examples/daal4py/implicit_als.py

# ==============================================================================
# Copyright 2014 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================

# daal4py implicit_als example for shared memory systems

from pathlib import Path

import numpy as np
from readcsv import pd_read_csv

import daal4py as d4p


def main(readcsv=pd_read_csv, method="defaultDense"):
    nFactors = 2
    data_path = Path(__file__).parent / "data" / "batch"
    infile = data_path / "implicit_als_dense.csv"
    # We load the data
    data = readcsv(infile)

    # configure a implicit_als init object
    algo1 = d4p.implicit_als_training_init(nFactors=nFactors, method=method)
    # and compute initial model
    result1 = algo1.compute(data)

    # configure a implicit_als training object
    algo2 = d4p.implicit_als_training(nFactors=nFactors, method=method)
    # and compute model using initial model
    result2 = algo2.compute(data, result1.model)

    # Now do some prediction; first get prediction algorithm object
    algo3 = d4p.implicit_als_prediction_ratings(nFactors=nFactors)
    # and compute
    result3 = algo3.compute(result2.model)

    # implicit als prediction result objects provide prediction
    assert result3.prediction.shape == data.shape

    return result3


if __name__ == "__main__":
    res = main()
    print("Predicted ratings:\n", res.prediction[:10])
    print("All looks good!")