exercise-binary-classification.ipynb

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   "source": [
    "**This notebook is an exercise in the [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/ryanholbrook/binary-classification).**\n",
    "\n",
    "---\n"
   ]
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   "source": [
    "# Introduction #\n",
    "\n",
    "In this exercise, you'll build a model to predict hotel cancellations with a binary classifier."
   ]
  },
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   "source": [
    "# Setup plotting\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('Solarize_Light2')\n",
    "# Set Matplotlib defaults\n",
    "plt.rc('figure', autolayout=True)\n",
    "plt.rc('axes', labelweight='bold', labelsize='large',\n",
    "       titleweight='bold', titlesize=18, titlepad=10)\n",
    "plt.rc('animation', html='html5')\n",
    "\n",
    "# Setup feedback system\n",
    "from learntools.core import binder\n",
    "binder.bind(globals())\n",
    "from learntools.deep_learning_intro.ex6 import *"
   ]
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   "source": [
    "First, load the *Hotel Cancellations* dataset."
   ]
  },
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.compose import make_column_transformer\n",
    "\n",
    "hotel = pd.read_csv('../input/dl-course-data/hotel.csv')\n",
    "\n",
    "X = hotel.copy()\n",
    "y = X.pop('is_canceled')\n",
    "\n",
    "X['arrival_date_month'] = \\\n",
    "    X['arrival_date_month'].map(\n",
    "        {'January':1, 'February': 2, 'March':3,\n",
    "         'April':4, 'May':5, 'June':6, 'July':7,\n",
    "         'August':8, 'September':9, 'October':10,\n",
    "         'November':11, 'December':12}\n",
    "    )\n",
    "\n",
    "features_num = [\n",
    "    \"lead_time\", \"arrival_date_week_number\",\n",
    "    \"arrival_date_day_of_month\", \"stays_in_weekend_nights\",\n",
    "    \"stays_in_week_nights\", \"adults\", \"children\", \"babies\",\n",
    "    \"is_repeated_guest\", \"previous_cancellations\",\n",
    "    \"previous_bookings_not_canceled\", \"required_car_parking_spaces\",\n",
    "    \"total_of_special_requests\", \"adr\",\n",
    "]\n",
    "features_cat = [\n",
    "    \"hotel\", \"arrival_date_month\", \"meal\",\n",
    "    \"market_segment\", \"distribution_channel\",\n",
    "    \"reserved_room_type\", \"deposit_type\", \"customer_type\",\n",
    "]\n",
    "\n",
    "transformer_num = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\"), # there are a few missing values\n",
    "    StandardScaler(),\n",
    ")\n",
    "transformer_cat = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\", fill_value=\"NA\"),\n",
    "    OneHotEncoder(handle_unknown='ignore'),\n",
    ")\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (transformer_num, features_num),\n",
    "    (transformer_cat, features_cat),\n",
    ")\n",
    "\n",
    "# stratify - make sure classes are evenlly represented across splits\n",
    "X_train, X_valid, y_train, y_valid = \\\n",
    "    train_test_split(X, y, stratify=y, train_size=0.75)\n",
    "\n",
    "X_train = preprocessor.fit_transform(X_train)\n",
    "X_valid = preprocessor.transform(X_valid)\n",
    "\n",
    "input_shape = [X_train.shape[1]]"
   ]
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   "source": [
    "# 1) Define Model #\n",
    "\n",
    "The model we'll use this time will have both batch normalization and dropout layers. To ease reading we've broken the diagram into blocks, but you can define it layer by layer as usual.\n",
    "\n",
    "Define a model with an architecture given by this diagram:\n",
    "\n",
    "<figure style=\"padding: 1em;\">\n",
    "<img src=\"https://storage.googleapis.com/kaggle-media/learn/images/V04o59Z.png\" width=\"400\" alt=\"Diagram of network architecture: BatchNorm, Dense, BatchNorm, Dropout, Dense, BatchNorm, Dropout, Dense.\">\n",
    "<figcaption style=\"textalign: center; font-style: italic\"><center>Diagram of a binary classifier.</center></figcaption>\n",
    "</figure>\n"
   ]
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      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n",
      "  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n",
      "/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n",
      "caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n",
      "  warnings.warn(f\"file system plugins are not loaded: {e}\")\n"
     ]
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       "parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.3333333333333333, \"interactionType\": 1, \"questionType\": 2, \"questionId\": \"1_Q1\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"
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      "text/markdown": [
       "<span style=\"color:#33cc33\">Correct</span>"
      ],
      "text/plain": [
       "Correct"
      ]
     },
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    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# YOUR CODE HERE: define the model given in the diagram\n",
    "model = keras.Sequential([\n",
    "    layers.BatchNormalization(input_shape = input_shape),\n",
    "    layers.Dense(256,activation ='relu'),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(256,activation ='relu'),\n",
    "    layers.BatchNormalization(),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(1,activation = 'sigmoid'), \n",
    "])\n",
    "\n",
    "# Check your answer\n",
    "q_1.check()"
   ]
  },
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   },
   "source": [
    "# 2) Add Optimizer, Loss, and Metric #\n",
    "\n",
    "Now compile the model with the Adam optimizer and binary versions of the cross-entropy loss and accuracy metric."
   ]
  },
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       "<span style=\"color:#33cc33\">Correct</span>"
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       "Correct"
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   "source": [
    "# YOUR CODE HERE\n",
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss='binary_crossentropy',\n",
    "    metrics=['binary_accuracy'],\n",
    ")\n",
    "\n",
    "# Check your answer\n",
    "q_2.check()"
   ]
  },
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   "source": [
    "# Lines below will give you a hint or solution code\n",
    "#q_2.hint()\n",
    "#q_2.solution()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Finally, run this cell to train the model and view the learning curves. It may run for around 60 to 70 epochs, which could take a minute or two."
   ]
  },
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     "text": [
      "Epoch 1/200\n",
      "175/175 [==============================] - 7s 7ms/step - loss: 0.4840 - binary_accuracy: 0.7703 - val_loss: 0.4282 - val_binary_accuracy: 0.8023\n",
      "Epoch 2/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.4225 - binary_accuracy: 0.8007 - val_loss: 0.3988 - val_binary_accuracy: 0.8152\n",
      "Epoch 3/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.4095 - binary_accuracy: 0.8090 - val_loss: 0.3931 - val_binary_accuracy: 0.8187\n",
      "Epoch 4/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.4020 - binary_accuracy: 0.8131 - val_loss: 0.3888 - val_binary_accuracy: 0.8205\n",
      "Epoch 5/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3968 - binary_accuracy: 0.8152 - val_loss: 0.3846 - val_binary_accuracy: 0.8208\n",
      "Epoch 6/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3925 - binary_accuracy: 0.8173 - val_loss: 0.3809 - val_binary_accuracy: 0.8219\n",
      "Epoch 7/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3892 - binary_accuracy: 0.8197 - val_loss: 0.3767 - val_binary_accuracy: 0.8244\n",
      "Epoch 8/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3847 - binary_accuracy: 0.8223 - val_loss: 0.3766 - val_binary_accuracy: 0.8270\n",
      "Epoch 9/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3836 - binary_accuracy: 0.8213 - val_loss: 0.3749 - val_binary_accuracy: 0.8268\n",
      "Epoch 10/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3793 - binary_accuracy: 0.8255 - val_loss: 0.3720 - val_binary_accuracy: 0.8283\n",
      "Epoch 11/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3772 - binary_accuracy: 0.8242 - val_loss: 0.3722 - val_binary_accuracy: 0.8281\n",
      "Epoch 12/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3750 - binary_accuracy: 0.8261 - val_loss: 0.3693 - val_binary_accuracy: 0.8329\n",
      "Epoch 13/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3714 - binary_accuracy: 0.8275 - val_loss: 0.3674 - val_binary_accuracy: 0.8304\n",
      "Epoch 14/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3701 - binary_accuracy: 0.8293 - val_loss: 0.3660 - val_binary_accuracy: 0.8322\n",
      "Epoch 15/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3684 - binary_accuracy: 0.8293 - val_loss: 0.3644 - val_binary_accuracy: 0.8341\n",
      "Epoch 16/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3655 - binary_accuracy: 0.8312 - val_loss: 0.3651 - val_binary_accuracy: 0.8334\n",
      "Epoch 17/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3648 - binary_accuracy: 0.8306 - val_loss: 0.3607 - val_binary_accuracy: 0.8346\n",
      "Epoch 18/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3634 - binary_accuracy: 0.8322 - val_loss: 0.3602 - val_binary_accuracy: 0.8364\n",
      "Epoch 19/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3617 - binary_accuracy: 0.8335 - val_loss: 0.3626 - val_binary_accuracy: 0.8344\n",
      "Epoch 20/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3596 - binary_accuracy: 0.8329 - val_loss: 0.3603 - val_binary_accuracy: 0.8364\n",
      "Epoch 21/200\n",
      "175/175 [==============================] - 1s 8ms/step - loss: 0.3583 - binary_accuracy: 0.8352 - val_loss: 0.3590 - val_binary_accuracy: 0.8362\n",
      "Epoch 22/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3573 - binary_accuracy: 0.8352 - val_loss: 0.3549 - val_binary_accuracy: 0.8384\n",
      "Epoch 23/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3561 - binary_accuracy: 0.8350 - val_loss: 0.3563 - val_binary_accuracy: 0.8354\n",
      "Epoch 24/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3550 - binary_accuracy: 0.8357 - val_loss: 0.3546 - val_binary_accuracy: 0.8384\n",
      "Epoch 25/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3534 - binary_accuracy: 0.8367 - val_loss: 0.3586 - val_binary_accuracy: 0.8373\n",
      "Epoch 26/200\n",
      "175/175 [==============================] - 1s 7ms/step - loss: 0.3518 - binary_accuracy: 0.8378 - val_loss: 0.3541 - val_binary_accuracy: 0.8383\n",
      "Epoch 27/200\n",
      "175/175 [==============================] - 1s 6ms/step - loss: 0.3510 - binary_accuracy: 0.8382 - val_loss: 0.3546 - val_binary_accuracy: 0.8384\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    patience=5,\n",
    "    min_delta=0.001,\n",
    "    restore_best_weights=True,\n",
    ")\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    validation_data=(X_valid, y_valid),\n",
    "    batch_size=512,\n",
    "    epochs=200,\n",
    "    callbacks=[early_stopping],\n",
    ")\n",
    "\n",
    "history_df = pd.DataFrame(history.history)\n",
    "history_df.loc[:, ['loss', 'val_loss']].plot(title=\"Cross-entropy\")\n",
    "history_df.loc[:, ['binary_accuracy', 'val_binary_accuracy']].plot(title=\"Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fe7329a",
   "metadata": {
    "papermill": {
     "duration": 0.043476,
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   "source": [
    "# 3) Train and Evaluate #\n",
    "\n",
    "\n",
    "What do you think about the learning curves? Does it look like the model underfit or overfit? Was the cross-entropy loss a good stand-in for accuracy?"
   ]
  },
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   "outputs": [
    {
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       "<span style=\"color:#33cc33\">Correct:</span> \n",
       "\n",
       "Though we can see the training loss continuing to fall, the early stopping callback prevented any overfitting. Moreover, the accuracy rose at the same rate as the cross-entropy fell, so it appears that minimizing cross-entropy was a good stand-in. All in all, it looks like this training was a success!"
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       "Correct: \n",
       "\n",
       "Though we can see the training loss continuing to fall, the early stopping callback prevented any overfitting. Moreover, the accuracy rose at the same rate as the cross-entropy fell, so it appears that minimizing cross-entropy was a good stand-in. All in all, it looks like this training was a success!"
      ]
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   ],
   "source": [
    "# View the solution (Run this cell to receive credit!)\n",
    "q_3.check()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Conclusion #\n",
    "\n",
    "Congratulations! You've completed Kaggle's *Introduction to Deep Learning* course!\n",
    "\n",
    "With your new skills you're ready to take on more advanced applications like computer vision and sentiment classification. What would you like to do next?\n",
    "\n",
    "Why not try one of our *Getting Started* competitions?\n",
    "\n",
    "- Classify images with TPUs in [**Petals to the Metal**](https://www.kaggle.com/c/tpu-getting-started)\n",
    "- Create art with GANs in [**I'm Something of a Painter Myself**](https://www.kaggle.com/c/gan-getting-started)\n",
    "- Classify Tweets in [**Real or Not? NLP with Disaster Tweets**](https://www.kaggle.com/c/nlp-getting-started)\n",
    "- Detect contradiction and entailment in [**Contradictory, My Dear Watson**](https://www.kaggle.com/c/contradictory-my-dear-watson)\n",
    "\n",
    "Until next time, Kagglers!"
   ]
  },
  {
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   "source": [
    "---\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/intro-to-deep-learning/discussion) to chat with other learners.*"
   ]
  }
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