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Changed prediction to run with multithreading #54

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Changed prediction to run with multithreading #54

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f31de4a
Changed prediction to run with multiprocessing
skjerns May 15, 2019
13ab9b1
Update Porter.py
skjerns May 15, 2019
b142360
Update Porter.py
skjerns May 15, 2019
b96071f
Update Porter.py
skjerns May 15, 2019
fed1559
Update requirements.txt
skjerns May 16, 2019
19bb498
update dependency/import
skjerns Jun 25, 2019
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1 change: 1 addition & 0 deletions requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,2 +1,3 @@
six
scikit-learn>=0.14.1
joblib
16 changes: 11 additions & 5 deletions sklearn_porter/Porter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,12 @@
import os
import sys
import types
import subprocess

import numpy as np

from joblib import Parallel, delayed, cpu_count

from sklearn.metrics import accuracy_score
from sklearn.tree.tree import DecisionTreeClassifier
from sklearn.ensemble.weight_boosting import AdaBoostClassifier
Expand Down Expand Up @@ -375,11 +378,14 @@ def predict(self, X, class_name=None, method_name=None, tnp_dir='tmp',

# Multiple feature sets:
if exec_cmd is not None and len(X.shape) > 1:
pred_y = np.empty(X.shape[0], dtype=int)
for idx, features in enumerate(X):
full_exec_cmd = exec_cmd + [str(f).strip() for f in features]
pred = Shell.check_output(full_exec_cmd, cwd=tnp_dir)
pred_y[idx] = int(pred)
tnp_dir = './' + tnp_dir
exec_cmd = [os.path.join(os.path.abspath(tnp_dir), exec_cmd[0])]
cmds = [exec_cmd + [str(f).strip() for f in feat] for feat in X]
max_threads = cpu_count()
# using threading will increase speed n-fold, depending on CPUs
preds = Parallel(n_jobs=max_threads, backend='threading') \
(delayed(subprocess.check_output)(cmd, cwd=tnp_dir) for cmd in cmds)
pred_y = np.array([int(pred) for pred in preds], dtype=int)

# Cleanup:
if not keep_tmp_dir:
Expand Down