AI_Cancer_Neural_Net.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9265f8a-7ab6-40a0-8284-a76c6d9cfbab",
   "metadata": {},
   "source": [
    "# Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0cfe5de1-001c-444b-ab7d-bb34ff752f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install scikit-learn tensorflow tensorflow[and-cuda]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389ca91a-9c19-4d8a-b5bb-4855474de78b",
   "metadata": {},
   "source": [
    "## Import the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd2d5d3c-1d86-4a63-8e91-b4809501164a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#!wget https://gist.github.com/KhanradCoder/35a6beea49e5b9ba62797e595a9626c0/raw/8974e055bdf3a9d7e6cacf1c1c30fcfd2ffd6de3/cancer.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ff8caf4-1f5b-48a0-b881-f7146c6efc6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2205954/3957816590.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv('cancer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40378c95-8af3-4504-baf0-763f139fb19f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 = malign, 0 = beningn tumor\n",
    "x=dataset.drop(columns=[\"diagnosis(1=m, 0=b)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "55c19f07-037e-4ac5-b6e5-bb114cbfcaa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataset[\"diagnosis(1=m, 0=b)\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89bbcb0-c7c5-4db0-974a-2ac03e0af7c8",
   "metadata": {},
   "source": [
    "## Split the data into a training set and a testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "604e8f7e-7412-46b5-8b8d-5beda0a7c2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dab18c0-8010-4c15-b247-0eb2013006ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-14 14:31:55.888761: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "model=tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9497e5f7-0f7a-4704-b10c-4c9400b25a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455, 30)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99bed0f3-3743-4cf4-b60e-a7f67c62d9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/goncalo/ai/jupyterlab/venv/lib/python3.10/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
      "2024-05-14 14:31:56.629564: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-14 14:31:56.630033: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-14 14:31:56.630450: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-14 14:31:56.630854: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-14 14:31:56.631256: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-05-14 14:31:56.631943: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "model.add(tf.keras.layers.Dense(256, input_shape=(x_train.shape[1],), activation='sigmoid'))\n",
    "model.add(tf.keras.layers.Dense(256, activation='sigmoid'))\n",
    "model.add(tf.keras.layers.Dense(128, activation='sigmoid'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa76ab37-4770-42e2-b731-ae773a5a22aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
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       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,936</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,792</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">129</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m7,936\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m65,792\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │           \u001b[38;5;34m129\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106,753</span> (417.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m106,753\u001b[0m (417.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">106,753</span> (417.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m106,753\u001b[0m (417.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8b9ca50-c5e5-4e9e-be1e-a86d07105572",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e45b3cf5-d54a-4182-9d84-af4e3cf9f127",
   "metadata": {},
   "outputs": [],
   "source": [
    "history= model.fit(x_train, y_train, epochs=1000, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "354d8f70-0b5e-4d52-8bdd-09909030d449",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['accuracy', 'loss'])\n"
     ]
    }
   ],
   "source": [
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e66b1b0e-9c7c-4663-a480-86085d71fd34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49eb6bc8-5340-4cc6-8eb3-5c1eb876fc9c",
   "metadata": {},
   "source": [
    "# Summarize history for accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b9fbb6c1-2416-452d-8104-dcf5448d0492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "#plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1245509a-7489-4c92-9538-17f01e1f1869",
   "metadata": {},
   "source": [
    "# Summarize history for loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5ac6d17-6988-45c5-a3d4-d0d3a6a226b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "#plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "680a256a-5902-4753-8a3c-bea6f5f169eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}