useful-python-script-for-research.ipynb

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    "# Useful Scripts for Research\n",
    "\n",
    "_Summary:_\n",
    "This notebook is updated regularly based on the necessity and anytime I find anything useful for my learning and research using Python, git-bash and github.\n",
    "\n",
    "> ## Table of Contents\n",
    "> - Markdown Shorcuts\n",
    "> - Data Preparation\n",
    "> - Advance Scripts\n",
    "> - Git & Github"
   ]
  },
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    "## Markdown Shortcuts\n",
    "\n",
    "- Run = `shift` + 'enter'/'return'\n",
    "- insert tab above = `esc` + a \n",
    "- insert tab below = `esc` + b\n",
    "- convert cell into `Markdown` = `esc` + m\n",
    "- convert cell into 'Code' = `esc` + y\n",
    "- undo = `esc` + z\n",
    "- redo = `esc` + shift + z\n",
    "- copy = `esc` + c\n",
    "\n",
    "\n",
    "- inserting equation stats and end with 'dollar sign', such as $f(x) = x^2$\n",
    "- inserting picture: \n",
    "\n",
    "- inserting web/links: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation (Python)\n",
    "\n",
    "getting the column name\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This following code is from this [source](https://github.com/gedeck/practical-statistics-for-data-scientists/blob/master/python/notebooks/Chapter%201%20-%20Exploratory%20Data%20Analysis.ipynb). It is used to create a relative directory, to access data contained in the folder called `./data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "try:\n",
    "    import common\n",
    "    DATA = common.dataDirectory()\n",
    "except ImportError:\n",
    "    DATA = Path().resolve() / 'data'"
   ]
  },
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   "source": [
    "Parsing multiple name\n",
    "\n",
    "Cek on: https://www.youtube.com/watch?v=ve2pmm5JqmI"
   ]
  },
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   "source": [
    "# Advance Scripts\n",
    "\n",
    "### Processing a single script for multiple similar files"
   ]
  },
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   "source": [
    "## Matrix Operation\n",
    "\n",
    "The following codes are for conducting matrix operation using Python.\n",
    "\n",
    "1. Using `numpy`\n",
    "2. Using `pytorch`\n",
    "3. Using `tensorflow`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 27],\n",
       "       [17, 45],\n",
       "       [23, 63]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "A = np.array([[3, 4],[5, 6],[7, 8]]) # A matrix with 3x2 dimension\n",
    "B = np.array([[1, 9], [2, 0]]) # B matrix with 2x2 dimension\n",
    "AB = np.dot(A,B)\n",
    "AB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.5       ],\n",
       "       [ 0.11111111, -0.05555556]])"
      ]
     },
     "execution_count": 10,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ainv = np.linalg.inv(B)\n",
    "Ainv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Using `torch`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1, 9],\n",
       "        [2, 0]], dtype=torch.int32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "B_pt = torch.from_numpy(B) # much cleaner than TF conversion\n",
    "B_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement tensorflow\n",
      "ERROR: No matching distribution found for tensorflow\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-64156d691fe5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
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   "execution_count": 14,
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement tensorflow\n",
      "ERROR: No matching distribution found for tensorflow\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
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  {
   "cell_type": "code",
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  {
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  {
   "cell_type": "markdown",
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   "source": [
    "## Machine Learning\n",
    "\n",
    "### ML using `keras`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import SGD\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 3s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_valid, y_valid) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s examine these data. As mentioned in Chapter 4, the mathematical notation x is used to represent the data we’re feeding into a model as input, while y is used for the labeled output that we’re training the model to predict. With this in mind, `X_train` stores the MNIST digits we’ll be training our model on.9 Executing `X_train.shape` yields the output `(60000, 28, 28)`. This shows us that, as expected, we have 60,000 images in our training dataset, each of which is a 28×28 matrix of values. Running y_train.shape, we unsurprisingly discover we have 60,000 labels indicating what digit is contained in each of the 60,000 training images. y_train[0:12] outputs an array of 12 integers representing the first dozen labels, so we can see that the first handwritten digit in the training set `(X_train[0])` is the number five, the second is a zero, the third is a four, and so on.\n",
    "\n",
    "\n",
    "9. The convention is to use an uppercase letter like $X$ when the variable being represented is a __two-dimensional matrix__ or a __data structure with even higher dimensionality__. In contrast, a lowercase letter like $x$ is used to represent a __single value (a scalar)__ or a __one-dimensional array__."
   ]
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\n",
      "text/plain": [
       "<Figure size 360x360 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "for k in range(12):\n",
    "    plt.subplot(3,4, k+1)\n",
    "    plt.imshow(X_train[k], cmap='Greys')\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(60000, 784).astype('float32')\n",
    "X_valid = X_valid.reshape(10000, 784).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Git and Github\n",
    "\n",
    "## Git Command\n",
    "\n",
    "* `mkdir` --> to make directory/folder\n",
    "* `cd {dirName}` --> to go to {dirName}\n",
    "* `cd ..` --> to go to upper folder\n",
    "* `ls` --> to get list of folder & files\n",
    "* `ls -a` --> to get all lists includes git\n",
    "* `touch {fileName.ext}` --> to create a file with specific extension\n",
    "* `mv {oldFileName.ext} {newFileName.ext}` --> Change the name of file\n",
    "* `touch new-folder/style.css` --> create file in specific folder\n",
    "* `ls new-folder/` --> check list of files in specific folder\n",
    "* `cp style.css` .. --> copy file in the upper folder\n",
    "* `rm style.css` --> to remove a file\n",
    "* `rm -rf {folderName}` --> to remove folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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