Predicting events

Fetching data

Let us verify the model which has been trained.

Switch to Python interpreter or create a Jupyter notebook with Python kernel and load the data.

from evaics.client import HttpClient
from datetime import datetime, timedelta
import pandas as pd

client = HttpClient('http://host', user='username', password='secret')
# do not set if no ML kit server installed
# set to ML kit server host/port if HMI and ML kit server are on different
# ports
client.mlkit = True

req = client.history_df(params_csv='params_alarm.csv')

# it is recommended to perform tests on a time frame with differs from the
# one the model has been trained with
data = req.t_start( - timedelta(days=10)).t_end( - timedelta(days=5)).fill(

# pop alarm and time column as they are required for a comparison chart
# only
alarms = data.pop('alarm')
times = data.pop('time')


Here is the data frame which is used by the model to predict alarm events:

            pwr       temp
0     78.598641  18.676482
1     78.896076  18.825306
2     79.158506  18.974394
3     79.385697  19.123713
4     79.577443  19.273229
...         ...        ...
7135  79.878043  10.700966
7136  79.826741  10.745771
7137  79.766478  10.791908
7138  79.697268  10.839371
7139  79.619127  10.888154

[7140 rows x 2 columns]

Performing a prediction

Let us load the model and predict alarm events:

from import Regression

reg = Regression().load('alarms').with_prediction_data(
result = reg.predict()

The result data frame contains a single column called “alarm_predict” (Y_NAME + “_predict”).

Let us return “time” and “alarm” columns and compare the predicted data with the real one:

result['alarm'] = alarms
result['time'] = times
Prediction chart

If the sensor data is similar to the data which has been used to train the model, the prediction will be pretty accurate. Otherwise the results may differ.

To increase the model accuracy, continue training it with various production data. The more plant state the model has experience with, the more accurate predictions it can output.