simple_ml.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 5\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataframe = pandas.read_csv(\"mydata1.csv\",header=None)\n",
    "dataset = dataframe.values\n",
    "X = dataset[:,0:3]\n",
    "Y = dataset[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "encoder.fit(Y)\n",
    "encoded_Y = encoder.transform(Y)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = np_utils.to_categorical(encoded_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define baseline model\n",
    "def baseline_model():\n",
    "\t# create model\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Dense(8, input_dim=3, activation='relu'))\n",
    "\tmodel.add(Dense(2, activation='softmax'))\n",
    "\t# Compile model\n",
    "\tmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=10, shuffle=True, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline: 88.33% (13.02%)\n"
     ]
    }
   ],
   "source": [
    "results = cross_val_score(estimator, X, dummy_y, cv=kfold)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1]\n",
      "['yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'no' 'no' 'no' 'no' 'yes' 'yes' 'no'\n",
      " 'yes' 'no' 'yes' 'no' 'yes' 'yes' 'yes']\n",
      "[[5 5 5]\n",
      " [7 8 1]\n",
      " [5 8 5]\n",
      " [4 5 4]\n",
      " [6 5 9]\n",
      " [1 5 1]\n",
      " [1 4 5]\n",
      " [1 1 5]\n",
      " [4 1 4]\n",
      " [4 2 2]\n",
      " [4 4 1]\n",
      " [6 5 4]\n",
      " [2 1 1]\n",
      " [1 6 1]\n",
      " [1 3 1]\n",
      " [6 5 9]\n",
      " [1 1 3]\n",
      " [2 4 2]\n",
      " [7 5 4]\n",
      " [1 5 1]]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, dummy_y, test_size=0.33, random_state=seed)\n",
    "estimator.fit(X_train, Y_train)\n",
    "predictions = estimator.predict(X_test)\n",
    "print(predictions)\n",
    "print(encoder.inverse_transform(predictions))\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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