import sys
import os
import json
from sklearn.metrics import classification_report
import pickle
if len(sys.argv) != 4:
    sys.stderr.write('Arguments error. Usage:\n')
    sys.stderr.write('\tpython evaluate.py model features output\n')
    sys.exit(1)
model_file = sys.argv[1]
matrix_file = os.path.join(sys.argv[2], 'test.pkl')
metrics_file = sys.argv[3]
with open(model_file, 'rb') as fd:
    model = pickle.load(fd)
with open(matrix_file, 'rb') as fd:
    matrix = pickle.load(fd)
labels = matrix[:, 0].toarray()
x = matrix[:, 1:]
predictions = model.predict(x)
report = classification_report(labels, predictions, target_names=[
    'negative', 'positive'], output_dict=True)
with open(metrics_file, 'w') as fd:
    fd.write(json.dumps(report, indent=2))

53f54cc801f15aec6744050430c2cd0f9f213109
<mask token>

<mask token>
if len(sys.argv) != 4:
    sys.stderr.write('Arguments error. Usage:\n')
    sys.stderr.write('\tpython evaluate.py model features output\n')
    sys.exit(1)
<mask token>
with open(model_file, 'rb') as fd:
    model = pickle.load(fd)
with open(matrix_file, 'rb') as fd:
    matrix = pickle.load(fd)
<mask token>
with open(metrics_file, 'w') as fd:
    fd.write(json.dumps(report, indent=2))

<mask token>
if len(sys.argv) != 4:
    sys.stderr.write('Arguments error. Usage:\n')
    sys.stderr.write('\tpython evaluate.py model features output\n')
    sys.exit(1)
model_file = sys.argv[1]
matrix_file = os.path.join(sys.argv[2], 'test.pkl')
metrics_file = sys.argv[3]
with open(model_file, 'rb') as fd:
    model = pickle.load(fd)
with open(matrix_file, 'rb') as fd:
    matrix = pickle.load(fd)
labels = matrix[:, 0].toarray()
x = matrix[:, 1:]
predictions = model.predict(x)
report = classification_report(labels, predictions, target_names=[
    'negative', 'positive'], output_dict=True)
with open(metrics_file, 'w') as fd:
    fd.write(json.dumps(report, indent=2))

import sys
import os
import json
from sklearn.metrics import classification_report
import pickle
if len(sys.argv) != 4:
    sys.stderr.write('Arguments error. Usage:\n')
    sys.stderr.write('\tpython evaluate.py model features output\n')
    sys.exit(1)
model_file = sys.argv[1]
matrix_file = os.path.join(sys.argv[2], 'test.pkl')
metrics_file = sys.argv[3]
with open(model_file, 'rb') as fd:
    model = pickle.load(fd)
with open(matrix_file, 'rb') as fd:
    matrix = pickle.load(fd)
labels = matrix[:, 0].toarray()
x = matrix[:, 1:]
predictions = model.predict(x)
report = classification_report(labels, predictions, target_names=[
    'negative', 'positive'], output_dict=True)
with open(metrics_file, 'w') as fd:
    fd.write(json.dumps(report, indent=2))
