[FIXED] Converting Sklearn model to Core ML Model for iOS

Issue

I have created sklearn model for predicting ESG Scores. But for imputation reasons my input features matrix X were converted to np.ndarray. I found on apple developers website how to convert them, but there is optional kwargs input_features and output_features.

import coremltools
coreml_model = coremltools.converters.sklearn.convert(model,
                                                    ["bedroom", "bath", "size"],
                                                    "price")

coreml_model.save('HousePricer.mlmodel')

I am wondering is it necessary match names of features in X with value I am sending to convert() method with input_features argument?

I.e.

imputer = KNNImputer()

# Impute missing values in X
X_imputed = imputer.fit_transform(X)

X_imputed

Out:

array([[ 5.00000e+00,  1.80000e+04,  3.27000e+08, ..., -2.20000e+07,
         2.64000e+08,  2.64000e+08],
       [ 6.00000e+00,  1.32500e+05,  9.00000e+06, ...,  9.38000e+08,
         1.90000e+07,  1.90000e+07],
       [ 2.00000e+00,  4.00000e+04,  2.37765e+08, ..., -1.88580e+07,
         1.78696e+08,  1.78696e+08],
       ...,
     ])

So there are no labels now.

The question is how to convert it to Core ML Model now, with no labels for input_features.

Unfortunately I don't have access to macOS to try different approaches and test them.

I would be grateful if you showed not only how to convert but also sample usage of the model with Swift.

(Model based on Random Forest Regressor)

Solution

There is dedicated method called ct.converters.sklearn.convert for this goal.

Here is a full example of the conversion:

from sklearn.linear_model import LinearRegression
import numpy as np
import coremltools as ct

# Load data
X = np.random.rand(10,5).astype(np.ndarray)
y = np.random.rand(10).astype(np.ndarray)

# Train a model
model = LinearRegression()
model.fit(X, y)

# save the scikit-learn model
coreml_model = ct.converters.sklearn.convert(model)
coreml_model.save('Predictor.mlmodel')

Printing coreml_model you get:

In [13]: coreml_model
Out[13]:
input {
  name: "input"
  type {
    multiArrayType {
      shape: 5
      dataType: DOUBLE
    }
  }
}
output {
  name: "prediction"
  type {
    doubleType {
    }
  }
}
predictedFeatureName: "prediction"
metadata {
  userDefined {
    key: "com.github.apple.coremltools.source"
    value: "scikit-learn==1.1.1"
  }
  userDefined {
    key: "com.github.apple.coremltools.version"
    value: "7.1"
  }
}

Answered By - seralouk