<commit_before># Copyright 2016 <NAME>

# 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.
import argparse
import numpy as np
from .utils import RandomParameterSearch, PARAMS
from .sparse_array import SparseArray
from .model import Ensemble
from multiprocessing import Pool
import EvoDAG as evodag
from EvoDAG import EvoDAG
import os
import gzip
import json
import gc
import pickle
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x, **kwargs):
        return x


def init_evodag(seed_args_X_y_test):
    seed, args, X, y, test = seed_args_X_y_test
    m = EvoDAG(seed=seed, **args).fit(X, y, test_set=test)
    m = m.model()
    gc.collect()
    return m


def rs_evodag(args_X_y):
    args, X, y = args_X_y
    rs = RandomParameterSearch
    fit = []
    for seed in range(3):
        evo = EvoDAG(seed=seed,
                     **rs.process_params(args)).fit(X, y)
        fit.append(evo.model().fitness_vs)
    gc.collect()
    return fit, args


class CommandLine(object):
    def __init__(self):
        self.Xtest = None
        self.word2id = {}
        self.label2id = {}
        self.parser = argparse.ArgumentParser(description="EvoDAG")
        self.training_set()
        self.init_params()
        self.model_file()
        self.test_set()
        self.optimize_parameters()
        self.cores()
        self.ensemble()
        self.output_file()
        self.version()

    def version(self):
        pa = self.parser.add_argument
        pa('--version',
           action='version', version='EvoDAG %s' % evodag.__version__)

    def output_file(self):
        self.parser.add_argument('-o', '--output-file',
                                 help='File to store the test set',
                                 dest='output_file',
                                 default=None,
                                 type=str)

    def ensemble(self):
        self.parser.add_argument('-n', '--ensemble-size',
                                 help='Ensemble size',
                                 dest='ensemble_size',
                                 default=1,
                                 type=int)

    def cores(self):
        self.parser.add_argument('-u', '--cpu-cores',
                                 help='Number of cores',
                                 dest='cpu_cores',
                                 default=1,
                                 type=int)

    def optimize_parameters(self):
        cdn = '''Optimize parameters sampling
        N points from the parameter space'''
        self.parser.add_argument('-r', '--optimize-parameters',
                                 dest='optimize_parameters',
                                 type=int, help=cdn)
        cdn = 'File to store the fitness of the configuration explored'
        self.parser.add_argument('--parameters',
                                 dest='parameters',
                                 type=str,
                                 help=cdn)

    def test_set(self):
        cdn = 'File containing the test set on csv.'
        self.parser.add_argument('-t', '--test_set',
                                 default=None, type=str,
                                 help=cdn)

    def model_file(self):
        pa = self.parser.add_argument
        pa('-m', '--model-file', type=str, dest='model_file',
           default=None,
           help='File name to either store or load the model')

    def init_params(self):
        pa = self.parser.add_argument
        g = self.parser.add_mutually_exclusive_group(required=True)
        g.add_argument('-C', '--classifier', dest='classifier',
                       help='The task is classification (default)',
                       default=True,
                       action="store_true")
        g.add_argument('-R', '--regressor', dest='regressor',
                       help='The task is regression',
                       action="store_true")
        pa('-e', '--early_stopping_rounds', dest='early_stopping_rounds',
           type=int,
           help='Early stopping rounds')
        pa('-p', '--popsize', dest='popsize',
           type=int, help='Population size')
        pa('-s', '--seed', dest='seed',
           default=0,
           type=int, help='Seed')
        pa('-j', '--json', dest='json',
           action="store_true",
           help='Whether the inputs are in json format',
           default=False)
        pa('--evolution', dest='population_class',
           help="Type of evolution (SteadyState|Generational)",
           type=str)
        pa('--all-inputs', dest='all_inputs',
           help="The initial population has all the available inputs ",
           action="store_true")<commit_msg>cleaning<commit_after># Copyright 2016 <NAME>

# 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.
import argparse
import numpy as np
from .utils import RandomParameterSearch, PARAMS
from .sparse_array import SparseArray
from .model import Ensemble
from multiprocessing import Pool
import EvoDAG as evodag
from EvoDAG import EvoDAG
import gzip
import json
import gc
import pickle
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x, **kwargs):
        return x


def init_evodag(seed_args_X_y_test):
    seed, args, X, y, test = seed_args_X_y_test
    m = EvoDAG(seed=seed, **args).fit(X, y, test_set=test)
    m = m.model()
    gc.collect()
    return m


def rs_evodag(args_X_y):
    args, X, y = args_X_y
    rs = RandomParameterSearch
    fit = []
    for seed in range(3):
        evo = EvoDAG(seed=seed,
                     **rs.process_params(args)).fit(X, y)
        fit.append(evo.model().fitness_vs)
    gc.collect()
    return fit, args


class CommandLine(object):
    def version(self):
        pa = self.parser.add_argument
        pa('--version',
           action='version', version='EvoDAG %s' % evodag.__version__)

    def output_file(self):
        self.parser.add_argument('-o', '--output-file',
                                 help='File to store the test set',
                                 dest='output_file',
                                 default=None,
                                 type=str)

    def ensemble(self):
        self.parser.add_argument('-n', '--ensemble-size',
                                 help='Ensemble size',
                                 dest='ensemble_size',
                                 default=1,
                                 type=int)

    def cores(self):
        self.parser.add_argument('-u', '--cpu-cores',
                                 help='Number of cores',
                                 dest='cpu_cores',
                                 default=1,
                                 type=int)

    def test_set(self):
        cdn = 'File containing the test set on csv.'
        self.parser.add_argument('-t', '--test_set',
                                 default=None, type=str,
                                 help=cdn)

    def init_params(self):
        pa = self.parser.add_argument
        g = self.parser.add_mutually_exclusive_group(required=True)
        g.add_argument('-C', '--classifier', dest='classifier',
                       help='The task is classification (default)',
                       default=True,
                       action="store_true")
        g.add_argument('-R', '--regressor', dest='regressor',
                       help='The task is regression',
                       action="store_true")
        pa('-e', '--early_stopping_rounds', dest='early_stopping_rounds',
           type=int,
           help='Early stopping rounds')
        pa('-p', '--popsize', dest='popsize',
           type=int, help='Population size')
        pa('-s', '--seed', dest='seed',
           default=0,
           type=int, help='Seed')
        pa('-j', '--json', dest='json',
           action="store_true",
           help='Whether the inputs are in json format',
           default=False)
        pa('--evolution', dest='population_class',
           help="Type of evolution (SteadyState|Generational)",
           type=str)
        pa('--all-inputs', dest='all_inputs',
           help="The initial population has all the available inputs ",
           action="store_true")
{'fraction_non_alphanumeric': 0.10936648129974921, 'fraction_numerical': 0.0028350234434630904, 'mean_word_length': 1.7997557997557998, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 33, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6399705', 'n_tokens_mistral': 2569, 'n_tokens_neox': 2476, 'n_words': 675}