autoencoder-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the make_classification() scikit-learn function to define a synthetic binary (2-class) classification task with 100 input features (columns) and 1,000 examples (rows). Importantly, we will define the problem in such a way that most of the input variables are redundant (90 of the 100 or 90 percent), allowing the autoencoder later to learn a useful compressed representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 100) (1000,)\n"
     ]
    }
   ],
   "source": [
    "# synthetic classification dataset\n",
    "from sklearn.datasets import make_classification\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=100, n_informative=10, n_redundant=90, random_state=1)\n",
    "# summarize the dataset\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into train test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# scale data\n",
    "t = MinMaxScaler()\n",
    "t.fit(X_train)\n",
    "X_train = t.transform(X_train)\n",
    "X_test = t.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will define the encoder to have two hidden layers, the first with two times the number of inputs (e.g. 200) and the second with the same number of inputs (100), followed by the bottleneck layer with the same number of inputs as the dataset (100 uncompressed) or reduced number of nodes representing filtered bottleneck(50). To ensure the model learns well, we will use batch normalization and leaky ReLU activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n",
      "Metal device set to: Apple M1\n",
      "\n",
      "systemMemory: 16.00 GB\n",
      "maxCacheSize: 5.33 GB\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 18:16:32.760770: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-09-24 18:16:32.760853: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "# define encoder\n",
    "from keras.layers import Input, Dense, LeakyReLU \n",
    "from keras.layers.normalization import BatchNormalization\n",
    "\n",
    "n_inputs = X.shape[1]\n",
    "visible = Input(shape=(n_inputs,))\n",
    "# encoder level 1\n",
    "e = Dense(n_inputs*2)(visible)\n",
    "e = BatchNormalization()(e)\n",
    "e = LeakyReLU()(e)\n",
    "# encoder level 2\n",
    "e = Dense(n_inputs)(e)\n",
    "e = BatchNormalization()(e)\n",
    "e = LeakyReLU()(e)\n",
    "# bottleneck\n",
    "#n_bottleneck = n_inputs # no compression of bottleneck\n",
    "n_bottleneck = round(float(n_inputs) / 2.0) # reduced nodes to 50\n",
    "bottleneck = Dense(n_bottleneck)(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decoder will be defined with a similar structure, although in reverse. It will have two hidden layers, the first with the number of inputs in the dataset (e.g. 100) and the second with double the number of inputs (e.g. 200). The output layer will have the same number of nodes as there are columns in the input data and will use a linear activation function to output numeric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define decoder, level 1\n",
    "from keras.models import Model, load_model\n",
    "d = Dense(n_inputs)(bottleneck)\n",
    "d = BatchNormalization()(d)\n",
    "d = LeakyReLU()(d)\n",
    "# decoder level 2\n",
    "d = Dense(n_inputs*2)(d)\n",
    "d = BatchNormalization()(d)\n",
    "d = LeakyReLU()(d)\n",
    "# output layer\n",
    "output = Dense(n_inputs, activation='linear')(d)\n",
    "# define autoencoder model\n",
    "model = Model(inputs=visible, outputs=output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model will be fit using the efficient Adam version of stochastic gradient descent and minimizes the mean squared error, given that reconstruction is a type of multi-output regression problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compile autoencoder model\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "# plot the autoencoder\n",
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, 'autoencoder_compress.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can train the model to reproduce the input and keep track of the performance of the model on the hold-out test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 18:16:33.619396: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-09-24 18:16:33.619575: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 18:16:40.744965: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42/42 - 8s - loss: 0.2205 - val_loss: 0.1490\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 18:16:41.706885: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/200\n",
      "42/42 - 1s - loss: 0.0328 - val_loss: 0.0795\n",
      "Epoch 3/200\n",
      "42/42 - 1s - loss: 0.0228 - val_loss: 0.0485\n",
      "Epoch 4/200\n",
      "42/42 - 1s - loss: 0.0174 - val_loss: 0.0277\n",
      "Epoch 5/200\n",
      "42/42 - 1s - loss: 0.0159 - val_loss: 0.0175\n",
      "Epoch 6/200\n",
      "42/42 - 1s - loss: 0.0144 - val_loss: 0.0131\n",
      "Epoch 7/200\n",
      "42/42 - 1s - loss: 0.0133 - val_loss: 0.0104\n",
      "Epoch 8/200\n",
      "42/42 - 1s - loss: 0.0118 - val_loss: 0.0084\n",
      "Epoch 9/200\n",
      "42/42 - 1s - loss: 0.0114 - val_loss: 0.0064\n",
      "Epoch 10/200\n",
      "42/42 - 1s - loss: 0.0106 - val_loss: 0.0065\n",
      "Epoch 11/200\n",
      "42/42 - 1s - loss: 0.0109 - val_loss: 0.0068\n",
      "Epoch 12/200\n",
      "42/42 - 1s - loss: 0.0101 - val_loss: 0.0065\n",
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      "Epoch 168/200\n",
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      "Epoch 169/200\n",
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      "Epoch 170/200\n",
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      "Epoch 171/200\n",
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      "Epoch 172/200\n",
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      "Epoch 173/200\n",
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      "Epoch 174/200\n",
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      "Epoch 175/200\n",
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      "Epoch 176/200\n",
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      "Epoch 177/200\n",
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      "Epoch 178/200\n",
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      "Epoch 179/200\n",
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      "Epoch 180/200\n",
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      "Epoch 181/200\n",
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      "Epoch 182/200\n",
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      "Epoch 183/200\n",
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      "Epoch 184/200\n",
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      "Epoch 185/200\n",
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      "Epoch 186/200\n",
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      "Epoch 187/200\n",
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      "Epoch 188/200\n",
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      "Epoch 189/200\n",
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      "Epoch 190/200\n",
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      "Epoch 191/200\n",
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      "Epoch 192/200\n",
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      "Epoch 193/200\n",
      "42/42 - 1s - loss: 0.0030 - val_loss: 8.8848e-04\n",
      "Epoch 194/200\n",
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      "Epoch 195/200\n",
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      "Epoch 196/200\n",
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      "Epoch 197/200\n",
      "42/42 - 1s - loss: 0.0030 - val_loss: 8.8995e-04\n",
      "Epoch 198/200\n",
      "42/42 - 1s - loss: 0.0030 - val_loss: 0.0013\n",
      "Epoch 199/200\n",
      "42/42 - 1s - loss: 0.0029 - val_loss: 8.8915e-04\n",
      "Epoch 200/200\n",
      "42/42 - 1s - loss: 0.0030 - val_loss: 0.0014\n"
     ]
    }
   ],
   "source": [
    "# fit the autoencoder model to reconstruct input\n",
    "history = model.fit(X_train, X_train, epochs=200, batch_size=16, verbose=2, validation_data=(X_test, X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 18:18:53.609324: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "X_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg error 0.02742080607501943\n",
      "median error 0.024396716807876244\n",
      "99Q: 0.062215805359252974\n",
      "setting threshold on 0.062215805359252974 \n"
     ]
    }
   ],
   "source": [
    "def get_error_term(v1, v2, _rmse=True):\n",
    "    if _rmse:\n",
    "        return np.sqrt(np.mean((v1 - v2) ** 2, axis=1))\n",
    "    #return MAE\n",
    "    return np.mean(abs(v1 - v2), axis=1)\n",
    "# mae is mean absolute error\n",
    "mae_vector = get_error_term(X_train_pred, X_train, _rmse=False)\n",
    "rmse_vector = get_error_term(X_train_pred, X_train, _rmse=True)\n",
    "reconstr_error_vector = [e ** 2 for e in rmse_vector]\n",
    "print(f'Avg error {np.mean(mae_vector)}\\nmedian error {np.median(mae_vector)}\\n99Q: {np.quantile(mae_vector, 0.99)}')\n",
    "print(f'setting threshold on { np.quantile(mae_vector, 0.99)} ')\n",
    "\n",
    "error_thresh = np.quantile(mae_vector, 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.021212121212121213"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred = model.predict(X_test)\n",
    "mae_vector = get_error_term(X_pred, X_test, _rmse=False)\n",
    "anomalies = (mae_vector > error_thresh)\n",
    "\n",
    "np.count_nonzero(anomalies) / len(anomalies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.96      0.67       170\n",
      "           1       0.14      0.01      0.01       160\n",
      "\n",
      "    accuracy                           0.50       330\n",
      "   macro avg       0.33      0.49      0.34       330\n",
      "weighted avg       0.33      0.50      0.35       330\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, anomalies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(330, 100)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot loss\n",
    "\n",
    "plt.plot(history.history['loss'], label='train')\n",
    "plt.plot(history.history['val_loss'], label='test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the encoder for use later. As part of saving the encoder, we will also plot the encoder model to get a feeling for the shape of the output of the bottleneck layer, e.g. a 50 compressed element vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define an encoder model (without the decoder)\n",
    "encoder = Model(inputs=visible, outputs=bottleneck)\n",
    "# save the encoder to file\n",
    "encoder.save('encoder.h5')\n",
    "plot_model(encoder, 'encoder_compress.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will use the trained encoder from the autoencoder to compress input data and train a different predictive model. First, let’s establish a baseline in performance on this problem. This is important as if the performance of a model is not improved by the compressed encoding, then the compressed encoding does not add value to the project and should not be used. We can train a logistic regression model on the training dataset directly and evaluate the performance of the model on the holdout test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8939393939393939\n"
     ]
    }
   ],
   "source": [
    "# baseline in performance with logistic regression model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=100, n_informative=10, n_redundant=90, random_state=1)\n",
    "# split into train test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# scale data\n",
    "t = MinMaxScaler()\n",
    "t.fit(X_train)\n",
    "X_train = t.transform(X_train)\n",
    "X_test = t.transform(X_test)\n",
    "# define model\n",
    "model = LogisticRegression()\n",
    "# fit model on training set\n",
    "model.fit(X_train, y_train)\n",
    "# make prediction on test set\n",
    "yhat = model.predict(X_test)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, yhat)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would hope and expect that a logistic regression model fit on an encoded version of the input to achieve better accuracy for the encoding to be considered useful. We can update the example to first encode the data using the encoder model trained in the previous section.\n",
    "\n",
    "First, we can load the trained encoder model from the file. We can then use the encoder to transform the raw input data (e.g. 100 columns) into bottleneck vectors (e.g. 50 element vectors). This process can be applied to the train and test datasets.\n",
    "![Illustrative Diagram on This Approach](2-layer-ae-LBGM.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 10:04:52.646519: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "# load the model from file\n",
    "encoder = load_model('encoder.h5')\n",
    "# encode the train data\n",
    "X_train_encode = encoder.predict(X_train)\n",
    "# encode the test data\n",
    "X_test_encode = encoder.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9181818181818182\n"
     ]
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the logistic regression model, as before.\n",
    "# define the model\n",
    "model = LogisticRegression(max_iter=2000)\n",
    "# fit the model on the training set\n",
    "model.fit(X_train_encode, y_train)\n",
    "# make predictions on the test set\n",
    "yhat = model.predict(X_test_encode)\n",
    "# calculate classification accuracy\n",
    "acc = accuracy_score(y_test, yhat)\n",
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_transform = pca.fit_transform(X_test_encode)\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.scatterplot(x=X_transform[:, 0], y=X_transform[:, 1], s=20, hue=mae_vector)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "sns.scatterplot(x=X_transform[:, 0], y=X_transform[:, 1], s=20, hue=anomalies)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "sns.scatterplot(x=X_transform[:, 0], y=X_transform[:, 1], s=10, hue=y_test)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will use the trained encoder from the autoencoder to compress input data and train an another predictive model. Gradient Boosting Machines Regression. Gradient Boosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. We can train a GBM regressor on the training dataset directly and evaluate the performance of the model on the holdout test set. Decision trees are used as the weak learners in gradient boosting. Decision Tree solves the problem of machine learning by transforming the data into tree representation. Each internal node of the tree representation denotes an attribute and each leaf node denotes a class label. The loss function is generally the squared error (particularly for regression problems). The loss function needs to be differentiable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score (acc) on test set: 0.9394\n"
     ]
    }
   ],
   "source": [
    "# baseline in performance with GBM regressor\n",
    "from sklearn import ensemble\n",
    "# define dataset\n",
    "X, y = make_classification(n_samples=1000, n_features=100, n_informative=10, n_redundant=90, random_state=1)\n",
    "# split into train test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1)\n",
    "# scale data\n",
    "t = MinMaxScaler()\n",
    "t.fit(X_train)\n",
    "X_train = t.transform(X_train)\n",
    "X_test = t.transform(X_test)\n",
    "# define model\n",
    "params = {'n_estimators': 200,\n",
    "          'max_depth': 4,\n",
    "          'min_samples_split': 6,\n",
    "          'min_samples_leaf': 1,\n",
    "          'learning_rate': 0.1,\n",
    "          'subsample': 0.4,          \n",
    "          'loss': 'ls'}\n",
    "gbm_regressor = ensemble.GradientBoostingRegressor(**params, random_state=1)\n",
    "# fit model on training set\n",
    "gbm_regressor.fit(X_train, y_train)\n",
    "# make prediction on test set\n",
    "yhat = gbm_regressor.predict(X_test)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "#rounded_yhat=np.argmax(yhat, axis=0)\n",
    "#print(rounded_yhat)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the 1999 paper “Greedy Function Approximation: A Gradient Boosting Machine“, Jerome Friedman comments on the trade-off between the number of trees (M) and the learning rate (v):\n",
    "\n",
    "The v-M trade-off is clearly evident; smaller values of v give rise to larger optimal M-values. They also provide higher accuracy, with a diminishing return for v < 0.125. The misclassification error rate is very flat for M > 200, so that optimal M-values for it are unstable. …  the qualitative nature of these results is fairly universal.\n",
    "\n",
    "He suggests to first set a large value for the number of trees, then tune the shrinkage parameter to achieve the best results. Studies in the paper preferred a shrinkage value of 0.1, a number of trees in the range 100 to 500 and the number of terminal nodes in a tree between 2 and 8. A tree depth of 3 (if the created tree was symmetrical) will have 8 leaf nodes, matching the upper bound of the preferred number of terminal nodes in Friedman’s studies (alternately max_leaf_nodes can be set). max_leaf_nodes=k gives comparable results to max_depth=k-1 but is significantly faster to train at the expense of a slightly higher training error.\n",
    "\n",
    "In the 1999 paper “Stochastic Gradient Boosting“, Friedman reiterated the preference for the shrinkage parameter:\n",
    "\n",
    "The “shrinkage” parameter 0 < v < 1 controls the learning rate of the procedure. Empirically …, it was found that small values (v <= 0.1) lead to much better generalization error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-09-24 19:49:02.542844: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "# try the GBM regressor using the same saved encoder model\n",
    "# load the model from file\n",
    "encoder = load_model('encoder.h5')\n",
    "# encode the train data\n",
    "X_train_encode = encoder.predict(X_train)\n",
    "# encode the test data\n",
    "X_test_encode = encoder.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the regression tree base learners defines the level of variable interactions that can be captured by the gradient boosting model. In general, a tree of depth h can capture interactions of order h . There are two ways in which the size of the individual regression trees can be controlled.\n",
    "\n",
    "If you specify max_depth=h then complete binary trees of depth h will be grown. Such trees will have (at most) 2**h leaf nodes and 2**h - 1 split nodes.\n",
    "\n",
    "Alternatively, you can control the tree size by specifying the number of leaf nodes via the parameter max_leaf_nodes. In this case, trees will be grown using best-first search where nodes with the highest improvement in impurity will be expanded first. A tree with max_leaf_nodes=k has k - 1 split nodes and thus can model interactions of up to order max_leaf_nodes - 1 .\n",
    "\n",
    "We found that max_leaf_nodes=k gives comparable results to max_depth=k-1 but is significantly faster to train at the expense of a slightly higher training error. The parameter max_leaf_nodes corresponds to the variable J in the chapter on gradient boosting in [F2001] and is related to the parameter interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score (acc) on test set: 0.9242\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the GBM regression model, as before.\n",
    "gbm_regressor = ensemble.GradientBoostingRegressor(**params, random_state=1)\n",
    "# fit model on training set\n",
    "gbm_regressor.fit(X_train_encode, y_train)\n",
    "# make prediction on test set\n",
    "yhat = gbm_regressor.predict(X_test_encode)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))\n",
    "importances = gbm_regressor.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in np.ravel(gbm_regressor.estimators_)],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.barh(range(X_train_encode.shape[1]), importances[indices],\n",
    "       color=\"r\", xerr=std[indices], align=\"center\")\n",
    "# If you want to define your own labels,\n",
    "# change indices to a list of labels on the following line.\n",
    "plt.yticks(range(X_train_encode.shape[1]), indices)\n",
    "plt.ylim([-1, X_train_encode.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Improvement on GBM regression model could be either higher or lower than that over Logistic Regression model using the autoencoder neural network model depending on hyperparameters used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.934 (0.023)\n",
      "The accuracy score (acc) on test set: 0.9333\n"
     ]
    }
   ],
   "source": [
    "# gradient boosting for classification in scikit-learn\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "\n",
    "# evaluate the model\n",
    "gbm_cls = GradientBoostingClassifier(random_state=1)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(gbm_cls, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))\n",
    "# fit the model on the whole dataset\n",
    "gbm_cls.fit(X_train, y_train)\n",
    "# make a single prediction\n",
    "yhat = gbm_cls.predict(X_test)\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score (acc) on test set: 0.9242\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the GBM classifier model, as before.\n",
    "# fit model on training set\n",
    "gbm_cls.fit(X_train_encode, y_train)\n",
    "# make prediction on test set\n",
    "yhat = gbm_cls.predict(X_test_encode)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "#rounded_yhat=np.argmax(yhat, axis=0)\n",
    "#print(rounded_yhat)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))\n",
    "importances = gbm_cls.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in np.ravel(gbm_cls.estimators_)],\n",
    "             axis=0)\n",
    "indices = np.argsort(importances)\n",
    "# Plot the feature importances of the forest\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.barh(range(X_train_encode.shape[1]), importances[indices],\n",
    "       color=\"r\", xerr=std[indices], align=\"center\")\n",
    "# If you want to define your own labels,\n",
    "# change indices to a list of labels on the following line.\n",
    "plt.yticks(range(X_train_encode.shape[1]), indices)\n",
    "plt.ylim([-1, X_train_encode.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The autoencoder with GBM regressor and classifier seem to come to the same results with or without GBM model hyperparameter tunnings. The best hyperparameter settings with GBM regressor alone can do better than the combined autoencoder and GBM prediction model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram-Based Gradient Boosting Machine for Classification\n",
    "\n",
    "The example below first evaluates a HistGradientBoostingClassifier on the test problem using repeated k-fold cross-validation and reports the mean accuracy. Then a single model is fit on all available data and a single prediction is made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.941 (0.026)\n",
      "The accuracy score (acc) on test set: 0.9394\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "# evaluate the model\n",
    "hgb_cls = HistGradientBoostingClassifier(random_state=1)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(hgb_cls, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))\n",
    "# fit the model on the whole dataset\n",
    "hgb_cls.fit(X_train, y_train)\n",
    "# make a single prediction\n",
    "yhat = hgb_cls.predict(X_test)\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score (acc) on test set: 0.9182\n"
     ]
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the HGB classifier model, as before.\n",
    "# fit model on training set\n",
    "hgb_cls.fit(X_train_encode, y_train)\n",
    "# make prediction on test set\n",
    "yhat = hgb_cls.predict(X_test_encode)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost for Classification\n",
    "The example below first evaluates an XGBClassifier on the test problem using repeated k-fold cross-validation and reports the mean accuracy. Then a single model is fit on all available data and a single prediction is made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:37:59] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:01] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:03] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:38:05] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Accuracy: 0.930 (0.027)\n",
      "[20:38:06] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "The accuracy score (acc) on test set: 0.9000\n"
     ]
    }
   ],
   "source": [
    "# xgboost for classification\n",
    "from numpy import asarray\n",
    "from xgboost import XGBClassifier\n",
    "# evaluate the model\n",
    "xgb_cls = XGBClassifier(random_state=1, use_label_encoder=False)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(xgb_cls, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))\n",
    "# fit the model on the whole dataset\n",
    "xgb_cls.fit(X_train, y_train)\n",
    "# make a single prediction\n",
    "yhat = xgb_cls.predict(X_test)\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:38:29] WARNING: /Users/ktietz/demo/mc3/conda-bld/xgboost-split_1628682908089/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "The accuracy score (acc) on test set: 0.9182\n"
     ]
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the HGB classifier model, as before.\n",
    "# fit model on training set\n",
    "xgb_cls.fit(X_train_encode, y_train)\n",
    "# make prediction on test set\n",
    "yhat = xgb_cls.predict(X_test_encode)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM for Classification\n",
    "The example below first evaluates an LGBMClassifier on the test problem using repeated k-fold cross-validation and reports the mean accuracy. Then a single model is fit on all available data and a single prediction is made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.943 (0.021)\n",
      "The accuracy score (acc) on test set: 0.9273\n"
     ]
    }
   ],
   "source": [
    "# lightgbm for classification\n",
    "from lightgbm import LGBMClassifier\n",
    "# evaluate the model\n",
    "lgbm_cls = LGBMClassifier(random_state=1)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "n_scores = cross_val_score(lgbm_cls, X_train, y_train, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n",
    "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))\n",
    "# fit the model on the whole dataset\n",
    "lgbm_cls.fit(X_train, y_train)\n",
    "# make a single prediction\n",
    "yhat = lgbm_cls.predict(X_test)\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score (acc) on test set: 0.9303\n"
     ]
    }
   ],
   "source": [
    "#We can then use this encoded data to train and evaluate the HGB classifier model, as before.\n",
    "# fit model on training set\n",
    "lgbm_cls.fit(X_train_encode, y_train)\n",
    "# make prediction on test set\n",
    "yhat = lgbm_cls.predict(X_test_encode)\n",
    "#rounted_yhat = np.rint(np.array(yhat))\n",
    "rounded_yhat = np.around(yhat)\n",
    "rounded_yhat = rounded_yhat.astype(int)\n",
    "# calculate accuracy\n",
    "acc = accuracy_score(y_test, rounded_yhat)\n",
    "print(\"The accuracy score (acc) on test set: {:.4f}\".format(acc))"
   ]
  }
 ],
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