vTaiwan_Uber v0.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pol-is/notebooks/blob/master/020-PCA.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# vTaiwan Uber 🏟 Polis conversation statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import altair as alt\n",
    "from textwrap import wrap\n",
    "from sklearn.neighbors import kneighbors_graph\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "import numba\n",
    "\n",
    "\n",
    "import umap\n",
    "\n",
    "import igraph as ig\n",
    "import leidenalg\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Set up plots\n",
    "plt.figure(figsize=(500, 500))\n",
    "sns.set_context('poster')\n",
    "sns.set_style('white')\n",
    "sns.set(font=\"Gill Sans\")\n",
    "sns.set(font_scale=.7)\n",
    "sns.set_color_codes()\n",
    "\n",
    "%matplotlib inline\n",
    "np.seterr(divide='ignore', invalid='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🧹 Import raw data && clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./participants-votes.csv',index_col='participant')\n",
    "df_comments = pd.read_csv('./comments_mod1_en_aug.csv',index_col='comment-id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_comments_integerIndex = df_comments;\n",
    "df_comments.index = df_comments.index.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_fields = ['group-id', 'n-comments', 'n-votes', \n",
    "                   'n-agree', 'n-disagree']\n",
    "val_fields = [c for c in df.columns.values if c not in metadata_fields]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# REMOVE COLUMNS (comments) which were moderated out\n",
    "statements_all_in = sorted(list(df_comments.loc[df_comments[\"moderated\"] > 0].index.array), key = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## for a row, count the number of finite values\n",
    "def count_finite(row):\n",
    "    finite = np.isfinite(row[val_fields]) # boolean array of whether each entry is finite\n",
    "    return sum(finite) # count number of True values in `finite`\n",
    "\n",
    "## REMOVE ROWS (participants) WITH LESS THAN N VOTES check for each row if the number of finite values >= cutoff\n",
    "def select_rows(df, threshold=7):\n",
    "    \n",
    "    number_of_votes = df.apply(count_finite, axis=1)\n",
    "    valid = number_of_votes >= threshold\n",
    "    \n",
    "    return df[valid]\n",
    "    \n",
    "df = select_rows(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "metadata = df[metadata_fields]\n",
    "vals = df[val_fields]\n",
    "# If the participant didn't see the statement, it's a null value, here we fill in the nulls with zeros\n",
    "vals = vals.fillna(0)\n",
    "vals = vals.sort_values(\"participant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 📓 Groups of comments and subconversations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               0    3    4    5    6    7    8    9   10   12  ...  161  162  \\\n",
       "participant                                                    ...             \n",
       "0            1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  ...  1.0  0.0   \n",
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       "2            1.0  1.0  1.0  0.0 -1.0  1.0  1.0  1.0  1.0  1.0  ...  0.0  0.0   \n",
       "4            1.0  1.0  1.0  1.0 -1.0  1.0  1.0  1.0 -1.0  0.0  ...  0.0  0.0   \n",
       "5            0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "             164  165  169  170  171  172  173  174  \n",
       "participant                                          \n",
       "0           -1.0  1.0 -1.0  1.0  1.0  1.0 -1.0 -1.0  \n",
       "1            1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2            0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4            0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
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       "\n",
       "[5 rows x 99 columns]"
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    ## main axis of disagreement\n",
    "\n",
    "comments_high_loadings_on_pcs = [\n",
    "    \"18\",\"19\",\"20\",\"29\",\n",
    "    \"30\",\"31\",\"32\",\"38\",\n",
    "    \"44\",\n",
    "]\n",
    "\n",
    "vals_high_loadings_on_pcs = vals[comments_high_loadings_on_pcs]\n",
    "\n",
    "    \n",
    "    ## a sub-cluster\n",
    "#     \"62\",\"53\",\"72\",\"153\",\"46\",\"39\",\"61\",\n",
    "#     \"65\",\"66\",\"68\",\"69\",\"137\",\"84\",\n",
    "#     \"87\",\"90\",\"92\",\"104\",\"106\",\"109\",\"119\",\"133\",\n",
    "    \n",
    "    \n",
    "    ## others\n",
    "#     \"37\", \"40\",\n",
    "#     \"3\",\"0\",\"8\",\"7\",\"6\",\"5\",\"4\",\"9\",\"10\",\n",
    "#     \"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\n",
    "#     \"71\", \"67\", \"63\", \n",
    "#     \"159\", \"156\", \"154\",  \"151\", \"77\", \"145\", \n",
    "#     \"143\",\"35\",\"21\",\"24\",\"34\",\n",
    "#     \"41\",\"43\",\"48\",\"50\",\"51\",\"55\",\"59\",\n",
    "#     \"64\",\"144\",\"78\",\"80\",\"150\",\n",
    "#     \"157\",\"94\",\"96\",\"160\",\"161\",\"162\",\n",
    "#     \"100\",\"164\",\"165\",\"169\",\"170\",\"171\",\n",
    "#     \"172\",\"111\",\"173\",\"174\",\"120\",\"121\",\"123\",\n",
    "#     \"122\",\"126\",\"128\",\"135\",\"139\",\"140\",\"141\"\n",
    "                           \n",
    "\n",
    "vals_all_in = vals[statements_all_in]\n",
    "\n",
    "vals_all_in.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🏟 Polis Helper Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@numba.njit()\n",
    "def sparsity_aware_dist(a, b):\n",
    "    n_both_seen = len(a) - (np.isnan(a) | np.isnan(b)).sum()\n",
    "    return (n_both_seen - (a == b).sum() + 1) / (n_both_seen + 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_pca(dataframe, components):\n",
    "    pca_object = PCA(n_components=components) ## pca is apparently different, it wants \n",
    "    pca_object = pca_object.fit(dataframe.T) ## .T transposes the matrix (flips it)\n",
    "    coords = pca_object.components_.T ## isolate the coordinates and flip \n",
    "    explained_variance = pca_object.explained_variance_ratio_\n",
    "\n",
    "    return coords, explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_umap(dataframe, neighbors):\n",
    "    reducer = umap.UMAP(\n",
    "        n_neighbors=neighbors,\n",
    "        metric=sparsity_aware_dist,\n",
    "        init='random',\n",
    "        min_dist=0.1,\n",
    "        spread=1.0,\n",
    "        local_connectivity=3.0,\n",
    "    )\n",
    "    embedding = reducer.fit_transform(dataframe.values)\n",
    "    # embedding.shape\n",
    "    \n",
    "    return embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def c(comment, coords):\n",
    "    fig, ax = plt.subplots(figsize=(7,5))\n",
    "    plt.sca(ax)\n",
    "    colorMap = {-1:'#A50026', 1:'#313695', 0:'#FEFEC050'}\n",
    "    ax.scatter(\n",
    "        x=coords[:,0],\n",
    "        y=coords[:,1],\n",
    "        c=vals[str(comment)].apply(lambda x: colorMap[x]),\n",
    "        s=10\n",
    "    )\n",
    "    ax.set_title(\"\\n\".join(wrap(str(comment) + \"  \" + str(df_comments['english-translation'][comment]))), fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Thanks to https://github.com/ciortanmadalina/high_noise_clustering/blob/master/graph-partitioning-louvain.ipynb\n",
    "\n",
    "def polis_leiden(dataframe, neighbors):\n",
    "    A = kneighbors_graph(\n",
    "        dataframe.values, \n",
    "        neighbors, \n",
    "        mode=\"connectivity\", \n",
    "        metric=sparsity_aware_dist, \n",
    "        p=3, \n",
    "        metric_params=None, \n",
    "        include_self=True, \n",
    "        n_jobs=None\n",
    "    )\n",
    "\n",
    "    sources, targets = A.nonzero()\n",
    "    weights = A[sources, targets]\n",
    "    if isinstance(weights, np.matrix): # ravel data\n",
    "            weights = weights.A1\n",
    "\n",
    "    g = ig.Graph(directed=False)\n",
    "    g.add_vertices(A.shape[0])  # each observation is a node\n",
    "    edges = list(zip(sources, targets))\n",
    "    g.add_edges(edges)\n",
    "    g.es['weight'] = weights\n",
    "    weights = np.array(g.es[\"weight\"]).astype(np.float64)\n",
    "\n",
    "    part = leidenalg.find_partition(\n",
    "        g, \n",
    "        leidenalg.ModularityVertexPartition\n",
    "    );\n",
    "\n",
    "    leidenClusters = np.array(part.membership)\n",
    "    leidenClustersStr = [str(i) for i in leidenClusters] \n",
    "\n",
    "    #df[\"leiden\"] = leidenClustersStr\n",
    "    \n",
    "    return leidenClusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_k_means_silhouettes(pca_coordinates):\n",
    "    \n",
    "    range_n_clusters = [2, 3, 4, 5, 6]\n",
    "\n",
    "    for n_clusters in range_n_clusters:\n",
    "        # Create a subplot with 1 row and 2 columns\n",
    "        fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "        fig.set_size_inches(18, 7)\n",
    "\n",
    "        # The 1st subplot is the silhouette plot\n",
    "        # The silhouette coefficient can range from -1, 1 but in this example all\n",
    "        # lie within [-0.1, 1]\n",
    "        ax1.set_xlim([-0.1, 1])\n",
    "        # The (n_clusters+1)*10 is for inserting blank space between silhouette\n",
    "        # plots of individual clusters, to demarcate them clearly.\n",
    "        ax1.set_ylim([0, len(pca_coordinates) + (n_clusters + 1) * 10])\n",
    "\n",
    "        # Initialize the clusterer with n_clusters value and a random generator\n",
    "        # seed of 10 for reproducibility.\n",
    "        clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
    "        cluster_labels = clusterer.fit_predict(pca_coordinates)\n",
    "\n",
    "        # The silhouette_score gives the average value for all the samples.\n",
    "        # This gives a perspective into the density and separation of the formed\n",
    "        # clusters\n",
    "        silhouette_avg = silhouette_score(pca_coordinates, cluster_labels)\n",
    "        print(\"For n_clusters =\", n_clusters,\n",
    "              \"The average silhouette_score is :\", silhouette_avg)\n",
    "\n",
    "        # Compute the silhouette scores for each sample\n",
    "        sample_silhouette_values = silhouette_samples(pca_coordinates, cluster_labels)\n",
    "\n",
    "        y_lower = 10\n",
    "        for i in range(n_clusters):\n",
    "            # Aggregate the silhouette scores for samples belonging to\n",
    "            # cluster i, and sort them\n",
    "            ith_cluster_silhouette_values = \\\n",
    "                sample_silhouette_values[cluster_labels == i]\n",
    "\n",
    "            ith_cluster_silhouette_values.sort()\n",
    "\n",
    "            size_cluster_i = ith_cluster_silhouette_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "\n",
    "            color = cm.nipy_spectral(float(i) / n_clusters)\n",
    "            ax1.fill_betweenx(np.arange(y_lower, y_upper),\n",
    "                              0, ith_cluster_silhouette_values,\n",
    "                              facecolor=color, edgecolor=color, alpha=0.7)\n",
    "\n",
    "            # Label the silhouette plots with their cluster numbers at the middle\n",
    "            ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "\n",
    "            # Compute the new y_lower for next plot\n",
    "            y_lower = y_upper + 10  # 10 for the 0 samples\n",
    "\n",
    "        ax1.set_title(\"The silhouette plot for the various clusters.\")\n",
    "        ax1.set_xlabel(\"The silhouette coefficient values\")\n",
    "        ax1.set_ylabel(\"Cluster label\")\n",
    "\n",
    "        # The vertical line for average silhouette score of all the values\n",
    "        ax1.axvline(x=silhouette_avg, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "        ax1.set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "\n",
    "        # 2nd Plot showing the actual clusters formed\n",
    "        colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)\n",
    "        ax2.scatter(pca_coordinates[:, 0], pca_coordinates[:, 1], marker='.', s=30, lw=0, alpha=0.7,\n",
    "                    c=colors, edgecolor='k')\n",
    "\n",
    "        # Labeling the clusters\n",
    "        centers = clusterer.cluster_centers_\n",
    "        # Draw white circles at cluster centers\n",
    "        ax2.scatter(centers[:, 0], centers[:, 1], marker='o',\n",
    "                    c=\"white\", alpha=1, s=200, edgecolor='k')\n",
    "\n",
    "        for i, c in enumerate(centers):\n",
    "            ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,\n",
    "                        s=50, edgecolor='k')\n",
    "\n",
    "        ax2.set_title(\"The visualization of the clustered data.\")\n",
    "        ax2.set_xlabel(\"Feature space for the 1st feature\")\n",
    "        ax2.set_ylabel(\"Feature space for the 2nd feature\")\n",
    "\n",
    "        plt.suptitle((\"Silhouette analysis for KMeans clustering on sample data \"\n",
    "                      \"with n_clusters = %d\" % n_clusters),\n",
    "                     fontsize=14, fontweight='bold')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_subconversation(dataframe, comments, neighbors=15):\n",
    "    coords, explained_variance = polis_pca(dataframe, 2)\n",
    "    print(\"Explained variance:\", explained_variance)\n",
    "\n",
    "    embedding = polis_umap(dataframe, neighbors)\n",
    "\n",
    "    leidenClusters = polis_leiden(dataframe, neighbors)\n",
    "    \n",
    "    polis_heatmap(dataframe, neighbors)\n",
    "    \n",
    "    polis_k_means_silhouettes(pca_coordinates=coords)\n",
    "    \n",
    "    # Show clusters given umap embedding \n",
    "    fig, ax = plt.subplots(figsize=(7,5))\n",
    "    plt.sca(ax)\n",
    "    ax.scatter(\n",
    "        x=embedding[:,0],\n",
    "        y=embedding[:,1],\n",
    "        c=leidenClusters,\n",
    "        cmap=\"tab20\",\n",
    "        s=5\n",
    "    )\n",
    "    ax.set_title(\"Leiden detected communities in UMAP space\", fontsize=14)\n",
    "    plt.show()\n",
    "\n",
    "    # Show clusters given pca embedding \n",
    "    fig, ax = plt.subplots(figsize=(7,5))\n",
    "    plt.sca(ax)\n",
    "    ax.scatter(\n",
    "        x=coords[:,0],\n",
    "        y=coords[:,1],\n",
    "        c=leidenClusters,\n",
    "        cmap=\"tab20\",\n",
    "        s=5\n",
    "    )\n",
    "\n",
    "    ax.set_title(\"Leiden detected communities in PCA space\", fontsize=14)\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Number of votes per participant, PCA space\")\n",
    "    # number of votes in pca space\n",
    "    plt.figure(figsize=(7, 5), dpi=80)\n",
    "    plt.scatter(\n",
    "        x=coords[:,0], \n",
    "        y=coords[:,1], \n",
    "        c=metadata['n-votes'],\n",
    "        cmap=\"magma_r\",\n",
    "        s=5\n",
    "    )\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"Participant ID number, indexed sequentially from first vote\")\n",
    "    # when did the participant show up? index\n",
    "    plt.figure(figsize=(7, 5), dpi=80)\n",
    "    plt.scatter(\n",
    "        x=coords[:,0], \n",
    "        y=coords[:,1], \n",
    "        c=metadata.index,\n",
    "        cmap=\"magma_r\",\n",
    "        s=5\n",
    "    )\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    \n",
    "    for x in comments:\n",
    "        c(x, coords)\n",
    "#         c(x, embedding)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_heatmap(__dataframe, neighbors=15):\n",
    "    leidenClusters = polis_leiden(__dataframe, neighbors)\n",
    "\n",
    "    # Show clustermap\n",
    "    __dataframe['leiden_cluster_assignments'] = leidenClusters\n",
    "    clusters_by_comments_means = __dataframe.groupby('leiden_cluster_assignments').agg('mean').T\n",
    "\n",
    "    index_to_label = df_comments['english-translation'].to_dict() # {index: label}\n",
    "\n",
    "    clustergrid = sns.clustermap(clusters_by_comments_means, cmap=\"RdBu\", figsize=(10,10))\n",
    "\n",
    "    ax = clustergrid.ax_heatmap\n",
    "    new_labels = [index_to_label[str(idx._text)] for idx in ax.get_yticklabels()] # [ label0, label1, label2, ...]\n",
    "    ax.set_yticklabels(new_labels, rotation=0, fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_pca_comments(__dataframe, comments):\n",
    "    \n",
    "    # PCA for COMMENTS on transposed matrix    \n",
    "    coords, explained_variance = polis_pca(dataframe.T, 2)\n",
    "    \n",
    "    plt.figure(figsize=(7, 5), dpi=80)\n",
    "    plt.scatter(\n",
    "        x=coords[:,0], \n",
    "        y=coords[:,1], \n",
    "        cmap=\"magma_r\",\n",
    "        s=5\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def polis_clustermap(__dataframe, __comments):\n",
    "    index_to_label = __comments['english-translation'].to_dict() # {index: label}\n",
    "\n",
    "    clustergrid = sns.clustermap(__dataframe, cmap=\"RdBu\", figsize=(30,30))\n",
    "\n",
    "    ax = clustergrid.ax_heatmap\n",
    "    new_labels = [index_to_label[str(idx._text)] for idx in ax.get_yticklabels()] # [ label0, label1, label2, ...]\n",
    "    ax.set_yticklabels(new_labels, rotation=0, fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Summary stats on participant behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global, will need to be scoped to do multiple times\n",
    "\n",
    "participant_vote_totals = []\n",
    "participant_agree_totals = []\n",
    "participant_disagree_totals = []\n",
    "participant_means = [] \n",
    "participant_variance = []\n",
    "\n",
    "def polis_create_summaries_participants(__dataframe):\n",
    "    \n",
    "    for participant, votes in __dataframe.iterrows(): \n",
    "        counts = votes.value_counts()\n",
    "        agree_count = counts.get(1.0, default = 0)\n",
    "        disagree_count = counts.get(-1.0, default = 0)\n",
    "\n",
    "        participant_agree_totals.append(agree_count)\n",
    "        participant_disagree_totals.append(disagree_count)\n",
    "        participant_vote_totals.append(agree_count + disagree_count)\n",
    "        participant_means.append(np.mean(votes))\n",
    "        participant_variance.append(np.var(votes))\n",
    "\n",
    "polis_create_summaries_participants(vals_all_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Summary stats on comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we take the mean in each column - ie., we average the votes to figure out whether it was net positive or net negative. This gives us a sense of whether the comments in the conversation were generally agreed on, disagreed on, or whether it was split. The median is not particularly interesting, because the data is -1 0 1 format, so the median will be one of the three. In this plot, minority interest groups will be overwhelmed, as this is not informed by clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "comment_total_votes = []\n",
    "comment_total_agrees = []\n",
    "comment_total_disagrees = []\n",
    "comment_means = []\n",
    "comment_variance = []\n",
    "\n",
    "def polis_create_summaries_comments(__dataframe):\n",
    "\n",
    "    columns = list(__dataframe)\n",
    "\n",
    "    for i in columns:\n",
    "        comment_means.append(np.mean(__dataframe[i]))\n",
    "        comment_variance.append(np.var(__dataframe[i]))\n",
    "        total_agrees = __dataframe[i].get(1.0, default = 0) ## halp, not like iterrows D: \n",
    "        total_disagrees = __dataframe[i].get(-1.0, default = 0)\n",
    "        comment_total_agrees.append(total_agrees)\n",
    "        comment_total_disagrees.append(total_disagrees)\n",
    "        comment_total_votes.append(total_agrees + total_disagrees)\n",
    "\n",
    "polis_create_summaries_comments(vals_all_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # 🪐 Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ⏳ Full participants * comments matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some things to notice about the matrix: comments are submitted over time, so participants who do not return will only have voted on the statements which were avialable when they arrived. \n",
    "\n",
    "Long horizontal lines sticking out into otherwise blank space indicate participants who returned to vote on comments, while others who started voting when there were a limited amount of comments did not.\n",
    "\n",
    "This indicates both the nature and a primary caveat of the method: since participants can add features (comments, columns):\n",
    "\n",
    "* Not all comments will be present when the conversation starts\n",
    "* Not all participants will have an opportunity to vote on all comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a15cdb128>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,14))\n",
    "sns.heatmap(vals_all_in, center=0, cmap=\"RdBu\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sparsity, total agreement, total disagreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions of matrix: (1238, 99)\n",
      "Total number of possible votes: 122562\n",
      "Total number of agrees: 30263\n",
      "Total number of disagrees: 11718\n",
      "Total without vote: 80581\n",
      "Percent sparse:  0.6574713206377181 %\n"
     ]
    }
   ],
   "source": [
    "melted = vals_all_in.melt();\n",
    "all_votes = melted.count();\n",
    "by_type = melted[\"value\"].value_counts();\n",
    "total_possible_votes = all_votes[\"value\"];\n",
    "total_agrees = by_type[1.0];\n",
    "total_disagrees = by_type[-1.0];\n",
    "total_without_vote = by_type[0.0];\n",
    "\n",
    "print(\"Dimensions of matrix:\", vals_all_in.shape)\n",
    "print(\"Total number of possible votes:\", total_possible_votes)\n",
    "print(\"Total number of agrees:\", total_agrees)\n",
    "print(\"Total number of disagrees:\", total_disagrees)\n",
    "print(\"Total without vote:\", total_without_vote)\n",
    "print(\"Percent sparse: \", total_without_vote / total_possible_votes,\"%\")\n",
    "\n",
    "\n",
    "## Make sure to check how many people and votes, relative to the total matrix, you are losing given min vote threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean total participant votes (agree or disagree, no pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33.91033925686591\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(participant_vote_totals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median total participant votes (agree or disagree, no pass):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.0\n"
     ]
    }
   ],
   "source": [
    "print(np.median(participant_vote_totals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of total votes by participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20f8d710>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(participant_vote_totals, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of total agrees by participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a15b87198>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(participant_agree_totals, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of total disagrees by participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a14c02438>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(participant_disagree_totals, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean of participant votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1101c0320>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(participant_means, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance of participant votes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A participant with 0 variance would have agreed, or disagreed, on 100% of the comments on which they voted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12f7d198>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(participant_variance, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12e83080>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=participant_variance, size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of total votes on each comment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a12fbf710>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(comment_total_votes, rug=True, bins=9, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average vote per comment (-1 ..... 0 ..... 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a15f12940>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(comment_means, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance per comment (higher variance --> more divisive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a15f4dcf8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(comment_variance, rug=True, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a160db588>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=comment_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.480375742833959, 0.4559003917413325, 0.45949091896095456, 0.4871875008155887, 0.5041568687836148, 0.30146074365606323, 0.39869924131108453, 0.4074351251823655, 0.467352496731149, 0.4951632603527026, 0.5211334138912986, 0.3806572172011213, 0.4695075960236139, 0.30455148097014434, 0.46797821281394303, 0.5215483830556735, 0.5137083367044032, 0.5139021194745804, 0.4614046053747653, 0.44839636601846294, 0.4893145440167532, 0.4555036916596478, 0.4392670444017012, 0.4972531129211979, 0.4784000720323905, 0.478653229321353, 0.41483671354860363, 0.47795900417841136, 0.37719979329836995, 0.2688745723077294, 0.3458911528052151, 0.4272962279564021, 0.4710532909142688, 0.37079517487427016, 0.3972037863978879, 0.43357557267048874, 0.35049757151692706, 0.346901172092157, 0.3726795002622943, 0.34261054752440284, 0.31329127964485465, 0.3257905945542389, 0.3411992608851118, 0.33033176654200436, 0.30381027818593553, 0.3140957717513044, 0.33281375192150725, 0.31021424414280685, 0.3544547853252256, 0.3528679849984779, 0.33393860544261306, 0.39312260381405906, 0.3815028147436786, 0.3159722675324463, 0.32918864393818426, 0.3015938469729363, 0.2872160788806824, 0.3278099806608745, 0.29572164181635907, 0.2736689015844474, 0.28942402801955563, 0.2643490595337245, 0.27312082910316876, 0.2840300813496146, 0.22910212678221745, 0.2538280252948529, 0.19066397676172298, 0.19555030391924152, 0.20379683736079254, 0.19414293208337882, 0.1962549685380265, 0.20641257852443, 0.1722487413906934, 0.16718820548020122, 0.16366488238625695, 0.1699337876245218, 0.1654291538021869, 0.15723416527256343, 0.15348704591542395, 0.15069122379365257, 0.1385944811711008, 0.12793120907399114, 0.10917864814007797, 0.11268370215131453, 0.08571918853954313, 0.1029939111757205, 0.10750572213769136, 0.07194364770944825, 0.07644893399902462, 0.0651919167138622, 0.04502284940273112, 0.08229699786773789, 0.07019307810554862, 0.0695791064330657, 0.05152925271622134, 0.07903205180067911, 0.070399257753268, 0.06208682512051052, 0.05571874486182041]\n"
     ]
    }
   ],
   "source": [
    "# something that shows {comment id: variance} needed here...\n",
    "# something that creates a custom set of comments based on variance... \n",
    "# something that creates a custom set of comments based on repful metrics\n",
    "# VARIANCE OVER TIME! \n",
    "print(comment_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>datetime</th>\n",
       "      <th>author-id</th>\n",
       "      <th>agrees</th>\n",
       "      <th>disagrees</th>\n",
       "      <th>moderated</th>\n",
       "      <th>comment-body</th>\n",
       "      <th>english-translation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comment-id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1435654477147</td>\n",
       "      <td>Tue Jun 30 01:54:37 PDT 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>我覺得載客的車子上應該要有明確標示。</td>\n",
       "      <td>I think the car's passenger should have clearl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1435654449781</td>\n",
       "      <td>Tue Jun 30 01:54:09 PDT 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>我覺得主動取締白牌車是交通部的責任。</td>\n",
       "      <td>I think the initiative to outlaw white car bra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1435654422024</td>\n",
       "      <td>Tue Jun 30 01:53:42 PDT 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>480</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>我覺得應該開放司機同時接受多家派遣。</td>\n",
       "      <td>I think we should open the driver while receiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1435654296727</td>\n",
       "      <td>Tue Jun 30 01:51:36 PDT 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>562</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>我覺得尖峰時段可以彈性提高收費。</td>\n",
       "      <td>I think the rush hour can increase the elastic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1435654192077</td>\n",
       "      <td>Tue Jun 30 01:49:52 PDT 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>508</td>\n",
       "      <td>191</td>\n",
       "      <td>1</td>\n",
       "      <td>我有用過 Uber 叫車。</td>\n",
       "      <td>I have used an Uber.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp                      datetime  author-id  agrees  \\\n",
       "comment-id                                                                   \n",
       "6           1435654477147  Tue Jun 30 01:54:37 PDT 2015          0     435   \n",
       "5           1435654449781  Tue Jun 30 01:54:09 PDT 2015          0     390   \n",
       "4           1435654422024  Tue Jun 30 01:53:42 PDT 2015          0     480   \n",
       "3           1435654296727  Tue Jun 30 01:51:36 PDT 2015          0     562   \n",
       "0           1435654192077  Tue Jun 30 01:49:52 PDT 2015          0     508   \n",
       "\n",
       "            disagrees  moderated        comment-body  \\\n",
       "comment-id                                             \n",
       "6                 238          1  我覺得載客的車子上應該要有明確標示。   \n",
       "5                 247          1  我覺得主動取締白牌車是交通部的責任。   \n",
       "4                 180          1  我覺得應該開放司機同時接受多家派遣。   \n",
       "3                 155          1    我覺得尖峰時段可以彈性提高收費。   \n",
       "0                 191          1       我有用過 Uber 叫車。   \n",
       "\n",
       "                                          english-translation  \n",
       "comment-id                                                     \n",
       "6           I think the car's passenger should have clearl...  \n",
       "5           I think the initiative to outlaw white car bra...  \n",
       "4           I think we should open the driver while receiv...  \n",
       "3           I think the rush hour can increase the elastic...  \n",
       "0                                        I have used an Uber.  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comments_integerIndex.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 📝 Comments by total  ✅ agree and 🚫 disagree votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "ax = sns.scatterplot(x=\"agrees\", y=\"disagrees\", data=df_comments)\n",
    "# ax.axes.set_title(\"Comments by total agree and disagree votes\",fontsize=24)\n",
    "ax.set_xlabel(\"Agrees\",fontsize=18)\n",
    "ax.set_ylabel(\"Disagrees\",fontsize=18)\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🌌 Dimensionality Reduction and 🦚 Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Are the first two principle components explained by how much people vote?\n",
    "\n",
    "In this chart, we take the PCA coordinates and color the participant locations by the number of total votes. Hopefully, it looks random. If it doesn't, we might imagine the following scenario:\n",
    "\n",
    "1. 1000 people vote, and there are very few controversial statements. They do not return.\n",
    "2. 1 person submits a statement which is incredibly controversial. \n",
    "3. 1000 more people vote, the space begins to take on structure, PCA is closely linked to vote count.\n",
    "\n",
    "We know this scenario - that voters don't see controversial comments - happens. Polis mitigates in two ways:\n",
    "* polis eliminates participants who don't vote at least 7 times from the analysis\n",
    "* polis shows several highly controversial comments (large egeinvalue) in the first 10 comments participants see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1a20ea06a0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 560x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coords, embedding = polis_pca(vals_all_in, 2)\n",
    "\n",
    "plt.figure(figsize=(7, 5), dpi=80)\n",
    "plt.scatter(\n",
    "    x=coords[:,0], \n",
    "    y=coords[:,1], \n",
    "    c=metadata['n-votes'],\n",
    "    cmap=\"magma_r\",\n",
    "    s=5\n",
    ")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-means"
   ]
  },
  {
   "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": [
    "# Consensus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustermap All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# index_to_label = df_comments['english-translation'].to_dict() # {index: label}\n",
    "\n",
    "# clustergrid = sns.clustermap(vals_all_in, cmap=\"RdBu\", figsize=(30,30))\n",
    "\n",
    "# ax = clustergrid.ax_heatmap\n",
    "# new_labels = [index_to_label[str(idx._text)] for idx in ax.get_xticklabels()] # [ label0, label1, label2, ...]\n",
    "# ax.set_xticklabels(new_labels, rotation=90, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coords, explained_variance = polis_pca(vals_all_in, 2)\n",
    "# print(\"Explained variance:\", explained_variance)\n",
    "\n",
    "# embedding = polis_umap(vals_all_in, 4)\n",
    "\n",
    "# leidenClusters = polis_leiden(vals_all_in, 8)\n",
    "\n",
    "# # Show clusters given umap embedding \n",
    "# fig, ax = plt.subplots(figsize=(7,5))\n",
    "# plt.sca(ax)\n",
    "# ax.scatter(\n",
    "#     x=embedding[:,0],\n",
    "#     y=embedding[:,1],\n",
    "#     c=leidenClusters,\n",
    "#     cmap=\"tab20\",\n",
    "#     s=5\n",
    "# )\n",
    "\n",
    "# ax.set_title(\"Leiden detected communities in UMAP space\", fontsize=14)\n",
    "# plt.show()\n",
    "\n",
    "# for x in statements_all_in:\n",
    "#     if int(x) < 5:\n",
    "#         c(x, coords)\n",
    "#         c(x, embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/colinmegill/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "leidenClusters = polis_leiden(vals_all_in, 15)\n",
    "\n",
    "# Show clustermap\n",
    "vals_all_in['leiden_cluster_assignments'] = leidenClusters\n",
    "clusters_by_comments_means = vals_all_in.groupby('leiden_cluster_assignments').agg('mean').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1, 0, 'I think we should review staff. Passenger protection. Driving interests must take into account. The most important thing is safety first'),\n",
       " Text(1, 0, 'I think the driver should ensure passenger accident insurance.'),\n",
       " Text(1, 0, 'I think when UberX own passenger car, the car should be insured passenger liability insurance.'),\n",
       " Text(1, 0, 'I think, Uber is a response to market demand, the current should identify the problems, to face and solve problems.'),\n",
       " Text(1, 0, 'I think both of qualification, can not guarantee the quality of service of taxi driver'),\n",
       " Text(1, 0, 'Should be modified so that the passenger car from the insurance can also protect the interests of passengers or the insurance of UBER'),\n",
       " Text(1, 0, 'I feel that since the high north, other townships in Taiwan almost all white taxi car brand is always a price hike when riding, as legislation fully into the specification.'),\n",
       " Text(1, 0, 'I think the only shall fix a law to protect the passengers, the driver of the other passers-by with the minimum standards, the other should be left to the market mechanism to decide.'),\n",
       " Text(1, 0, 'I think I take the uber because drivers are generally better quality, unlike the taxi driver told him that I will go to several different parts of the stink face will greet people, even if there are regulations to them, the quality is still poor! Want to maintain the status quo ⋯ uber'),\n",
       " Text(1, 0, 'I think part of the vehicle because it is his own love of the car, but also will not violate Jiaotongguize compliance, passengers will not be as scary as general taxi rampage'),\n",
       " Text(1, 0, 'I think that every guest can travel with GPS positioning record, if things happen UBER company can assist police investigators access to travel records'),\n",
       " Text(1, 0, \"I think open Uber is to enhance the competitiveness of Taiwan's base important part\"),\n",
       " Text(1, 0, \"I think the question should be whether UBER legislation (or amending the law)? UBER service should depend on whether there are enough big influence on the present situation, affecting people's daily not great. If elected officials feel the influence of big enough, I am in favor of amending the law or legislation.\"),\n",
       " Text(1, 0, 'I think the taxi was ㄧ risky thing to do, because the vehicle quality is uneven, uneven quality drivers.'),\n",
       " Text(1, 0, 'The Uber legitimate, allowing drivers and passengers can receive better protection, can only hope that the Taiwan government proposed rule'),\n",
       " Text(1, 0, 'White counties outside car license price hike, when heard, like über if there is an open and transparent rates, and will not mess detour, will attract more passengers to take'),\n",
       " Text(1, 0, 'I think it should be for personal use passenger car registration, more than twice the daily commute is limited up to carpool effect, and should be added to protect the passengers to buy insurance'),\n",
       " Text(1, 0, 'Dealers Tube drivers used to be, now is the team Tube drivers. So the brand team we are more confident to take, because there are in management. Dealers now unlikely tube driver, so the driver will not join the team more likely to mess messing around. uber small problem now is not the driver, the driver until the uber much, probably just as many now nobody drivers the same. And uber only APP Tube drivers? You think this is really you?'),\n",
       " Text(1, 0, 'On the management side, unless uber admit that he is called a virtual car company, or management is weak.'),\n",
       " Text(1, 0, 'I think Uber since flaunt it should carpool to carpool, not for a bunch are professional drivers, and made do with small yellow business is not subject to the same regulatory controls driving career.'),\n",
       " Text(1, 0, \"Hong Kong banned Uber not just for the driver to open a penalty, also arrested the head of the company, Taiwan banned Uber penalty fines will only fills your iron hand alright? Random vehicle personnel can carry passengers, but sacrificed passengers (people's) safety.\"),\n",
       " Text(1, 0, 'I think the real spirit carpool, you should ask the driver can choose to take the passengers what areas together, carpool or just white brand car business profitable packaging Bale.'),\n",
       " Text(1, 0, 'I think the government set up a carpool with UBER network are matchmaking platform has nothing to do with the Ministry of Transportation'),\n",
       " Text(1, 0, 'I think UBER tax avoidance normal, just as you would choose methods of reporting the lowest amount in your income tax as normal.'),\n",
       " Text(1, 0, \"I think that Uber should pay the taxes paid to the government, without too much government intervention, let Uber free competition with small yellow; consumers have the right to choose, if they feel unsafe or Uber no job security, and they can go take a small yellow, and If this is the general public's idea that Uber will naturally be out of the market mechanism, why should the government interfere too much of it?\"),\n",
       " Text(1, 0, 'I think the management of occupational regulations buses share the economic unfriendly. It should be as soon as possible to amend the law, more environmentally friendly.'),\n",
       " Text(1, 0, 'I agree with uber legalization, allow consumers a choice, let the taxi industry more of a healthy competition of power, leaving many part-time pipeline open.'),\n",
       " Text(1, 0, 'Huang Li personally think that should be subjected to a small norm, would like to open uber drivers are required to pass the driving exam or certification by the Ministry of Transportation organized supervision unit, the test is too personal or business can be issued with a registration certificate, the one can protect all the passengers, On the other hand the basic rights of all drivers can be guaranteed.'),\n",
       " Text(1, 0, 'I think UberX on billing, night should be able to increase the cost, not only for passengers at a low price, allows the driver unable to continue, resulting in reduced number of vehicles.'),\n",
       " Text(1, 0, 'Regardless of the legal aspects under the premise, uber matchmaking benefits of better, from the taxi benefit is better'),\n",
       " Text(1, 0, 'I feel good scoring mechanism Uber, and effective management, will eliminate low-scoring drivers, so generally if the driver committed a serious error, will be permanently prohibit cooperation, on the contrary taxi mistake can still continue on the road, so the consumer Uber who is more secure platform.'),\n",
       " Text(1, 0, \"On driving, the taxi drivers 'income is better than uber, uber fare taxi drivers' income is Liu Cheng.\"),\n",
       " Text(1, 0, 'Should not limit the color of the taxi vehicle interior styling and so on, no matter how good the car painted in yellow hard to see, Tu Lite set manufacturers and cause an increase in purchase costs, consumers lose freshness'),\n",
       " Text(1, 0, \"On the security side, compared with the professional driver's license for personal use, written more than a place with attractions, road test more and more narrow lane S, Jiaotonganquan rules are the same.\"),\n",
       " Text(1, 0, 'I think the biggest difference UBER taxi fleet and scoring system are: UBER scoring system is transparent and open implementation, scores below 4.5 driver will be shut down, and thus ensure the quality of the vehicle. Instead, the taxi fleet scoring system is completely black box, input mode is not conducive to passengers, nor the identification of the scoring system at all. Even after the press, the passengers can not know whether the team had received scores.'),\n",
       " Text(1, 0, 'I have flown with Uber to increase billing'),\n",
       " Text(1, 0, 'I have been called Uber in a place outside of Taiwan'),\n",
       " Text(1, 0, 'I have a trade license.'),\n",
       " Text(1, 0, 'I think the Government should set fair rules for transport of control, rather than protecting particular vested interests.'),\n",
       " Text(1, 0, 'I think, called the car network tools (eg app), you can reduce the chances of a driver deliberately detour.'),\n",
       " Text(1, 0, 'I think that the taxi service quality is uneven, with the market does not provide enough of a fair competition environment.'),\n",
       " Text(1, 0, 'I have used an Uber.'),\n",
       " Text(1, 0, 'I think the rush hour can increase the elasticity charges.'),\n",
       " Text(1, 0, 'I think it is similar to carpool, but the concept of charging motorists to carpool who is feasible.'),\n",
       " Text(1, 0, 'I think, shared economy can reduce the waste of social resources.'),\n",
       " Text(1, 0, 'I feel good UberX average quality than a taxi'),\n",
       " Text(1, 0, 'I think it was UBER passengers and the driver both sides can benefit from the new service.'),\n",
       " Text(1, 0, 'I think, UBER is a resilient business model can create jobs.'),\n",
       " Text(1, 0, 'Uber is a matchmaking platform, like the auction site, belonged to the IT industry.'),\n",
       " Text(1, 0, 'If you do not hurry, even if there are many taxis in the street, I would tend to call Uber'),\n",
       " Text(1, 0, \"I think that even Uber X does not have occupational driver's license or vehicle clearly marked to identify, but because every trip travel itinerary Jie record, but also feel safe\\n.\"),\n",
       " Text(1, 0, 'I think that the traditional taxi now have to join the team only way to survive, this is not required by the Government, UberX subvert the provisions of this unwritten rule, feel great!'),\n",
       " Text(1, 0, 'I think UberX usually cheaper than a taxi on billing, can save an average lunch money.'),\n",
       " Text(1, 0, 'I think when the Uber app customer complaints, problem solved is more efficient than a taxi complaints.'),\n",
       " Text(1, 0, 'I feel like Uber drivers are less likely Luanzuan taxi driver while driving, when the guests have to get off the taxi directly behind the switch lanes to disregard the safety car, but the driver will switch Uber slowly to the curb lane.'),\n",
       " Text(1, 0, \"I think UBER is a shared platform concept, not the concept of employees. UBER platform is based on a manager's point of view to monitor on a platform of cooperation drivers and passengers\\n\"),\n",
       " Text(1, 0, 'I think the mechanism UberX existing safeguards the interests of the passengers enough, so that people can release operation more than a convenient mode of transportation.'),\n",
       " Text(1, 0, 'I think UberX is international App, today Taiwan to integrate into the international, we must accept this platform, rather than crowding it.'),\n",
       " Text(1, 0, 'I think the taxi prices have been subsidized by the government, now the Society still decided to hike fares, should be open UberX competition, let the market determine demand.'),\n",
       " Text(1, 0, 'I think we should open the driver while receiving a number of dispatch.'),\n",
       " Text(1, 0, 'Uber is manpower dispatch, as passenger employees belong to the service sector.'),\n",
       " Text(1, 0, 'Uber have the opportunity to take advanced cars (Ex.Audi.BMW.Benz ..... etc.), Taxi mostly for domestic cars, have different freshness.'),\n",
       " Text(1, 0, 'I think now is a technological age, many things should be standardized and laws should be Expelling, rather than rigid.'),\n",
       " Text(1, 0, 'I think that all commercial vehicles should always take passengers scoring mechanism, rather than rely on government-issued business license.'),\n",
       " Text(1, 0, 'I think the company since Uber charge income renewals, have a responsibility to provide passenger insurance, may develop additional types of insurance needed.'),\n",
       " Text(1, 0, 'I feel that although the addition billing will make people more do not want to take the Uber, in accordance with the law of supply and demand in addition to the accounting change is reasonable.'),\n",
       " Text(1, 0, 'I think UberX asked to find the exit, laid in respect unlike occupational minibus and taxi regulations for non-commercial vehicles, carpool or just to subsidize oil money from part-time nature of the car, there is a compliance with rules of the game'),\n",
       " Text(1, 0, 'I think UberX flagrante delicto make every effort should be closed down, people do not need to express an opinion.'),\n",
       " Text(1, 0, 'I think the taxi body must be painted in yellow, and other vehicles of different colors.'),\n",
       " Text(1, 0, 'Although I think UberX is to serve the public, but if you add the cost of legal taxes, insurance, its business model can not continue operating.'),\n",
       " Text(1, 0, 'I think the popularity of public transportation, car sales rising rate of empty, open passenger car for personal use will not increase demand, will only make it harder taxi business.'),\n",
       " Text(1, 0, 'I think the Ministry of Transportation to ban ineffective, the performance of public authority is incompetent.'),\n",
       " Text(1, 0, 'I think UberX currently not operating according to the law, so I think there are risks when traveling.'),\n",
       " Text(1, 0, 'I think UberX management qualifications for drivers is not stringent enough.'),\n",
       " Text(1, 0, 'I think UberX management system is not transparent, so I can not feel at ease.'),\n",
       " Text(1, 0, 'I feel that since the Ministry of Transportation has rejected Uber administrative appeal, the Taipei City Government should log out \"Taiwan Yu Bo Digital\" company registration.'),\n",
       " Text(1, 0, 'I think UberX has produced situations of unfair competition with existing domestic transportation.'),\n",
       " Text(1, 0, 'I think the car from the passenger on their own without government certification business, has threatened to Gonggonganquan.'),\n",
       " Text(1, 0, 'I think the profile UberX drivers, should be subject to government regulation.'),\n",
       " Text(1, 0, 'I think the initiative to outlaw white car brand is the responsibility of the Ministry of Transportation.'),\n",
       " Text(1, 0, 'I think UberX not yet possible to help protect passenger accident insurance, makes me feel insecure.'),\n",
       " Text(1, 0, 'I think UberX should make application for business transportation business law.'),\n",
       " Text(1, 0, \"I think the car's passenger should have clearly marked.\"),\n",
       " Text(1, 0, \"I think UberX drivers should first obtain professional driver's license.\"),\n",
       " Text(1, 0, 'I think before for comments relevant government agencies at all levels should first clear stand.'),\n",
       " Text(1, 0, 'Ministry of Transportation should publicly report on the 2014 survey Uber is illegal in line.'),\n",
       " Text(1, 0, 'I have a small car driving license.'),\n",
       " Text(1, 0, 'I think Uber place of business should pay taxes to the government.'),\n",
       " Text(1, 0, 'I think the platform to solve the dispute Uber record should be reported to the Ministry of Transportation.'),\n",
       " Text(1, 0, 'I think the Government should be able to face the challenges by Uber while improving supervision and evaluation system of the taxi, let the taxi drivers and passengers can get the same quality of service as Uber.'),\n",
       " Text(1, 0, 'I think any innovative services to achieve the purpose of profit is indeed an important process of social progress, but it must be completely legitimate business under the norms of the law, in order to avoid the illegal operation to produce social justice and social security.'),\n",
       " Text(1, 0, 'Uber is an offshore company, I think Uebr responsibility to raise enough to convince Taiwan in response to the way social problems in Taiwan for the tax.'),\n",
       " Text(1, 0, 'I think a similar transportation and food, medicine nature than matchmaking services platform in general, it should be strictly defined and special checks.'),\n",
       " Text(1, 0, 'I think UberX taxi mutatis mutandis, the registration certificate, license, driving complete information furnished in the car visible place.'),\n",
       " Text(1, 0, 'I think the car ride from others, if not judge whether the person through the test and health check, feeling very insecure.'),\n",
       " Text(1, 0, 'I think it is the obligation of a tax business operations in Taiwan, Uber, or create a new model then it excellent, resulting in Taiwan should try to be legitimate tax liability.'),\n",
       " Text(1, 0, 'I think Uber \"bonus rates\" logic is very opaque, resulting in sometimes UberBlack will be cheaper than UberX strange situation occurs.'),\n",
       " Text(1, 0, 'I think we need to consider in advance autopilot computer case, including whether to build a modern public system also necessary, as well as future employment taxi drivers how to do.'),\n",
       " Text(1, 0, 'I think \"driving his intended destination, and take the same people\" is the carpool spirit, bypass waiting passengers can not be regarded carpool.')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {