# Copyright (c) 2017, MD2K Center of Excellence
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import pickle
import numpy as np
import pandas as pd
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import StructField, StructType, StringType, FloatType, TimestampType
schema = StructType([
StructField("user", StringType()),
StructField("timestamp", TimestampType()),
StructField("stress_probability", FloatType()),
])
[docs]@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def stress_prediction(data: object) -> object:
num_rows = len(data['rr_feature'].values)
fm = np.zeros((num_rows, 11))
for c in range(num_rows):
for k in range(11):
fm[c][k] = data['rr_feature'].values[c][k]
clf_ecg = pickle.load(open('/home/a/stress_classifier/classifier_for_ecg.p','rb'))
predicted = clf_ecg.predict_proba(fm)
df = pd.DataFrame(index = np.arange(0, len(data['timestamp'].values)), columns=['user', 'timestamp', 'stress_probability'])
user = data['user'].values[0]
for c in range(len(data['timestamp'].values)):
ts = data['timestamp'].values[c]
prob = predicted[c][1]
df.loc[c] = [user, ts, prob]
return df