-
Notifications
You must be signed in to change notification settings - Fork 51
/
rhapsody
196 lines (169 loc) · 7.09 KB
/
rhapsody
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
#!/usr/bin/env python3
import os
import time
from tqdm import tqdm
import pandas as pd
import numpy as np
from biom import load_table, Table
from biom.util import biom_open
from skbio.stats.composition import clr, centralize, closure
from skbio.stats.composition import clr_inv as softmax
import matplotlib.pyplot as plt
from scipy.stats import entropy, spearmanr
import click
from scipy.sparse import coo_matrix
import tensorflow as tf
from tensorflow.contrib.distributions import Multinomial, Normal
import datetime
from rhapsody.multimodal import MMvec, cross_validation
from rhapsody.util import onehot, rank_hits, random_multimodal, split_tables
@click.group()
def rhapsody():
pass
@rhapsody.command()
@click.option('--microbe-file',
help='Input microbial abundances')
@click.option('--metabolite-file',
help='Input metabolite abundances')
@click.option('--metadata-file', default=None,
help='Input sample metadata file')
@click.option('--training-column',
help=('Column in the sample metadata specifying which '
'samples are for training and testing.'),
default=None)
@click.option('--num-testing-examples',
help=('Number of samples to randomly select for testing'),
default=10)
@click.option('--min-feature-count',
help=('Minimum number of samples a microbe needs to be observed '
'in order to not filter out'),
default=10)
@click.option('--epochs',
help='Number of epochs to train', default=10)
@click.option('--batch_size',
help='Size of mini-batch', default=32)
@click.option('--latent_dim',
help=('Dimensionality of shared latent space. '
'This is analogous to the number of PC axes.'),
default=3)
@click.option('--input_prior',
help=('Width of normal prior for input embedding. '
'Smaller values will regularize parameters towards zero. '
'Values must be greater than 0.'),
default=1.)
@click.option('--output_prior',
help=('Width of normal prior for input embedding. '
'Smaller values will regularize parameters towards zero. '
'Values must be greater than 0.'),
default=1.)
@click.option('--arm-the-gpu', is_flag=True,
help=('Enables GPU support'),
default=False)
@click.option('--top-k',
help=('Number of top hits to compare for cross-validation.'),
default=50)
@click.option('--learning-rate',
help=('Gradient descent decay rate.'),
default=1e-1)
@click.option('--beta1',
help=('Gradient decay rate for first Adam momentum estimates'),
default=0.9)
@click.option('--beta2',
help=('Gradient decay rate for second Adam momentum estimates'),
default=0.95)
@click.option('--clipnorm',
help=('Gradient clipping size.'),
default=10.)
@click.option('--checkpoint-interval',
help=('Number of seconds before a storing a summary.'),
default=1000)
@click.option('--summary-interval',
help=('Number of seconds before a storing a summary.'),
default=1000)
@click.option('--summary-dir', default='summarydir',
help='Summary directory to save cross validation results.')
@click.option('--ranks-file', default=None,
help='Ranks file containing microbe-metabolite rankings.')
def mmvec(microbe_file, metabolite_file,
metadata_file, training_column,
num_testing_examples, min_feature_count,
epochs, batch_size, latent_dim,
input_prior, output_prior, arm_the_gpu, top_k,
learning_rate, beta1, beta2, clipnorm,
checkpoint_interval, summary_interval,
summary_dir, ranks_file):
microbes = load_table(microbe_file)
metabolites = load_table(metabolite_file)
if metadata_file is not None:
metadata = pd.read_table(metadata_file, index_col=0)
else:
metadata = None
res = split_tables(
microbes, metabolites,
metadata=metadata, training_column=training_column,
num_test=num_testing_examples,
min_samples=min_feature_count)
(train_microbes_df, test_microbes_df,
train_metabolites_df, test_metabolites_df) = res
# filter out low abundance microbes
microbe_ids = microbes.ids(axis='observation')
metabolite_ids = metabolites.ids(axis='observation')
params = []
sname = 'latent_dim_' + str(latent_dim) + \
'_input_prior_%.2f' % input_prior + \
'_output_prior_%.2f' % output_prior + \
'_beta1_%.2f' % beta1 + \
'_beta2_%.2f' % beta2
sname = os.path.join(summary_dir, sname)
n, d1 = microbes.shape
n, d2 = metabolites.shape
train_microbes_coo = coo_matrix(train_microbes_df.values)
test_microbes_coo = coo_matrix(test_microbes_df.values)
if arm_the_gpu:
# pick out the first GPU
device_name='/device:GPU:0'
else:
device_name='/cpu:0'
config = tf.ConfigProto()
with tf.Graph().as_default(), tf.Session(config=config) as session:
model = MMvec(
latent_dim=latent_dim,
u_scale=input_prior, v_scale=output_prior,
learning_rate = learning_rate,
beta_1=beta1, beta_2=beta2,
device_name=device_name,
clipnorm=clipnorm, save_path=sname)
model(session,
train_microbes_coo, train_metabolites_df.values,
test_microbes_coo, test_metabolites_df.values)
loss, cv = model.fit(epoch=epochs, summary_interval=summary_interval,
checkpoint_interval=checkpoint_interval)
U, V = model.U, model.V
d1 = U.shape[0]
U_ = np.hstack(
(np.ones((model.U.shape[0], 1)),
model.Ubias.reshape(-1, 1), U)
)
V_ = np.vstack(
(model.Vbias.reshape(1, -1),
np.ones((1, model.V.shape[1])), V)
)
np.savetxt(os.path.join(summary_dir, 'U.txt'), model.U)
np.savetxt(os.path.join(summary_dir, 'V.txt'), model.V)
np.savetxt(os.path.join(summary_dir, 'Ubias.txt'), model.Ubias)
np.savetxt(os.path.join(summary_dir, 'Vbias.txt'), model.Vbias)
if ranks_file is not None:
ranks = pd.DataFrame(
clr(softmax(np.hstack(
(np.zeros((model.U.shape[0], 1)), U_ @ V_)))),
index=train_microbes_df.columns,
columns=train_metabolites_df.columns)
# shift the reference from the first microbe to the average microbe
ranks = ranks - ranks.mean(axis=1)
params, rank_stats = cross_validation(
model, test_microbes_df, test_metabolites_df, top_N=top_k)
params.to_csv(os.path.join(summary_dir, 'model_results.csv'))
rank_stats.to_csv(os.path.join(summary_dir, 'otu_cv_results.csv'))
ranks.to_csv(ranks_file)
if __name__ == '__main__':
rhapsody()