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from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
                                                    train_size=0.75, test_size=0.25)

tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)
tpot.fit(X_train, y_train)

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# Inputs & imports : 必要があるのはそれだけです！

from mlbox.preprocessing import *
from mlbox.optimisation import *
from mlbox.prediction import *

paths = ["../input/train.csv","../input/test.csv"]
target_name = "SalePrice"

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import com.salesforce.op._
import com.salesforce.op.readers._
import com.salesforce.op.features._
import com.salesforce.op.features.types._
import com.salesforce.op.stages.impl.classification._
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession

implicit val spark = SparkSession.builder.config(new SparkConf()).getOrCreate()
import spark.implicits._

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from keras.datasets import mnist
from autokeras import ImageClassifier
from autokeras.constant import Constant

if __name__ == '__main__':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(x_train.shape + (1,))
    x_test = x_test.reshape(x_test.shape + (1,))
    clf = ImageClassifier(verbose=True, augment=False)
    clf.fit(x_train, y_train, time_limit=30 * 60)

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import h2o
from h2o.automl import H2OAutoML

h2o.init()

# サンプルバイナリ結果トレイン/テストセットをH2Oにインポートする
train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
test = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_test_5k.csv")

# 予測子とレスポンスを特定する

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import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics

import autosklearn.regression

def main():
    X, y = sklearn.datasets.load_boston(return_X_y=True)
    feature_types = (['numerical'] * 3) + ['categorical'] + (['numerical'] * 9)

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from keras.callbacks import ReduceLROnPlateau
from keras.utils.np_utils import to_categorical
import keras.backend as K
from keras import regularizers
from keras.layers import Lambda
from keras.layers.convolutional import Conv1D, MaxPooling1D
from keras.layers.core import Activation, Dense
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
import numpy as np

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import os
import pickle
from glob import iglob
import numpy as np
import librosa

DATA_AUDIO_DIR = './audio'
TARGET_SR = 8000
OUTPUT_DIR = './output'
OUTPUT_DIR_TRAIN = os.path.join(OUTPUT_DIR, 'train')

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import os
import pickle
from glob import iglob
from shutil import rmtree
import numpy as np
from model_data import read_audio_from_filename

DATA_AUDIO_DIR = './audio'
TARGET_SR = 8000
OUTPUT_DIR = './output'