#*******************************************************************************
# Copyright 2014-2019 Intel Corporation
# All Rights Reserved.
#
# This software is licensed under the Apache License, Version 2.0 (the
# "License"), the following terms apply:
#
# 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 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
    read_csv = lambda f, c, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=np.float32)
except:
    # fall back to numpy loadtxt
    read_csv = lambda f, c, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2, dtype=np.float32)


def main(readcsv=read_csv, method='defaultDense'):
    infile = "./data/batch/df_regression_train.csv"
    testfile = "./data/batch/df_regression_test.csv"

    # Configure a Linear regression training object
    train_algo = d4p.decision_forest_regression_training(nTrees=100,
                                                         varImportance='MDA_Raw',
                                                         bootstrap=True,
                                                         engine = d4p.engines_mt2203(seed=777),
                                                         resultsToCompute='computeOutOfBagError|computeOutOfBagErrorPerObservation')
    
    # Read data. Let's have 13 independent, and 1 dependent variables (for each observation)
    indep_data = readcsv(infile, range(13), t=np.float32)
    dep_data   = readcsv(infile, range(13,14), t=np.float32)
    # Now train/compute, the result provides the model for prediction
    train_result = train_algo.compute(indep_data, dep_data)
    # Traiing result provides (depending on parameters) model, outOfBagError, outOfBagErrorPerObservation and/or variableImportance

    # Now let's do some prediction
    predict_algo = d4p.decision_forest_regression_prediction()
    # read test data (with same #features)
    pdata = readcsv(testfile, range(13), t=np.float32)
    ptdata = readcsv(testfile, range(13,14), t=np.float32)
    # now predict using the model from the training above
    predict_result = predict_algo.compute(pdata, train_result.model)

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

    return (train_result, predict_result, ptdata)


if __name__ == "__main__":
    from daal4py import __daal_link_version__ as dv
    daal_version = tuple(map(int, (dv[0:4], dv[4:8])))
    if daal_version < (2019, 1):
        print("Need Intel(R) DAAL 2019.1 or later")
    else:
        (train_result, predict_result, ptdata) = 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("\nGround truth (first 10 rows):\n", ptdata[0:10])
        print('All looks good!')
