tutorial_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets, svm, cluster\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [[0,0], [1,1]]\n",
    "y = [0,1]\n",
    "clf = svm.SVC()\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[2,2], [-1,-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "================================\n",
      "SVM Exercise\n",
      "================================\n",
      "\n",
      "A tutorial exercise for using different SVM kernels.\n",
      "\n",
      "This exercise is used in the :ref:`using_kernels_tut` part of the\n",
      ":ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.\n",
      "src: http://scikit-learn.org/stable/_downloads/plot_iris_exercise.py\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "================================\n",
    "SVM Exercise\n",
    "================================\n",
    "\n",
    "A tutorial exercise for using different SVM kernels.\n",
    "\n",
    "This exercise is used in the :ref:`using_kernels_tut` part of the\n",
    ":ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.\n",
    "src: http://scikit-learn.org/stable/_downloads/plot_iris_exercise.py\n",
    "\"\"\"\n",
    "print(__doc__)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "X = X[y != 0, :2]\n",
    "y = y[y != 0]\n",
    "\n",
    "n_sample = len(X)\n",
    "\n",
    "np.random.seed(0)\n",
    "order = np.random.permutation(n_sample)\n",
    "X = X[order]\n",
    "y = y[order].astype(np.float)\n",
    "\n",
    "X_train = X[:.9 * n_sample]\n",
    "y_train = y[:.9 * n_sample]\n",
    "X_test = X[.9 * n_sample:]\n",
    "y_test = y[.9 * n_sample:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  1.,  2.,  2.,  2.,  1.,  2.,  2.,  2.,  2.,  2.,  2.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  2.,  1.,  2.,  1.,  1.,\n",
       "        1.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  1.,  1.,  2.,  2.,\n",
       "        2.,  1.,  2.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  2.,  2.,\n",
       "        1.,  1.,  1.,  2.,  1.,  1.,  1.,  2.,  1.,  1.,  2.,  2.,  2.,\n",
       "        2.,  1.,  2.,  1.,  2.,  1.,  1.,  1.,  2.,  2.,  2.,  1.,  2.,\n",
       "        2.,  2.,  1.,  1.,  2.,  1.,  1.,  2.,  2.,  1.,  2.,  2.,  2.,\n",
       "        1.,  1.,  2.,  1.,  2.,  2.,  2.,  1.,  1.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d4dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d4df10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107e1f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fit the model\n",
    "for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):\n",
    "    clf = svm.SVC(kernel=kernel, gamma=10)\n",
    "    clf.fit(X_train, y_train)\n",
    "\n",
    "    plt.figure(fig_num)\n",
    "    plt.clf()\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired)\n",
    "\n",
    "    # Circle out the test data\n",
    "    plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)\n",
    "\n",
    "    plt.axis('tight')\n",
    "    x_min = X[:, 0].min()\n",
    "    x_max = X[:, 0].max()\n",
    "    y_min = X[:, 1].min()\n",
    "    y_max = X[:, 1].max()\n",
    "\n",
    "    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n",
    "    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(XX.shape)\n",
    "    plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)\n",
    "    plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],\n",
    "                levels=[-.5, 0, .5])\n",
    "\n",
    "    plt.title(kernel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_csv(\"omit/ai2_data.csv\")\n",
    "df_work = pd.DataFrame.from_csv(\"omit/ai2_data_work.csv\")\n",
    "NUM_PROJECTS = len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ball_bounce': (109, 112), 'paintpot': (162, 185), 'i_have_dream': (37, 42), 'digital_doodle:': (67, 71), 'video_wall': (3, 3), 'pic_call': (4, 4), 'magic_8_ball': (61, 68), 'mash': (122, 144), 'talk_to_me': (133, 152), 'space_invaders': (29, 30), 'mini_golf': (20, 21), 'stockquotes': (9, 9), 'hello_purr': (66, 82), 'no_text': (12, 14)}\n",
      "total tutorials: 937(17.922723795%)\n",
      "total working tutorials: 834(15.9525631217%)\n"
     ]
    }
   ],
   "source": [
    "# Finding tutorials by name\n",
    "tutorials = {\"hello_purr\":\"purr\",\n",
    "             \"talk_to_me\":\"talk\",\n",
    "             \"ball_bounce\":\"bounce\",\n",
    "             \"digital_doodle:\" : \"doodle\",\n",
    "             \"magic_8_ball\": \"magic\",\n",
    "             \"paintpot\" : \"paint\",\n",
    "             \"i_have_dream\" : \"dream\",\n",
    "             \"pic_call\": \"piccall\",\n",
    "             \"video_wall\": \"videowall|video_wall\",\n",
    "             \"stockquotes\": \"stockquotes|stock_quotes\",\n",
    "             \"mini_golf\" : \"minigolf|mini_golf\",\n",
    "             \"space_invaders\" : \"spaceinvader|space_invader\",\n",
    "             \"no_text\":\"notext|driving\",\n",
    "             \"mash\": \"mash\"}\n",
    "\n",
    "tutorials_count = {}\n",
    "total_tutorials = 0\n",
    "total_working_tutorials = 0\n",
    "\n",
    "for entry in tutorials:\n",
    "    search_term = tutorials[entry]\n",
    "    count_work = len(df_work[df_work.project_name.str.contains(search_term)])\n",
    "    count_all = len(df[df.project_name.str.contains(search_term)])\n",
    "    tutorials_count[entry] = count_work, count_all\n",
    "\n",
    "    total_working_tutorials += count_work\n",
    "    total_tutorials += count_all\n",
    "\n",
    "print tutorials_count\n",
    "print \"total tutorials: \" + str(total_tutorials) + \"(\" + str(float(total_tutorials)/ NUM_PROJECTS*100) +\"%)\"\n",
    "print \"total working tutorials: \" + str(total_working_tutorials) + \"(\" + str(float(total_working_tutorials)/ NUM_PROJECTS*100) +\"%)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>project_name</th>\n",
       "      <th>user_name</th>\n",
       "      <th>num_screens</th>\n",
       "      <th>b_component_event</th>\n",
       "      <th>b_component_method</th>\n",
       "      <th>b_component_set_get</th>\n",
       "      <th>b_lexical_variable_get</th>\n",
       "      <th>b_logic_false</th>\n",
       "      <th>b_math_add</th>\n",
       "      <th>...</th>\n",
       "      <th>b_math_convert_angles</th>\n",
       "      <th>b_lists_to_csv_table</th>\n",
       "      <th>b_math_trig</th>\n",
       "      <th>b_math_ceiling</th>\n",
       "      <th>b_math_tan</th>\n",
       "      <th>b_lists_to_csv_row</th>\n",
       "      <th>b_lists_append_list</th>\n",
       "      <th>b_math_random_set_seed</th>\n",
       "      <th>c_ProximitySensor</th>\n",
       "      <th>b_obsufcated_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 174 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, project_name, user_name, num_screens, b_component_event, b_component_method, b_component_set_get, b_lexical_variable_get, b_logic_false, b_math_add, b_math_division, b_math_number, b_math_random_int, b_math_subtract, c_Button, c_Canvas, c_Clock, c_HorizontalArrangement, num_blocks, num_components, b_color_blue, b_color_cyan, b_color_magenta, b_color_orange, b_color_pink, b_color_yellow, c_Label, c_TableArrangement, b_color_green, b_color_make_color, b_color_red, b_global_declaration, b_lists_create_with, c_AccelerometerSensor, c_Slider, b_controls_if, b_lexical_variable_set, b_logic_boolean, b_math_compare, b_text, b_text_join, c_Camera, c_VerticalArrangement, b_logic_compare, b_procedures_callnoreturn, b_procedures_defnoreturn, b_text_contains, c_ActivityStarter, c_LocationSensor, c_Notifier, c_Sound, c_TextToSpeech, c_TextBox, c_Image, c_ListPicker, c_ListView, c_PasswordTextBox, c_WebViewer, b_math_multiply, b_controls_forRange, b_lists_is_list, b_lists_length, b_lists_select_item, b_text_split, c_TinyWebDB, b_text_compare, c_OrientationSensor, c_Player, c_SoundRecorder, c_CheckBox, c_SpeechRecognizer, b_color_black, b_color_gray, b_math_random_float, b_logic_or, b_text_isEmpty, b_component_component_block, b_controls_openAnotherScreen, c_PhoneCall, b_controls_forEach, b_controls_closeApplication, b_controls_closeScreen, c_TinyDB, b_controls_eval_but_ignore, c_BluetoothClient, b_color_white, b_text_changeCase, b_local_declaration_statement, b_math_floor, c_Web, b_controls_while, b_logic_operation, b_lists_add_items, b_text_length, c_Texting, c_PhoneNumberPicker, b_logic_negate, c_Spinner, b_procedures_callreturn, b_procedures_defreturn, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 174 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Testing clustering (really datatypes)\n",
    "features = ['num_blocks', 'num_components']\n",
    "\n",
    "data = df[features].values.astype(float) #going from df to array\n",
    "\n",
    "n_samples, n_features = data.shape\n",
    "n_clusters = 3;\n",
    "\n",
    "k_means = cluster.KMeans(n_clusters=n_clusters)\n",
    "k_means.fit(data)\n",
    "est = k_means.labels_[::10]\n",
    "# for i in range(len(est)):\n",
    "#     if est[i]!=0:\n",
    "        #print i\n",
    "x = [i for i in est if est[i]!=0 ]        \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 48.,   4.],\n",
       "       [  5.,   1.],\n",
       "       [ 19.,   5.],\n",
       "       ..., \n",
       "       [  0.,   0.],\n",
       "       [  8.,   3.],\n",
       "       [ 35.,   7.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-4bd7afb0c041>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plot' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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