def string_parser(s):
    if len(re.findall(r":leaf=", s)) == 0:
        out  = re.findall(r"[\w.-]+", s)
        tabs = re.findall(r"[\t]+", s)
        if (out[4] == out[8]):
            missing_value_handling = (" or np.isnan(x['" + out[1] + "']) ")
        else:
            missing_value_handling = ""

        if len(tabs) > 0:
            return (re.findall(r"[\t]+", s)[0].replace('\t', '    ') +
                    '        if state == ' + out[0] + ':\n' +
                    re.findall(r"[\t]+", s)[0].replace('\t', '    ') +
                    '            state = (' + out[4] +
                    ' if ' +  "x['" + out[1] +"']<" + out[2] + missing_value_handling +
                    ' else ' + out[6] + ')\n' )

        else:
            return ('        if state == ' + out[0] + ':\n' +
                    '            state = (' + out[4] +
                    ' if ' +  "x['" + out[1] +"']<" + out[2] + missing_value_handling +
                    ' else ' + out[6] + ')\n' )
    else:
        out = re.findall(r"[\d.-]+", s)
        return (re.findall(r"[\t]+", s)[0].replace('\t', '    ') +
                '        if state == ' + out[0] + ':\n    ' +
                re.findall(r"[\t]+", s)[0].replace('\t', '    ') +
                '        return ' + out[1] + '\n')

def tree_parser(tree, i):
    if i == 0:
        return ('    if num_booster == 0:\n        state = 0\n'
             + "".join([string_parser(tree.split('\n')[i])
                        for i in range(len(tree.split('\n'))-1)]))
    else:
        return ('    elif num_booster == '+str(i)+':\n        state = 0\n'
             + "".join([string_parser(tree.split('\n')[i])
                        for i in range(len(tree.split('\n'))-1)]))

def model_to_py(base_score, model, out_file):
    trees = model.get_dump()
    result = ["import numpy as np\n\n"
             +"def xgb_tree(x, num_booster):\n"]

    for i in range(len(trees)):
        result.append(tree_parser(trees[i], i))

    with open(out_file, 'a') as the_file:
        the_file.write("".join(result) + "\ndef xgb_predict(x):\n    predict = "
                       + str(base_score) + "\n"
                       + "# initialize prediction with base score\n"
                       + "    for i in range("
                       + str(len(trees))
                       + "):\n        predict = predict + xgb_tree(x, i)"
                       + "\n    return predict")