#===============================================================================
# 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
# limitations under the License.
#===============================================================================

# daal4py DBSCAN example for shared memory systems

import daal4py as d4p
import numpy as np
import os
from daal4py.oneapi import sycl_buffer

# let's try to use pandas' fast csv reader
try:
    import pandas

    def read_csv(f, c, t=np.float64):
        return pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)
except ImportError:
    # fall back to numpy loadtxt
    def read_csv(f, c, t=np.float64):
        return np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)

try:
    from daal4py.oneapi import sycl_context
    with sycl_context('gpu'):
        gpu_available = True
except:
    gpu_available = False


# At this moment with sycl we are working only with numpy arrays
def to_numpy(data):
    try:
        from pandas import DataFrame
        if isinstance(data, DataFrame):
            return np.ascontiguousarray(data.values)
    except ImportError:
        pass
    try:
        from scipy.sparse import csr_matrix
        if isinstance(data, csr_matrix):
            return data.toarray()
    except ImportError:
        pass
    return data


# Common code for both CPU and GPU computations
def compute(data, minObservations, epsilon):
    # configure dbscan main object:
    # we also request the indices and observations of cluster cores
    algo = d4p.dbscan(
        minObservations=minObservations,
        fptype='float',
        epsilon=epsilon,
        resultsToCompute='computeCoreIndices|computeCoreObservations',
        memorySavingMode=True
    )
    # and compute
    return algo.compute(data)


def main(readcsv=read_csv, method='defaultDense'):
    infile = os.path.join('..', 'data', 'batch', 'dbscan_dense.csv')
    epsilon = 0.04
    minObservations = 45

    # Load the data
    data = readcsv(infile, range(2), t=np.float32)

    result_classic = compute(data, minObservations, epsilon)

    data = to_numpy(data)

    # It is possible to specify to make the computations on GPU
    if gpu_available:
        with sycl_context('gpu'):
            sycl_data = sycl_buffer(data)
            result_gpu = compute(sycl_data, minObservations, epsilon)
            assert np.allclose(result_classic.nClusters, result_gpu.nClusters)
            assert np.allclose(result_classic.assignments, result_gpu.assignments)
            assert np.allclose(result_classic.coreIndices, result_gpu.coreIndices)
            assert np.allclose(result_classic.coreObservations,
                               result_gpu.coreObservations)

    with sycl_context('cpu'):
        sycl_data = sycl_buffer(data)
        result_cpu = compute(sycl_data, minObservations, epsilon)
        assert np.allclose(result_classic.nClusters, result_cpu.nClusters)
        assert np.allclose(result_classic.assignments, result_cpu.assignments)
        assert np.allclose(result_classic.coreIndices, result_cpu.coreIndices)
        assert np.allclose(result_classic.coreObservations, result_cpu.coreObservations)

    return result_classic


if __name__ == "__main__":
    result = main()
    print("\nFirst 10 cluster assignments:\n", result.assignments[0:10])
    print("\nFirst 10 cluster core indices:\n", result.coreIndices[0:10])
    print("\nFirst 10 cluster core observations:\n", result.coreObservations[0:10])
    print("\nNumber of clusters:\n", result.nClusters)
    print('All looks good!')
