badge_calc.ipynb

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  {
   "cell_type": "markdown",
   "id": "fa6b2857",
   "metadata": {},
   "source": [
    "# Badge Calculations \n",
    "\n",
    "```{important} \n",
    "This page is generated with code and calculations, you can view them for more precise implementations of what the english sentences mean. \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "51238030",
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import date,timedelta\n",
    "import calendar\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "from myst_nb import glue\n",
    "import plotly.express as px\n",
    "# learning complexities\n",
    "weights_mrw = {'experience' :2,'review': 3,'practice': 6,'explore': 9,'build' :36}\n",
    "# base grade influence cutoffs\n",
    "thresh_mrw = {'D ':22*weights_mrw['experience'], 'C ':22*weights_mrw['experience']+18*weights_mrw['review'],\n",
    "          'B ':22*weights_mrw['experience']+18*weights_mrw['practice'],\n",
    "          'A ': 22*weights_mrw['experience']+18*weights_mrw['practice'] + 6*weights_mrw['explore']}\n",
    "\n",
    "examples_mrw = [[22,0,0,0,0],[23,18,0,0,0],[22,24,0,0,0],[24,18,6,0,0],\n",
    "                [22,18,0,0,1],[26,0,18,0,0],[24,12,12,0,0],[24,0,24,0,0],\n",
    "                [25,0,18,6,0],[25,0,18,4,0],[24,0,18,0,1],\n",
    "                [22,18,0,0,3],[24,6,12,4,1],[24,12,6,2,2],[24,0,24,2,0]]\n",
    "\n",
    "ex_df_mrw = pd.DataFrame(data=examples_mrw,columns = weights_mrw.keys())\n",
    "w_df_mrw = pd.DataFrame(data=[(ex_df_mrw[col]*weights_mrw[col]) for col in weights_mrw]).T.rename(columns=lambda col:col + '_weight')\n",
    "w_df_mrw['total'] = w_df_mrw.sum(axis=1)\n",
    "# [ex_df[col]*weights[col] for col in weights]\n",
    "# columns = [col + '_weight' for col in weights ]\n",
    "badge_thresh_df_mrw = pd.concat([ex_df_mrw,w_df_mrw],axis=1).reset_index()\n",
    "\n",
    "\n",
    "delta = thresh_mrw['C ']-thresh_mrw['D ']\n",
    "minus_thresh = {k.strip()+'-':int(v-delta/3) for k,v in thresh_mrw.items()}\n",
    "minus_thresh\n",
    "\n",
    "plus_thresh = {k.strip()+'+':int(v+delta/3) for k,v in thresh_mrw.items()}\n",
    "\n",
    "# calculate the final thresholds for all leter grades\n",
    "thresh_mrw.update(minus_thresh)\n",
    "thresh_mrw.update(plus_thresh)\n",
    "thresh_mrw.pop('A+');\n",
    "thresh_mrw.pop('D-');\n",
    "\n",
    "ind_badges_mrw = [[exid,bt,bw,bc,1] for i,ex in enumerate(examples_mrw) for bc,(bt,bw) in zip(ex,weights_mrw.items()) \n",
    "                                      for exid in bc*[i]]\n",
    "per_badge_df_mrw = pd.DataFrame(ind_badges_mrw,columns=['example','badge','complexity','count','weight'])\n",
    "\n",
    "# weight community badges, \n",
    "community_weights = {'plus': 3*weights_mrw['practice']/10,'experience_makeup':weights_mrw['experience']/3,\n",
    "                    'review_makeup':weights_mrw['review']/4,\n",
    "                     'review_upgrade': (weights_mrw['practice']-weights_mrw['review'])/3,\n",
    "                    'practice_makeup':weights_mrw['practice']/7}\n",
    "\n",
    "learning_df = pd.Series(weights_mrw,name ='complexity').reset_index()\n",
    "learning_df['badge_type'] = 'learning'\n",
    "comm_df  = pd.Series(community_weights,name='weight').reset_index()\n",
    "comm_df['badge_type'] = 'community'\n",
    "# nans are for learning badges which all ahve weight 1\n",
    "influence_df = pd.concat([learning_df,comm_df]).fillna(1).rename(columns={'index':'badge'})\n",
    "# final df\n",
    "influence_df['influence'] = influence_df['complexity']*influence_df['weight']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94113b6",
   "metadata": {},
   "source": [
    "Grade cutoffs for total influence are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c6f0b2ef",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>threshold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>letter</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D+</th>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C-</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C+</th>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B-</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B+</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A-</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        threshold\n",
       "letter           \n",
       "D              44\n",
       "D+             62\n",
       "C-             80\n",
       "C              98\n",
       "C+            116\n",
       "B-            134\n",
       "B             152\n",
       "B+            170\n",
       "A-            188\n",
       "A             206"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th_list = [[k,v] for k,v in thresh_mrw.items()]\n",
    "pd.DataFrame(th_list, columns = ['letter','threshold']).sort_values(by='threshold').set_index('letter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db071c2c",
   "metadata": {},
   "source": [
    "The total influence of each badge is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "300f22db",
   "metadata": {
    "tags": [
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>badge_type</th>\n",
       "      <th>badge</th>\n",
       "      <th>complexity</th>\n",
       "      <th>weight</th>\n",
       "      <th>influence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>learning</td>\n",
       "      <td>experience</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>learning</td>\n",
       "      <td>review</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>learning</td>\n",
       "      <td>practice</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>learning</td>\n",
       "      <td>explore</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>learning</td>\n",
       "      <td>build</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>community</td>\n",
       "      <td>plus</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>community</td>\n",
       "      <td>experience_makeup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>community</td>\n",
       "      <td>review_makeup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>community</td>\n",
       "      <td>review_upgrade</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>community</td>\n",
       "      <td>practice_makeup</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  badge_type              badge  complexity    weight  influence\n",
       "0   learning         experience         2.0  1.000000   2.000000\n",
       "1   learning             review         3.0  1.000000   3.000000\n",
       "2   learning           practice         6.0  1.000000   6.000000\n",
       "3   learning            explore         9.0  1.000000   9.000000\n",
       "4   learning              build        36.0  1.000000  36.000000\n",
       "0  community               plus         1.0  1.800000   1.800000\n",
       "1  community  experience_makeup         1.0  0.666667   0.666667\n",
       "2  community      review_makeup         1.0  0.750000   0.750000\n",
       "3  community     review_upgrade         1.0  1.000000   1.000000\n",
       "4  community    practice_makeup         1.0  0.857143   0.857143"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display\n",
    "influence_df[['badge_type','badge','complexity','weight','influence']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9e9201ab",
   "metadata": {
    "tags": [
     "hide-input"
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   "outputs": [],
   "source": [
    "# example calculation helper functions\n",
    "def commumunity_example(badges,exid,cw_type):\n",
    "    case_weights = weights_mrw.copy()\n",
    "    case_weights['community'] = community_weights[cw_type]\n",
    "    return [[exid,bt,bw,bc] for bc,(bt,bw) in zip(badges,case_weights.items()) \n",
    "                                      for i in range(bc)]\n",
    "\n",
    "def commumunity_example(badges,exid,cw_type):\n",
    "    case_weights = weights_mrw.copy()\n",
    "    if type(cw_type)==list:\n",
    "        for cw in cw_type:\n",
    "            case_weights['community_'+cw] = community_weights[cw]\n",
    "    else:\n",
    "        case_weights['community'] = community_weights[cw_type]\n",
    "#     print(badge)\n",
    "    return [[exid,bt,bt.split('_')[0],bw,bc] for bc,(bt,bw) in zip(badges,case_weights.items()) \n",
    "                                      for i in range(bc)]\n",
    "# \n",
    "\n",
    "\n",
    "def get_row(exid,badge_detail,badge_category,badge_mult,badge_count):\n",
    "    if 'community' in badge_detail:\n",
    "        badge_complexity = 1\n",
    "        badge_weight = badge_mult\n",
    "    else: \n",
    "        badge_complexity = badge_mult\n",
    "        badge_weight = 1\n",
    "    \n",
    "    badge_row = [exid,badge_detail,badge_category,\n",
    "                 badge_complexity, badge_weight,badge_count]\n",
    "    return badge_row\n",
    "\n",
    "def commumunity_example_comp_w(badges,exid,cw_type):\n",
    "    case_weights = weights_mrw.copy()\n",
    "    if type(cw_type)==list:\n",
    "        for cw in cw_type:\n",
    "            case_weights['community_'+cw] = community_weights[cw]\n",
    "    else:\n",
    "        case_weights['community'] = community_weights[cw_type]\n",
    "#     print(badge)\n",
    "    return [get_row(exid,bt,bt.split('_')[0],bw,bc) for bc,(bt,bw) in zip(badges,case_weights.items()) \n",
    "                                      for i in range(bc)]\n",
    "# make more examples\n",
    "ind_badges_mrw_c = [get_row(str(exid),bt,bt,bw,bc) for i,ex in enumerate(examples_mrw) for bc,(bt,bw) in zip(ex,weights_mrw.items()) \n",
    "                                      for exid in bc*[i]]\n",
    "\n",
    "examples_mrw_cplus = [[24,0,0,0,0,10],[24,18,0,0,0,10],[24,0,18,0,0,10],[24,12,12,0,0,10],\n",
    "                     [24,0,18,0,1,10],[24,12,12,2,0,10]]\n",
    "ind_badges_mrw_c +=[r for i,ex in enumerate(examples_mrw_cplus) \n",
    "                    for r in commumunity_example_comp_w(ex,str(i)+'cp',['plus'])]\n",
    "examples_mrw_em = [[22,0,0,0,0,6],[21,18,0,0,0,9],[23,0,18,0,0,13],[20,12,12,0,0,12],[20,12,12,0,0,12]]\n",
    "ind_badges_mrw_c +=[r for i,ex in enumerate(examples_mrw_em) \n",
    "                    for r in commumunity_example_comp_w(ex,str(i)+'cem',['experience_makeup'])]\n",
    "examples_mrw_rm = [[22,17,0,0,0,6,4],[24,10,12,0,0,0,8]]\n",
    "ind_badges_mrw_c +=[r for i,ex in enumerate(examples_mrw_rm) for r in \n",
    "                    commumunity_example_comp_w(ex,str(i)+'cemrm',['experience_makeup','review_makeup'])]\n",
    "examples_mrw_ru = [[24,0,16,0,0,0,0,14],[23,1,16,0,0,3,3,7]]\n",
    "ind_badges_mrw_c +=[r for i,ex in enumerate(examples_mrw_ru) for r in \n",
    "                    commumunity_example_comp_w(ex,str(i)+'cemrupm',['experience_makeup','review_upgrade','practice_makeup'])]\n",
    "# ind_badges_mrw_c\n",
    "\n",
    "per_badge_df_mrw_c = pd.DataFrame(ind_badges_mrw_c,columns=['example','badge_det','badge',\n",
    "                                                            'complexity','weight','count'])\n",
    "per_badge_df_mrw_c['influence'] = per_badge_df_mrw_c['complexity']*per_badge_df_mrw_c['weight']\n",
    "\n",
    "# get example letter grades\n",
    "grade_calc = lambda df: (np.floor((df.sum()-48)/(delta/3))*(delta/3)+48).astype(int)\n",
    "ex_grade = per_badge_df_mrw_c.groupby(['example'])['influence'].apply(grade_calc).reset_index().rename(\n",
    "                    columns = {'influence':'influence_total'})\n",
    "\n",
    "# get letter for each example\n",
    "thresh_mrw_rv = {v:k for k, v in thresh_mrw.items()}\n",
    "thresh_mrw_rv[0] = 'F'\n",
    "thresh_mrw_cutoffs = np.asarray(sorted(thresh_mrw_rv))\n",
    "ex_grade['letter'] = ex_grade['influence_total'].apply(lambda v: thresh_mrw_rv[thresh_mrw_cutoffs[(v > thresh_mrw_cutoffs)].max()])\n",
    "\n",
    "# improve sample names\n",
    "example_names = {old:'example ' +str(i) for i,old in enumerate(list(ex_grade['example']))}\n",
    "ex_grade['example'] = ex_grade['example'].replace(example_names)\n",
    "per_badge_df_mrw_c['example'] = per_badge_df_mrw_c['example'].replace(example_names)\n",
    "# ex_grade = ((per_badge_df_mrw_c.groupby(['example'])['influence'].sum()-48).floordiv((delta/3))*(delta/3)+48).astype(int).reset_index()\n",
    "\n",
    "\n",
    "per_badge_df_mrw_cletter = pd.merge(per_badge_df_mrw_c,ex_grade[['example','letter','influence_total']],on='example')\n",
    "\n",
    "# sort to improve visual display\n",
    "badges_in_order = ['experience','community_experience_makeup', 'review',\n",
    "                   'community_review_makeup','community_review_upgrade',\n",
    "                  'practice','community_practice_makeup', 'explore', 'build','community_plus']\n",
    "badge_order_num ={b:i for i,b in enumerate(badges_in_order)}\n",
    "per_badge_df_mrw_cletter['badge_num'] = per_badge_df_mrw_cletter['badge_det'].apply(lambda bd:badge_order_num[bd])\n",
    "\n",
    "# per_badge_df_mrw_c= per_badge_df_mrw_c.sort_values(by='example')\n",
    "per_badge_df_mrw_cletter = per_badge_df_mrw_cletter.sort_values(by=['badge_num','influence_total'],ascending=True,)"
   ]
  },
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          "example 29",
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          "example 25",
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          "example 28",
          "example 28",
          "example 28",
          "example 8",
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          "example 23",
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       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make plot\n",
    "fig_cur = px.bar(per_badge_df_mrw_cletter,x='example',y='influence',color='badge_det',\n",
    "                 hover_data=['badge','count','letter'],height=700)\n",
    "fig_cur.update_layout(yaxis = dict(tickmode='array',\n",
    "                              tickvals = list(thresh_mrw.values()),\n",
    "                              ticktext =list(thresh_mrw.keys()),\n",
    "                                  title='grade'))\n",
    "# fig.update_layout( xaxis={'categoryorder':'array','categoryarray':per_badge_df_mrw_cletter['influence_total'].unique()})\n",
    "fig_cur"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0302d90",
   "metadata": {},
   "source": [
    "```{important}\n",
    "the labels on the horizontal axis are just example names, they do not have any meaning, I just have not figured out what I want to replace them with that might have meaning and need some sort of unique identifier there for the plot to work. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d2dfff",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "Officially what is on the [](grading) page is what applies if this page is in conflict with that.  \n",
    "```\n",
    "\n",
    "The total influence of a badge on your grade is the product of the badge's weight and its complexity.  All learning badges have a weight of 1, but have varying complexity.  All community badges have a complexity of 1, but the weight of a community badge can vary depending on what learning badges you earn. \n",
    "\n",
    "There are also some hard thresholds on learning badges. \n",
    "You must have:\n",
    "- 22 experience badges to earn a D or above\n",
    "- at least 18 additional badges total across review and practice to earn above C or above\n",
    "\n",
    "Only community badges can make exceptions to these thresholds. So if you are missing learning badges required to get to a threshold, your community badges will fill in for those.  If you meet all of the thresholds, the community badges will be applied with more weight to give you a step up (eg C to C+ or B+ to A-). \n",
    "\n",
    "Community badges have the most weight if you are on track for a grade between D and B+  \n",
    "\n",
    "If you are on track for an A, community badges can be used to fill in for learning badges, so for example, at the end of the semester, you might be able to skip some the low complexity learning badges (experience, review, practice) and focus on your high complexity ones to ensure you get an A. \n",
    "\n",
    "More precisely the order of application for community badges: \n",
    "- to make up missing experience badges \n",
    "- to make up for missing review or practice badge to reach a total of 18 between these two types\n",
    "- to upgrade review to practice to meet a threshold\n",
    "- to give a step up (highest weight)"
   ]
  }
 ],
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