#===============================================================================
# Copyright 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 Decision Forest Classification example of Hist method 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, dtype=t)


def main(readcsv=read_csv, method='hist'):
    # input data file
    infile = "./data/batch/df_classification_train.csv"
    testfile = "./data/batch/df_classification_test.csv"

    # Configure a training object (5 classes)
    train_algo = d4p.decision_forest_classification_training(
        5,
        method=method,
        maxBins=256,
        minBinSize=1,
        nTrees=10,
        minObservationsInLeafNode=8,
        featuresPerNode=3,
        engine=d4p.engines_mt19937(seed=777),
        varImportance='MDI',
        bootstrap=True,
        resultsToCompute='computeOutOfBagError'
    )

    # Read data. Let's use 3 features per observation
    data = readcsv(infile, range(3), t=np.float32)
    labels = readcsv(infile, range(3, 4), t=np.float32)
    train_result = train_algo.compute(data, labels)
    # Traiing result provides (depending on parameters) model,
    # outOfBagError, outOfBagErrorPerObservation and/or variableImportance

    # Now let's do some prediction
    predict_algo = d4p.decision_forest_classification_prediction(
        nClasses=5,
        resultsToEvaluate="computeClassLabels|computeClassProbabilities",
        votingMethod="unweighted"
    )
    # read test data (with same #features)
    pdata = readcsv(testfile, range(3), t=np.float32)
    plabels = readcsv(testfile, range(3, 4), t=np.float32)
    # now predict using the model from the training above
    predict_result = predict_algo.compute(pdata, train_result.model)

    # Prediction result provides prediction
    assert(predict_result.prediction.shape == (pdata.shape[0], 1))

    return (train_result, predict_result, plabels)


if __name__ == "__main__":
    (train_result, predict_result, plabels) = main()
    print("\nVariable importance results:\n", train_result.variableImportance)
    print("\nOOB error:\n", train_result.outOfBagError)
    print(
        "\nDecision forest prediction results (first 10 rows):\n",
        predict_result.prediction[0:10]
    )
    print(
        "\nDecision forest probabilities results (first 10 rows):\n",
        predict_result.probabilities[0:10]
    )
    print("\nGround truth (first 10 rows):\n", plabels[0:10])
    print('All looks good!')