#===============================================================================
# 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 logistic regression example for shared memory systems

import daal4py as d4p
import numpy as np

# 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)


def main(readcsv=read_csv, method='defaultDense'):
    nClasses = 5
    nFeatures = 6

    # read training data from file with 6 features per observation and 1 class label
    trainfile = "./data/batch/logreg_train.csv"
    train_data = readcsv(trainfile, range(nFeatures))
    train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))

    # set parameters and train
    train_alg = d4p.logistic_regression_training(nClasses=nClasses,
                                                 penaltyL1=0.1,
                                                 penaltyL2=0.1,
                                                 interceptFlag=True)
    train_result = train_alg.compute(train_data, train_labels)

    # read testing data from file with 6 features per observation
    testfile = "./data/batch/logreg_test.csv"
    predict_data = readcsv(testfile, range(nFeatures))

    # set parameters and compute predictions
    predict_alg = \
        d4p.logistic_regression_prediction(
            nClasses=nClasses,
            resultsToEvaluate="computeClassLabels|computeClassProbabilities|"
            "computeClassLogProbabilities"
        )
    predict_result = predict_alg.compute(predict_data, train_result.model)
    # the prediction result provides prediction, probabilities and logProbabilities
    assert predict_result.probabilities.shape == (predict_data.shape[0], nClasses)
    assert predict_result.logProbabilities.shape == (predict_data.shape[0], nClasses)
    predict_labels = np.loadtxt(
        testfile,
        usecols=range(nFeatures, nFeatures + 1),
        delimiter=',',
        ndmin=2
    )
    assert np.count_nonzero(predict_result.prediction - predict_labels) \
           / predict_labels.shape[0] < 0.025

    return (train_result, predict_result, predict_labels)


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