Skip to content

Instantly share code, notes, and snippets.

@NMZivkovic
Created July 12, 2019 13:42
Show Gist options
  • Save NMZivkovic/b1ae20243175eb30917b96b6107cb4d0 to your computer and use it in GitHub Desktop.
Save NMZivkovic/b1ae20243175eb30917b96b6107cb4d0 to your computer and use it in GitHub Desktop.
public void BuildAndFit()
{
var pipeline = _mlContext.Transforms.CopyColumns(inputColumnName: "Count", outputColumnName: "Label")
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Season"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Year"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Holiday"))
.Append(_mlContext.Transforms.Categorical.OneHotEncoding("Weather"))
.Append(_mlContext.Transforms.Concatenate("Features",
"Season",
"Year",
"Month",
"Hour",
"Weekday",
"Weather",
"Temperature",
"Humidity",
"Windspeed",
"Casual"))
.Append(_mlContext.Transforms.NormalizeMinMax("Features", "Features"))
.AppendCacheCheckpoint(_mlContext)
.Append(_algorythim);
_trainedModel = pipeline.Fit(_trainingDataView);
_predictionEngine = _mlContext.Model.CreatePredictionEngine<BikeSharingDemandSample, BikeSharingDemandPrediction>(_trainedModel);
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment