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Vivek Amilkanthawar vivek081166

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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"
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(,,
train_size=0.75, test_size=0.25)
tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2), y_train)
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._
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), y_train, time_limit=30 * 60)
import h2o
from h2o.automl import H2OAutoML
# 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("")
test = h2o.import_file("")
# 予測子とレスポンスを特定する
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)
vivek081166 / mlbox-auto-ml-house-prices.ipynb
Last active Apr 23, 2019
View mlbox-auto-ml-house-prices.ipynb
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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
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')
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'