.0 - p))
multiplier = multiplier.astype('float32')
logger.info('save mean and multipliers to output file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
f.create_dataset('mean', data=p)
f.create_dataset('multiplier', data=multiplier)
def create_gsm(args):
import h5py
import numpy as np
from utils import prepare_output_file, read_hdf5_dataset
'''
logger.info('read genomic positions from file: ' + args.genomic_pos_file)
positions = {}
with h5py.File(args.genomic_pos_file, 'r') as f:
for i in range(1, 11):
positions['chr%d'%i] = f['chr%d'%i][:]
n_snps_per_chrom = {chrom:positions[chrom].shape[0] for chrom in positions.keys()}
n_snps_total = sum(n_snps_per_chrom.values())
X = []
for chrom in positions.keys():
genotype_file = os.path.join(args.input_dir, chrom)
logger.info('read genotype file: ' + genotype_file)
with h5py.File(genotype_file, 'r') as f:
n_sel = int(np.round(args.n_snps*float(n_snps_per_chrom[chrom])/n_snps_total))
ind = np.random.choice(n_snps_per_chrom[chrom], size=n_sel)
X.append(f['data'][:][ind])
X = np.concatenate(X, axis=0).astype('float32')
'''
logger.info('read genotypes from file: ' + args.input_file)
X = read_hdf5_dataset(args.input_file).astype('float64')
logger.info('number of selected SNPs: %d' % X.shape[0])
logger.info('calculate GSM')
X -= X.mean(axis=1)[:, np.newaxis]
X_std = np.sqrt(np.sum(X**2, axis=1))
X_std[np.isclose(X_std, 0.0)] = 1.0
X = X / X_std[:, np.newaxis]
logger.info('calculate K')
K = np.dot(X.T, X)
logger.info('run SVD on X')
U, S, V = np.linalg.svd(X.T, full_matrices=False)
V = V.T
logger.info('save GSM to file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
f.create_dataset('K', data=K)
f.create_dataset('U', data=U)
f.create_dataset('S', data=S)
f.create_dataset('V', data=V)
def convert_2bit_to_minor(args):
from utils import read_hdf5_dataset, prepare_output_file
import numpy as np
import h5py
import numba
@numba.jit(nopython=True)
def _2bit_to_minor(X_2bit, X_minor):
n_snps = X_minor.shape[0]
n_samples = X_minor.shape[1]
max_freq = n_samples
for i in range(n_snps):
freq = 0
for j in range(n_samples):
count = X_2bit[i, 1, j] - X_2bit[i, 0, j] + 1
freq += count
X_minor[i, j] = count
if freq > n_samples:
for j in range(n_samples):
X_minor[i, j] = 2 - X_minor[i, j]
logger.info('read input file: ' + args.input_file)
X_2bit, dataset = read_hdf5_dataset(args.input_file, return_name=True)
n_snps, n_samples = X_2bit.shape
n_snps /= 2
logger.info('number of SNPs: %d, number of samples: %d'%(n_snps, n_samples))
X_2bit = X_2bit.reshape((n_snps, 2, n_samples))
logger.info('convert from 2bit code to minor copy numbers')
# assume that the second allele in the 2bit representation is the minor allele
# 10 -> 0, 11 -> 1, 01 -> 2
#X_minor = np.einsum('ijk,j->ik', X_2bit, np.array([-1, 1])) + 1
# swap the two alleles to make sure that the number represents a minor allele
#allele_freq = np.expand_dims(np.sum(X_2bit[:, 1, :], axis=1), axis=1)
#X_minor = np.where(allele_freq <= n_samples/2, X_minor, 2 - X_minor)
X_minor = np.empty((n_snps, n_samples), dtype='int8')
_2bit_to_minor(X_2bit, X_minor)
logger.info('save minor allele copy numbers to output file: ' + args.output_file)
prepare_output_file(args.output_file)
with h5py.File(args.output_file, 'w') as f:
f.create_dataset(dataset, data=X_minor)
def evaluate(args):
import h5py
from sklearn.metrics import r2_score, mean_squared_error
from scipy.stats import pearsonr
from utils import prepare_output_file, read_hdf5_dataset
logger.info('read prediction file: ' + args.input_file)
with h5py.File(args.input_file, 'r') as f:
y_true = f['y_true'][:]
y_pred = f['y_pred'][:]
logger.info('read sample indices file: ' + args.sample_indices_file)
indices = read_hdf5_dataset(args.sample_indices_file)
y_true = y_true[indices]
y_pred = y_pred[indices]
logger.info('save metrics file: ' + args.output_file)
prepare_output_file(args.output_file)
with open(args.output_file, 'w') as f:
f.write('r2\tmse\tpcc\n')
f.write('%f' % r2_score(y_true, y_pred))
f.write('\t%f' % mean_squared_error(y_true, y_pred))
f.write('\t%f' % pearsonr(y_true, y_pred)[0])
f.write('\n')
def run_regression(args):
import h5py
import numpy as np
from utils import read_hdf5_dataset, standardize_genotypes
from sklearn.metrics import r2_score, mean_squared_error
from scipy.stats import pearsonr
if args.gsm_file is not None:
logger.info('read GSM file: ' + args.gsm_file)
with h5py.File(args.gsm_file, 'r') as f:
U = f['U'][:]
S = f['S'][:]
U = U*S[np.newaxis, :]
U = U[:, S**2 > 0.5]
U = standardize_genotypes(U)
X = U
else:
logger.info('read genotype file: ' + args.genotype_file)
X = read_hdf5_dataset(args.genotype_file)
if args.transpose_x:
logger.info('transpose X')
X = X.T
X = standardize_genotypes(X)
logger.info('read phenotype file: ' + args.phenotype_file)
y = read_hdf5_dataset(args.phenotype_file)
logger.info('read training indices file: ' + args.train_index_file)
train_index = read_hdf5_dataset(args.train_index_file)
logger.info('read test indices file: ' + args.test_index_file)
test_index = read_hdf5_dataset(args.test_index_file)
if not os.path.exists(args.output_dir):
logger.info('create output directory: ' + args.output_dir)
os.makedirs(args.output_dir)
logger.info('use model: ' + args.model_name)
if args.model_name == 'mlp':
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras import backend as K
if K.backend() == 'tensorflow':
# replace the original get_session() function
keras.backend.tensorflow_backend.get_session.func_code = _get_session.func_code
logger.info('build the model')
model = Sequential() # Feedforward
model.add(Dense(500, input_dim=X.shape[1]))
model.add(Activation('tanh'))
model.add(Dense(100))
model.add(Activation('tanh'))
model.add(Dense(1))
optimizer = keras.optimizers.RMSprop()
model.compile(loss='mean_squared_error', optimizer=optimizer)
callbacks = [keras.callbacks.CSVLogger(os.path.join(args.output_dir, 'train_log.csv'))]
logger.info('build the model')
model.fit(X[train_index], y[train_index],
epochs=args.max_epochs,
callbacks=callbacks)
'''
logger.info('save the model')
model.save(os.path.join(args.output_dir, 'model'))'''
else:
logger.info('build the model')
import dill as pickle
if args.model_name == 'gpr':
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel
kernel = DotProduct(sigma_0=1.0)**4 + WhiteKernel()
model = GaussianProcessRegressor(kernel=kernel, optimizer=None)
elif args.model_name == 'ridge':
from sklearn.linear_model import Ridge
model = Ridge(alpha=1)
logger.info('train the model')
model.fit(X[train_index], y[train_index])
'''
logger.info('save the model')
with open(os.path.join(args.output_dir, 'model'), 'wb') as fout:
pickle.dump(model, fout)'''
logger.info('test the model')
y_pred = np.ravel(model.predict(X))
logger.info('save predictions on the test set')
fout = h5py.File(os.path.join(args.output_dir, 'predictions'), 'w')
fout.create_dataset('y_true', data=y)
fout.create_dataset('y_pred', data=y_pred)
fout.close()
for phase in ('train', 'test'):
if phase == 'train':
y_ = y[train_index]
y_pred_ = y_pred[train_index]
else:
y_ = y[test_index]
y_pred_ = y_pred[test_index]
metrics = {}
metrics['mean_squared_error'] = mean_squared_error(y_, y_pred_)
metrics['r2_score'] = r2_score(y_, y_pred_)
metrics['pearsonr'] = pearsonr(y_, y_pred_)[0]
for metric_name, metric_value in metrics.items():
logger.info('%s.%s = %f'%(phase, metric_name, metric_value))
logger.info('save metrics')
with open(os.path.join(args.output_dir, 'metrics.%s.txt'%phase), 'w') as fout:
fout.write('%s\t%f\n'%(metric_name, metric_value))
def run_metric_regressor(args):
from utils import read_hdf5_dataset
import h5py
from metric_regressor import MetricRegressor
import dill as pickle
import numpy as np
from utils import read_hdf5_single, cv_split_emaize, standardize_genotypes, get_indices_table
logger.info('read genotype file: ' + args.genotype_file)
X = read_hdf5_dataset(args.genotype_file)
if args.transpose_genotype:
X = X.T
X = standardize_genotypes(X)
logger.info('read GSM file: ' + args.gsm_file)
with h5py.File(args.gsm_file, 'r') as f:
U = f['U'][:]
S = f['S'][:]
U = U[:, S ** 2 > 0.5]
U = standardize_genotypes(U)
logger.info('read phenotype file: ' + args.phenotype_file)
y = read_hdf5_dataset(args.phenotype_file)
logger.info('read parent table file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
logger.info('read training indices file: ' + args.train_index_file)
train_index = read_hdf5_dataset(args.train_index_file)
logger.info('read test indices file: ' + args.train_index_file)
test_index = read_hdf5_dataset(args.test_index_file)
indices_table, mask = get_indices_table(train_index, parent_table)
if args.cv_type == 's1f':
train_index_list, test_index_list, s0_index_list = cv_split_emaize(indices_table, mask,
k=parent_table.shape[0], method='s1f')
elif args.cv_type == 's0':
train_index_list, test_index_list, s0_index_list = cv_split_emaize(indices_table, mask,
k1=parent_table.shape[0] / 5,
k2=parent_table.shape[1] / 5,
method='s0')
elif args.cv_type == 's1m':
train_index_list, test_index_list, s0_index_list = cv_split_emaize(indices_table, mask,
k=parent_table.shape[1], method='s1m')
else:
raise ValueError('unkown cross-validation type: %s'%args.cv_type)
logger.info('%d rows and %d columns in the indices table' % (indices_table.shape[0], indices_table.shape[1]))
logger.info('number of cross-validation folds %d' % len(train_index_list))
logger.info('number principal components to use: %d' % U.shape[1])
X = np.concatenate([X, U], axis=1)
y = y.reshape((-1, 1))
X_train, y_train = X[train_index], y[train_index]
def cv_generator(batch_size=5):
while True:
for i in range(len(train_index_list)):
train_index = train_index_list[i]
if args.cv_type == 's0':
test_index = test_index_list[i][s0_index_list[i]]
else:
test_index = test_index_list[i]
if (len(train_index) < batch_size) or (len(test_index) < batch_size):
continue
train_index = np.random.choice(train_index, size=batch_size, replace=False)
test_index = np.random.choice(test_index, size=batch_size, replace=False)
yield (X_train[train_index], X_train[test_index],
y_train[train_index], y_train[test_index])
model = MetricRegressor(input_dim=X.shape[1], hidden_dim=args.n_hidden, alpha=args.alpha,
sparse_rate=args.sparse_rate, kernel=args.kernel)
model.fit(X_train, y_train, data_generator=cv_generator(batch_size=args.batch_size),
lr=args.lr, max_iter=args.max_iter, n_batches=len(train_index_list))
y_pred = model.predict(X)
logger.info('save results to output directory: ' + args.output_dir)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_file = os.path.join(args.output_dir, 'model')
#with open(model_file, 'wb') as f:
# pickle.dump(model, f)
model.save(model_file)
pred_file = os.path.join(args.output_dir, 'predictions')
with h5py.File(pred_file, 'w') as f:
f.create_dataset('y_true', data=y)
f.create_dataset('y_pred', data=y_pred)
f.create_dataset('mses', data=model.mses_)
f.create_dataset('velocities', data=model.velocities_)
f.create_dataset('mse_grads', data=model.mse_grads_)
def plot_predictions(args):
import h5py
from utils import read_hdf5_single, prepare_output_file, read_hdf5_dataset
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['font.size'] = 12
plt.rcParams['legend.fontsize'] = 12
import numpy as np
def normalize_phenotype(x, range_pheno=4.0):
return (np.clip(x, -range_pheno, range_pheno) +
range_pheno) / 2.0 / range_pheno
logger.info('read parent table file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
logger.info('read predictions from file: ' + args.input_file)
with h5py.File(args.input_file, 'r') as f:
y_true = f['y_true'][:]
y_pred = f['y_pred'][:]
logger.info('read training indices from file: ' + args.train_indices_file)
train_index = read_hdf5_dataset(args.train_indices_file)
logger.info('read test indices from file: ' + args.test_indices_file)
test_index = read_hdf5_dataset(args.test_indices_file)
y_pred_train = np.full(y_pred.shape, np.nan)
y_pred_train[train_index] = y_pred[train_index]
y_pred_test = np.full(y_pred.shape, np.nan)
y_pred_test[test_index] = y_pred[test_index]
logger.info('create output file: ' + args.output_file)
prepare_output_file(args.output_file)
with PdfPages(args.output_file) as pdf:
fig, axes = plt.subplots(4, 1, figsize=(10, 8))
axes[0].matshow(np.take(np.ravel(normalize_phenotype(y_true)),
parent_table),
cmap=plt.cm.RdBu_r)
axes[0].set_title('True phenotypes')
axes[1].matshow(np.take(np.ravel(normalize_phenotype(y_pred)),
parent_table),
cmap=plt.cm.RdBu_r)
axes[1].set_title('Predicted phenotypes')
axes[2].matshow(np.take(np.ravel(normalize_phenotype(y_pred_train)),
parent_table),
cmap=plt.cm.RdBu_r)
axes[2].set_title('Predicted phenotypes (train)')
axes[3].matshow(np.take(np.ravel(normalize_phenotype(y_pred_test)),
parent_table),
cmap=plt.cm.RdBu_r)
axes[3].set_title('Predicted phenotypes (test)')
plt.tight_layout()
pdf.savefig(fig)
plt.clf()
fig, axes = plt.subplots(2, 3, figsize=(10, 6))
axes[0, 0].hist(y_true[~np.isnan(y_true)], bins=50)
axes[0, 0].set_title('True phenotypes')
axes[0, 1].hist(y_true[train_index], bins=50)
axes[0, 1].set_title('True phenotypes (train)')
axes[0, 2].hist(y_true[test_index], bins=50)
axes[0, 2].set_title('True phenotypes (test)')
axes[1, 0].hist(y_pred, bins=50)
axes[1, 0].set_title('Predicted phenotypes')
axes[1, 1].hist(y_pred[train_index], bins=50)
axes[1, 1].set_title('Predicted phenotypes (train)')
axes[1, 2].hist(y_pred[test_index], bins=50)
axes[1, 2].set_title('Predicted phenotypes (test)')
for i in range(2):
for j in range(3):
axes[i, j].set_xlim(-5, 5)
plt.tight_layout()
pdf.savefig(fig)
plt.clf()
fig, axes = plt.subplots(1, 3, figsize=(10, 4))
axes[0].scatter(y_true[~np.isnan(y_true)],
y_pred[~np.isnan(y_true)],
s=3)
axes[0].set_xlabel('True phenotypes')
axes[0].set_ylabel('Predicted phenotypes')
axes[0].set_title('All samples')
axes[1].scatter(y_true[train_index], y_pred[train_index], s=3)
axes[1].set_xlabel('True phenotypes')
axes[1].set_ylabel('Predicted phenotypes')
axes[1].set_title('Training samples')
axes[2].scatter(y_true[test_index], y_pred[test_index], s=3)
axes[2].set_xlabel('True phenotypes')
axes[2].set_ylabel('Predicted phenotypes')
axes[2].set_title('Training samples')
plt.tight_layout()
pdf.savefig(fig)
def select_best_subset(args):
import h5py
from tqdm import tqdm
import pandas as pd
from scipy.stats import pearsonr
from sklearn.metrics import mean_squared_error
from utils import prepare_output_file, read_hdf5_dataset
logger.info('read sample indices of test dataset from ' +
args.test_index_file)
test_index = read_hdf5_dataset(args.test_index_file)
traits = args.traits.split(',')
def iterator():
cv_type = 's1f'
for gamma in args.gammas.split(','):
for n_snps in [int(a) for a in args.n_snps.split(',')]:
for trait in traits:
for snp_set in range(args.n_groups):
filename = args.input_dir + '/gamma={gamma}/{n_snps}/{trait}/{snp_set}/{cv_type}/predictions'.format(
n_snps=n_snps,
trait=trait,
snp_set=snp_set,
cv_type=cv_type,
gamma=gamma)
yield ('random_choice', gamma, trait, n_snps, snp_set,
cv_type, filename)
def query_dict(records, **kwargs):
'''
Search for records (dicts) that match the key-value pairs
:param records: a list of dicts
:param kwargs: key-value pairs
:return: a list of records that match the query arguments
'''
results = []
for record in records:
val = True
for key in kwargs:
val = val and (record[key] == kwargs[key])
if val:
results.append(record)
return results
logger.info('read prediction results')
predictions = []
for method, gamma, trait, n_snps, snp_set, cv_type, filename in tqdm(
list(iterator())):
with h5py.File(filename, 'r') as f:
predictions.append({
'trait': trait,
'gamma': f['best_gamma'][()],
'alpha': f['best_alpha'][()],
'method': method,
'n_snps': n_snps,
'snp_set': snp_set,
'y_pred': f['y_pred'][:],
'cv_type': cv_type,
'mse_cv': np.ravel(f['mse_cv']),
'pcc_cv': np.ravel(f['pcc_cv'])
})
logger.info('summarize cross-validation metrics')
summary = []
for pred in tqdm(predictions):
summary.append(
(pred['method'], pred['gamma'], pred['alpha'], pred['trait'],
pred['n_snps'], pred['snp_set'], pred['cv_type'],
np.min(pred['pcc_cv']), np.min(pred['mse_cv']),
np.mean(pred['pcc_cv']), np.mean(pred['mse_cv']),
np.max(pred['pcc_cv']), np.max(pred['mse_cv']),
np.median(pred['pcc_cv']), np.median(pred['mse_cv'])))
summary = pd.DataFrame.from_records(
summary,
columns=('method', 'gamma', 'alpha', 'trait', 'n_snps', 'snp_set',
'cv_type', 'pcc_cv_min', 'mse_cv_min', 'pcc_cv_mean',
'mse_cv_mean', 'pcc_cv_max', 'mse_cv_max', 'pcc_cv_median',
'mse_cv_median'))
ascending = args.by.startswith('mse')
summary_best = summary.sort_values(['trait', args.by],
ascending=ascending).groupby(['trait'
]).head(3)
if not os.path.exists(args.output_dir):
logger.info('create output directory: ' + args.output_dir)
os.makedirs(args.output_dir)
summary_file = os.path.join(args.output_dir, 'summary.txt')
logger.info('write summary of all SNP subsets to file: ' + summary_file)
summary.to_csv(summary_file, sep='\t', index=False)
summary_best_file = os.path.join(args.output_dir, 'summary_best.txt')
logger.info('write summary of best SNP subsets to file: ' +
summary_best_file)
summary_best.to_csv(summary_best_file, sep='\t', index=False)
logger.info('extract predictions from best SNP subsets')
rank = {}
for trait in traits:
rank[trait] = 0
for index, record in summary_best.iterrows():
pred_file = args.input_dir + '/gamma={gamma:.2f}/{n_snps}/{trait}/{snp_set}/{cv_type}/predictions'.format(
**record.to_dict())
with h5py.File(pred_file, 'r') as f:
y_pred = f['y_pred'][:]
trait = record['trait']
test_pred_file = os.path.join(
args.output_dir, 'prediction.%s.%d.txt' % (trait, rank[trait]))
logger.info('save test predictions to file: ' + test_pred_file)
np.savetxt(test_pred_file, y_pred[test_index])
rank[trait] += 1
def single_model(args):
import h5py
import pandas as pd
import numpy as np
import dill as pickle
from utils import read_hdf5_dataset, prepare_output_file, read_hdf5_single
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score, mean_squared_error
from tqdm import tqdm
logger.info('read phenotypes from file: ' + args.phenotype_file)
#phenotypes = pd.read_table(args.phenotype_file)
phenotypes = read_hdf5_dataset(args.phenotype_file)
logger.info('read genotypes from file: ' + args.genotype_file)
X = read_hdf5_dataset(args.genotype_file)
if args.transpose_x:
logger.info('transpose X')
X = X.T
y = phenotypes
if args.feature_indices_file:
logger.info('read feature indices from: ' + args.feature_indices_file)
feature_indices = read_hdf5_dataset(args.feature_indices_file)
X = np.take(X, feature_indices, axis=1)
if args.normalize_x:
logger.info('normalize X')
X = StandardScaler().fit_transform(X)
if args.sample_indices_file:
logger.info('read sample indices from: ' + args.sample_indices_file)
sample_indices = read_hdf5_dataset(args.sample_indices_file)
else:
sample_indices = np.nonzero(~np.isnan(phenotypes))[0]
X_train = X[sample_indices]
y_train = y[sample_indices]
logger.info('read parent table from file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
logger.info('use model ' + args.model_name)
logger.info('X.shape = %s, y.shape = %s' % (repr(X.shape), repr(y.shape)))
if args.model_name == 'ridge':
from sklearn.linear_model import Ridge
model = Ridge(alpha=10000)
model.fit(X_train, y_train)
y_pred = np.ravel(model.predict(X))
y_pred_train = y_pred[sample_indices]
elif args.model_name == 'ridge_cv':
from sklearn.linear_model import Ridge
alphas = 10.0**np.arange(1, 6)
train_masks, test_masks = generate_cv_masks(sample_indices,
parent_table,
k_female=5,
k_male=5)
cv_metrics = {}
cv_metrics['mse'] = np.zeros((len(alphas), train_masks.shape[0]))
cv_metrics['r2'] = np.zeros((len(alphas), train_masks.shape[0]))
pbar = tqdm(total=len(alphas) * train_masks.shape[0])
for i, alpha in enumerate(alphas):
for j in range(train_masks.shape[0]):
model = Ridge(alpha=alpha)
model.fit(X[train_masks[j]], y[train_masks[j]])
y_pred = model.predict(X[test_masks[j]])
cv_metrics['mse'][i, j] = mean_squared_error(
y[test_masks[j]], y_pred)
cv_metrics['r2'][i, j] = r2_score(y[test_masks[j]], y_pred)
pbar.update(1)
pbar.close()
best_alpha = alphas[cv_metrics['r2'].mean(axis=1).argmax()]
logger.info('optmized alpha = %f' % best_alpha)
model = Ridge(alpha=best_alpha)
model.fit(X_train, y_train)
y_pred = np.ravel(model.predict(X))
y_pred_train = y_pred[sample_indices]
elif args.model_name == 'gpr':
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel, RBF
kernel = RBF() + WhiteKernel()
model = GaussianProcessRegressor(kernel=kernel)
model.fit(X_train, y_train)
logger.info('kernel params: %s' % repr(model.get_params()))
y_pred_train = np.ravel(model.predict(X_train))
y_pred = np.ravel(model.predict(X))
elif args.model_name == 'gpy':
from GPy.kern import Linear
from GPy.models import GPRegression
kernel = Linear(input_dim=2, name='linear')
model = GPRegression(X_train, y_train, kernel=kernel)
model.optimize()
else:
raise ValueError('unknown model name: ' + args.model_name)
logger.info('r2 score = %f' % r2_score(y_train, y_pred_train))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_file = os.path.join(args.output_dir, 'model')
logger.info('save model file: ' + model_file)
with open(model_file, 'wb') as f:
pickle.dump(model, f)
pred_file = os.path.join(args.output_dir, 'predictions')
logger.info('save predictions to file: ' + pred_file)
with h5py.File(pred_file, 'w') as f:
if args.output_residuals:
f.create_dataset('residual', data=(y - y_pred))
f.create_dataset('y_true', data=y)
f.create_dataset('y_pred', data=y_pred)
f.create_dataset('y_pred_train', data=y_pred_train)
f.create_dataset('indices_train', data=sample_indices)
if args.model_name == 'ridge_cv':
f.create_dataset('alpha', data=alphas)
g = f.create_group('cv_metrics')
for key in cv_metrics.keys():
g.create_dataset(key, data=cv_metrics[key])
def run_mixed_ridge(args):
from utils import read_hdf5_dataset
import h5py
from models import MixedRidge
import dill as pickle
from utils import read_hdf5_single, cv_split_emaize, standardize_genotypes, get_indices_table
logger.info('read genotype file: ' + args.genotype_file)
X = read_hdf5_dataset(args.genotype_file)
if args.transpose_genotype:
X = X.T
X = standardize_genotypes(X)
logger.info('read GSM file: ' + args.gsm_file)
with h5py.File(args.gsm_file, 'r') as f:
U = f['U'][:]
S = f['S'][:]
#U = U*S[np.newaxis, :]
U = U[:, S**2 > 0.5]
U = standardize_genotypes(U)
logger.info('read phenotype file: ' + args.phenotype_file)
y = read_hdf5_dataset(args.phenotype_file)
logger.info('read parent table file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
logger.info('read training indices file: ' + args.train_index_file)
train_index = read_hdf5_dataset(args.train_index_file)
logger.info('read test indices file: ' + args.test_index_file)
test_index = read_hdf5_dataset(args.test_index_file)
indices_table, mask = get_indices_table(train_index, parent_table)
if args.cv_type == 's1f':
if args.k is None:
k = parent_table.shape[0]
else:
k = args.k
train_index_list, test_index_list, s0_index_list = cv_split_emaize(
indices_table, mask, k=k, method='s1f')
elif args.cv_type == 's0':
train_index_list, test_index_list, s0_index_list = cv_split_emaize(
indices_table,
mask,
k1=parent_table.shape[0] / 5,
k2=parent_table.shape[1] / 5,
method='s0')
elif args.cv_type == 's1m':
if args.k is None:
k = parent_table.shape[1]
else:
k = args.k
train_index_list, test_index_list, s0_index_list = cv_split_emaize(
indices_table, mask, k=k, method='s1m')
else:
raise ValueError('unkown cross-validation type: %s' % args.cv_type)
logger.info('%d rows and %d columns in the indices table' %
(indices_table.shape[0], indices_table.shape[1]))
logger.info('number of cross-validation folds %d' % len(train_index_list))
logger.info('number principal components to use: %d' % U.shape[1])
alpha_list = [0.001, 0.01, 0.1]
gamma_list = [0.0, 0.05, 0.1, 0.15, 0.2]
alpha_list = [0.001]
gamma_list = [0.15]
alpha_list = [float(a) for a in args.alphas.split(',')]
gamma_list = [float(a) for a in args.gammas.split(',')]
metrics = {
'pcc_cv':
np.zeros((len(alpha_list), len(gamma_list), len(test_index_list))),
'mse_cv':
np.zeros((len(alpha_list), len(gamma_list), len(test_index_list)))
}
X_train, U_train, y_train = X[train_index], U[train_index], y[train_index]
n_samples_total = np.prod(parent_table.shape)
test_index_mask = np.zeros((len(test_index_list), n_samples_total),
dtype='bool')
if args.cv_type == 's0':
for i in range(len(s0_index_list)):
test_index_mask[i, train_index[s0_index_list[i]]] = True
else:
for i in range(len(test_index_list)):
test_index_mask[i, train_index[test_index_list[i]]] = True
for i, alpha in enumerate(alpha_list):
#model.optimize_grid(X[train_index], U[train_index], y[train_index])
'''
mse_cv = np.zeros(len(train_index_list))
pcc_cv = np.zeros(len(train_index_list))
for j in range(len(train_index_list)):
model.fit(X_train[train_index_list[j]],
U_train[train_index_list[j]],
y_train[train_index_list[j]],
gamma=0.1)
y_pred_cv = model.predict(X_train[test_index_list[j]],
U_train[test_index_list[j]])
mse_cv[j] = mean_squared_error(y_train[test_index_list[j]], y_pred_cv)
pcc_cv[j] = pearsonr(y_train[test_index_list[j]], y_pred_cv)[0]
logger.info('cross-validation (real) MSE = %f, %f, %f'%(np.nanmin(mse_cv), np.nanmean(mse_cv), np.nanmax(mse_cv)))
logger.info('cross-validation (real) PCC = %f, %f, %f' % (np.nanmin(pcc_cv), np.nanmean(pcc_cv), np.nanmax(pcc_cv)))
'''
for j, gamma in enumerate(gamma_list):
model = MixedRidge(alphas=alpha)
model.fit(X_train, U_train, y_train, gamma=gamma, cv=True)
mse_cv = model.kfold(test_index_list,
subset_indices=s0_index_list,
return_mean=False)
pcc_cv = model.pcc_cv
metrics['pcc_cv'][i, j] = pcc_cv
metrics['mse_cv'][i, j] = mse_cv
logger.info(
'cross-validation (fast) MSE = %f, %f, %f' %
(np.nanmin(mse_cv), np.nanmean(mse_cv), np.nanmax(mse_cv)))
logger.info(
'cross-validation (fast) PCC = %f, %f, %f' %
(np.nanmin(pcc_cv), np.nanmean(pcc_cv), np.nanmax(pcc_cv)))
logger.info('alpha=%f, gamma=%f' % (alpha, gamma))
pcc_cv_mean = np.nanmean(metrics['pcc_cv'], axis=2)
i_best = np.argmax(np.max(pcc_cv_mean, axis=1))
best_alpha = alpha_list[i_best]
best_gamma = gamma_list[np.argmax(pcc_cv_mean[i_best])]
logger.info('best model: alpha=%f, gamma=%f' % (best_alpha, best_gamma))
best_model = MixedRidge(alphas=best_alpha)
best_model.fit(X_train, U_train, y_train, gamma=best_gamma)
y_pred_best = best_model.predict(X, U)
'''
logger.info('best model on test data: pcc=%f, mse=%f'%(
pearsonr(y[test_index], y_pred_best[test_index])[0],
mean_squared_error(y[test_index], y_pred_best[test_index])))
'''
logger.info('save results to output directory: ' + args.output_dir)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
#model_file = os.path.join(args.output_dir, 'model')
#with open(model_file, 'wb') as f:
# pickle.dump(best_model, f)
pred_file = os.path.join(args.output_dir, 'predictions')
with h5py.File(pred_file, 'w') as f:
f.create_dataset('best_alpha', data=best_alpha)
f.create_dataset('best_gamma', data=best_gamma)
f.create_dataset('y_true', data=y)
f.create_dataset('y_pred', data=y_pred_best)
f.create_dataset('pcc_cv', data=metrics['pcc_cv'])
f.create_dataset('mse_cv', data=metrics['mse_cv'])
f.create_dataset('alpha_list', data=np.asarray(alpha_list))
f.create_dataset('gamma_list', data=np.asarray(gamma_list))
f.create_dataset('test_index_mask', data=test_index_mask)
def mixed_model(args):
import h5py
import numpy as np
import dill as pickle
from utils import read_hdf5_dataset, read_hdf5_single
from sklearn.metrics import r2_score, mean_squared_error
logger.info('read predictions of the first model: ' + args.input_file1)
with h5py.File(args.input_file1, 'r') as f:
X1 = f['y_pred'][:][:, np.newaxis]
logger.info('read predictions of the second model: ' + args.input_file2)
with h5py.File(args.input_file2, 'r') as f:
X2 = f['y_pred'][:][:, np.newaxis]
X = np.concatenate([X1, X2], axis=1)
logger.info('read phenotypes from file: ' + args.phenotype_file)
y = read_hdf5_dataset(args.phenotype_file)
logger.info('read parent table from file: ' + args.parent_table_file)
parent_table = read_hdf5_single(args.parent_table_file)
if args.sample_indices_file:
logger.info('read sample indices from: ' + args.sample_indices_file)
sample_indices = read_hdf5_dataset(args.sample_indices_file)
else:
sample_indices = np.nonzero(~np.isnan(y))[0]
X_train = X[sample_indices]
y_train = y[sample_indices]
logger.info('use model ' + args.model_name)
logger.info('X.shape = %s, y.shape = %s' % (repr(X.shape), repr(y.shape)))
if args.model_name == 'linear':
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X)
y_pred_train = y_pred[sample_indices]
logger.info('coefficients: ' + ', '.join([str(a)
for a in model.coef_]))
elif args.model_name == 'linear_cv':
mix_factors, mse_train, mse_test = linear_cv(X, y, sample_indices,
parent_table)
best_mix_factor = mix_factors[np.argmin(mse_test.mean(axis=1))]
logger.info('best mix factor: %f' % best_mix_factor)
X_mixed = (X[:, 0] * (1 - best_mix_factor) +
X[:, 1] * best_mix_factor)[:, np.newaxis]
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_mixed[sample_indices], y_train)
y_pred = model.predict(X_mixed)
y_pred_train = y_pred[sample_indices]
else:
raise ValueError('unknown model name: ' + args.model_name)
logger.info('r2 score = %f' % r2_score(y_train, y_pred_train))
logger.info('mse = %f' % mean_squared_error(y_train, y_pred_train))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_file = os.path.join(args.output_dir, 'model')
logger.info('save model file: ' + model_file)
with open(model_file, 'wb') as f:
pickle.dump(model, f)
pred_file = os.path.join(args.output_dir, 'predictions')
logger.info('save predictions to file: ' + pred_file)
with h5py.File(pred_file, 'w') as f:
f.create_dataset('y_true', data=y)
f.create_dataset('y_pred', data=y_pred)
f.create_dataset('y_pred_train', data=y_pred_train)
f.create_dataset('indices_train', data=sample_indices)
if args.model_name == 'linear_cv':
f.create_dataset('mse_train', data=mse_train)
f.create_dataset('mse_test', data=mse_test)
f.create_dataset('mix_factors', data=mix_factors)
