#===============================================================================
# Copyright 2014-2021 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 Linear Regression 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 dpctx import device_context, device_type
    with device_context(device_type.gpu, 0):
        gpu_available = True
except:
    try:
        from daal4py.oneapi import sycl_context
        with sycl_context('gpu'):
            gpu_available = True
    except:
        gpu_available = False


# Commone code for both CPU and GPU computations
def compute(train_indep_data, train_dep_data, test_indep_data):
    # Configure a Linear regression training object
    train_algo = d4p.linear_regression_training(interceptFlag=True, fptype='float')
    # Now train/compute, the result provides the model for prediction
    train_result = train_algo.compute(train_indep_data, train_dep_data)
    # Now let's do some prediction
    predict_algo = d4p.linear_regression_prediction(fptype='float')
    # now predict using the model from the training above
    return predict_algo.compute(test_indep_data, train_result.model), train_result


# 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


def main(readcsv=read_csv, method='defaultDense'):
    # read training data. Let's have 10 independent,
    # and 2 dependent variables (for each observation)
    trainfile = os.path.join('..', 'data', 'batch', 'linear_regression_train.csv')
    train_indep_data = readcsv(trainfile, range(10), t=np.float32)
    train_dep_data = readcsv(trainfile, range(10, 12), t=np.float32)

    # read testing data
    testfile = os.path.join('..', 'data', 'batch', 'linear_regression_test.csv')
    test_indep_data = readcsv(testfile, range(10), t=np.float32)
    test_dep_data = readcsv(testfile, range(10, 12), t=np.float32)

    # Using of the classic way (computations on CPU)
    result_classic, train_result = \
        compute(train_indep_data, train_dep_data, test_indep_data)

    train_indep_data = to_numpy(train_indep_data)
    train_dep_data = to_numpy(train_dep_data)
    test_indep_data = to_numpy(test_indep_data)

    try:
        from dpctx import device_context, device_type

        def gpu_context():
            return device_context(device_type.gpu, 0)

        def cpu_context():
            return device_context(device_type.cpu, 0)
    except:
        from daal4py.oneapi import sycl_context

        def gpu_context():
            return sycl_context('gpu')

        def cpu_context():
            return sycl_context('cpu')

    # It is possible to specify to make the computations on GPU
    if gpu_available:
        with gpu_context():
            sycl_train_indep_data = sycl_buffer(train_indep_data)
            sycl_train_dep_data = sycl_buffer(train_dep_data)
            sycl_test_indep_data = sycl_buffer(test_indep_data)
            result_gpu, _ = compute(sycl_train_indep_data, sycl_train_dep_data,
                                    sycl_test_indep_data)
        assert np.allclose(result_classic.prediction, result_gpu.prediction, atol=1e-3)

    # It is possible to specify to make the computations on CPU
    with cpu_context():
        sycl_train_indep_data = sycl_buffer(train_indep_data)
        sycl_train_dep_data = sycl_buffer(train_dep_data)
        sycl_test_indep_data = sycl_buffer(test_indep_data)
        result_cpu, _ = compute(sycl_train_indep_data, sycl_train_dep_data,
                                sycl_test_indep_data)

    # The prediction result provides prediction
    assert result_classic.prediction.shape == (test_dep_data.shape[0],
                                               test_dep_data.shape[1])

    assert np.allclose(result_classic.prediction, result_cpu.prediction)

    return (train_result, result_classic, test_dep_data)


if __name__ == "__main__":
    (train_result, predict_result, test_dep_data) = main()
    print("\nLinear Regression coefficients:\n", train_result.model.Beta)
    print(
        "\nLinear Regression prediction results: (first 10 rows):\n",
        predict_result.prediction[0:10]
    )
    print("\nGround truth (first 10 rows):\n", test_dep_data[0:10])
    print('All looks good!')