forked from TheJacksonLaboratory/KCET
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathrunRandomForest.py
188 lines (169 loc) · 8.49 KB
/
runRandomForest.py
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
import os
import sys
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score, roc_curve, auc, precision_recall_curve, precision_score, recall_score, \
average_precision_score, confusion_matrix
import matplotlib
import matplotlib.pyplot as plt
sys.path.insert(0, os.path.abspath('..'))
from kcet import KcetDatasetGenerator, KcetRandomForest
plt.rc('axes', labelsize=18)
plt.rc('xtick', labelsize=16) # fontsize of the tick labels
plt.rc('ytick', labelsize=16) # fontsize of the tick labels
download_dir = '/home/peter/data/pubmed2vec'
ctfile = os.path.join(download_dir, "clinical_trials_by_phase.tsv")
embeddings2010 = os.path.join(download_dir, "embedding_SG_dim100_upto2010.npy")
words2010 = os.path.join(download_dir, "words_SG_upto2010.txt")
embeddings2014 = os.path.join(download_dir, "embedding_SG_dim100_upto2014.npy")
words2014 = os.path.join(download_dir, "words_SG_upto2014.txt")
if not os.path.isfile(ctfile):
raise FileNotFoundError("Could not find clinical trials file at %s" % ctfile)
if not os.path.isfile(embeddings2010):
raise FileNotFoundError("Could not find 2010 embeddings file at %s" % embeddings2010)
if not os.path.isfile(words2010):
raise FileNotFoundError("Could not find 2010 words file at %s" % words2010)
if not os.path.isfile(embeddings2014):
raise FileNotFoundError("Could not find 2014 embeddings file at %s" % embeddings2014)
if not os.path.isfile(words2014):
raise FileNotFoundError("Could not find 2014 words file at %s" % words2014)
def year_label(begin_year: int, end_year: int):
if begin_year == end_year:
return "%d" % begin_year
else:
return "%d-%d" % (begin_year, end_year)
def plot_one_auc_curve(axis, y_test, yproba, begin_year: int, end_year: int, n_pos_test):
fpr, tpr, thresholds_auc = roc_curve(y_test, yproba)
auc_roc = roc_auc_score(y_test, yproba)
yearl = year_label(begin_year, end_year)
axis.plot(fpr, tpr, label='%s (%0.2f), n=%d' % (yearl, auc_roc, n_pos_test))
return auc_roc
def plot_one_precision_recall_curve(axis, y_test, y_pred, begin_year: int, end_year: int, n_pos_test):
"""
Plot a single precision recall curve
axis: a matplotlib axis
y_test: a numpy.ndarray with known classes
y_pred: a numpy.ndarray with predictions
midyear: an integer -- the start year for our predictions
num_years_later: an integer -- how many years after midyear to go
n_pos_test: number of true examples that are positive
"""
precision, recall, thresholds = precision_recall_curve(y_test, y_pred)
# some nan values are encountered. The following replaces NaN by 0.0
precision = np.nan_to_num(precision)
recall = np.nan_to_num(recall)
thresholds = np.nan_to_num(thresholds)
auc_recall_precision = average_precision_score(y_test, y_pred)
yearl = year_label(begin_year, end_year)
my_label = '%s, (%0.2f), n=%d' % (yearl, auc_recall_precision, n_pos_test)
axis.plot(recall, precision, label=my_label)
f1_scores = 2 * recall * precision / (recall + precision)
best_threshold = thresholds[np.argmax(f1_scores)]
best_f1 = np.max(f1_scores)
precision_at_threshold = precision_score(y_test, y_pred > best_threshold)
recall_at_threshold = recall_score(y_test, y_pred > best_threshold)
return best_threshold, best_f1, precision_at_threshold, recall_at_threshold, auc_recall_precision
def rrf(targetyear: int, test_years: list, outname: str, n_pk: int, phase4: bool):
if targetyear == 2010:
embeddings = embeddings2010
words = words2010
elif targetyear == 2014:
embeddings = embeddings2010
words = words2010
else:
raise ValueError("Invalid target year {}".format(targetyear))
datagen = KcetDatasetGenerator(clinical_trials=ctfile, embeddings=embeddings, words=words, n_pk=n_pk)
krf = KcetRandomForest(data_gen=datagen, target=targetyear, embedddingfile=embeddings2010, wordsfile=words2010)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
font = {'family': 'normal', 'size': 18}
matplotlib.rc('font', **font)
pr_data = []
# We use max_ap to figure out the best place to put the legend in the PR plot
max_ap = 0
for (begin_y, end_y) in test_years:
print(".", end='')
y_pred, y_test, yproba, n_pos_train, n_neg_train, n_pos_test, n_neg_test = krf.classify(begin_year=begin_y,
end_year=end_y,
phase4=phase4)
auc_roc = plot_one_auc_curve(ax1, y_test, yproba, begin_y, end_y, n_pos_test)
thresh, fscore, precision_at_threshold, recall_at_threshold, auc_pr = plot_one_precision_recall_curve(ax2, y_test,
yproba, begin_y,
end_y, n_pos_test)
max_ap = max(max_ap, auc_pr)
if phase4:
phase = 'phase4'
else:
phase = 'allphases'
pr_data.append({"target": targetyear,
"start": begin_y,
"end": end_y,
"n_pk": n_pk,
"phase": phase,
"AUROC": auc_roc,
"threshold": thresh,
"f-score": fscore,
"average_precision": auc_pr,
"precision@threshold": precision_at_threshold,
"recall@threshold": recall_at_threshold,
"n_pos_train": n_pos_train,
"n_neg_train": n_neg_train,
"n_pos_test": n_pos_test,
"n_neg_test": n_neg_test})
ax1.set_xlabel('1-Specificity')
ax1.set_ylabel('Sensitivity')
ax1.legend(loc="lower right")
ax1.xaxis.set_tick_params(width=3)
ax1.yaxis.set_tick_params(width=3)
ax2.set_xlabel('Recall')
ax2.set_ylabel('Precision')
if max_ap > 0.5:
ax2.legend(loc="lower left")
else:
ax2.legend(loc="upper right")
fig.savefig(outname, format='PDF')
plt.clf()
return pr_data
def run_rrf2010():
year = 2010
prediction_years_by2 = [(2011, 2012), (2013, 2014), (2015, 2016), (2017, 2018), (2019, 2020)]
prediction_AY = [(2011, 2011), (2011, 2014), (2011, 2017), (2011, 2020)]
pr_data = []
for npk in [1, 2, 5, 10]:
outname = "m{}_{}_by_two_allphases.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_years_by2, outname=outname, n_pk=npk, phase4=False)
pr_data.append(dat)
outname = "m{}_{}_by_two_phase4.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_years_by2, outname=outname, n_pk=npk, phase4=True)
pr_data.append(dat)
outname = "m{}_{}_allyears_allphases.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_AY, outname=outname, n_pk=npk, phase4=False)
pr_data.append(dat)
outname = "m{}_{}_allyears_phase4.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_AY, outname=outname, n_pk=npk, phase4=True)
pr_data.append(dat)
return pr_data
def run_rrf2014():
year = 2014
prediction_by2 = [(2015, 2016), (2017, 2018), (2019, 2020)]
prediction_AY = [(2015, 2015), (2015, 2017), (2015, 2020)]
pr_data = []
for npk in [1, 2, 5, 10]:
outname = "m{}_{}_by_two_allphases.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_by2, outname=outname, n_pk=npk, phase4=False)
pr_data.append(dat)
outname = "m{}_{}_by_two_phase4.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_by2, outname=outname, n_pk=npk, phase4=True)
pr_data.append(dat)
outname = "m{}_{}_allyears_allphases.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_AY, outname=outname, n_pk=npk, phase4=False)
pr_data.append(dat)
outname = "m{}_{}_allyears_phase4.pdf".format(npk, year)
dat = rrf(targetyear=year, test_years=prediction_AY, outname=outname, n_pk=npk, phase4=True)
pr_data.append(dat)
return pr_data
prdat1 = run_rrf2010()
df1 = pd.DataFrame.from_records(prdat1)
df1.to_csv('pr2010.csv', index=False, index_label=False)
prdat2 = run_rrf2014()
df2 = pd.DataFrame.from_records(prdat2)
df2.to_csv('pr2014.csv', index=False, index_label=False)