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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' |
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