data-hound

# Helper Method to get data into shape to pass to GridSearchClassifier
def get_sciki_xy(X,y):
  X_sciki = np.column_stack([X.reshape((y.shape[0], np.prod(X.shape[1:]))), y])
  y_sciki = np.column_stack([y,X.reshape((y.shape[0], np.prod(X.shape[1:])))])

  return X_sciki,y_sciki

def do_cross_val():
  (x_train_, y_train_), (x_test_, y_test_) = load_mnist() #load the dataset
  

data-hound

def input_reshaper(X):
    return [X[:,:-10].reshape(X.shape[0],28,28,1), X[:,-10:]]

data-hound

from typing import List

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder, FunctionTransformer, OneHotEncoder


class MultiOutputTransformer(BaseEstimator, TransformerMixin):

    def fit(self, y):
        # Separate the two different 'y's into two arrays

data-hound

from sklearn.base import BaseEstimator, TransformerMixin
from scikeras.wrappers import KerasClassifier

### Multi-Output Classifier

class MultiOutputTransformer(BaseEstimator, TransformerMixin):
  #define your transformer


class MultiOutputClassifier(KerasClassifier):

data-hound

import numpy as np
from sklearn.datasets import make_classification
from tensorflow import keras
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier

# Make a dummy dataset
X, y = make_classification(1000, 20, n_informative=10, random_state=0)
X = X.astype(np.float32)
y = y.astype(np.int64)