From d1ff495912d423821fe8a42b5e23451e42763390 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Sat, 20 Feb 2021 12:02:05 -0800
Subject: [PATCH 01/16] example code from sklearn

---
 .../plot_document_clustering.ipynb            | 285 ++++++++++
 tour_model_eval/plot_kmeans_digits.ipynb      | 531 ++++++++++++++++++
 2 files changed, 816 insertions(+)
 create mode 100644 tour_model_eval/plot_document_clustering.ipynb
 create mode 100644 tour_model_eval/plot_kmeans_digits.ipynb

diff --git a/tour_model_eval/plot_document_clustering.ipynb b/tour_model_eval/plot_document_clustering.ipynb
new file mode 100644
index 0000000..828ce5a
--- /dev/null
+++ b/tour_model_eval/plot_document_clustering.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Clustering text documents using k-means\n",
+    "\n",
+    "This is an example showing how the scikit-learn can be used to cluster\n",
+    "documents by topics using a bag-of-words approach. This example uses\n",
+    "a scipy.sparse matrix to store the features instead of standard numpy arrays.\n",
+    "\n",
+    "Two feature extraction methods can be used in this example:\n",
+    "\n",
+    "  - TfidfVectorizer uses a in-memory vocabulary (a python dict) to map the most\n",
+    "    frequent words to features indices and hence compute a word occurrence\n",
+    "    frequency (sparse) matrix. The word frequencies are then reweighted using\n",
+    "    the Inverse Document Frequency (IDF) vector collected feature-wise over\n",
+    "    the corpus.\n",
+    "\n",
+    "  - HashingVectorizer hashes word occurrences to a fixed dimensional space,\n",
+    "    possibly with collisions. The word count vectors are then normalized to\n",
+    "    each have l2-norm equal to one (projected to the euclidean unit-ball) which\n",
+    "    seems to be important for k-means to work in high dimensional space.\n",
+    "\n",
+    "    HashingVectorizer does not provide IDF weighting as this is a stateless\n",
+    "    model (the fit method does nothing). When IDF weighting is needed it can\n",
+    "    be added by pipelining its output to a TfidfTransformer instance.\n",
+    "\n",
+    "Two algorithms are demoed: ordinary k-means and its more scalable cousin\n",
+    "minibatch k-means.\n",
+    "\n",
+    "Additionally, latent semantic analysis can also be used to reduce\n",
+    "dimensionality and discover latent patterns in the data.\n",
+    "\n",
+    "It can be noted that k-means (and minibatch k-means) are very sensitive to\n",
+    "feature scaling and that in this case the IDF weighting helps improve the\n",
+    "quality of the clustering by quite a lot as measured against the \"ground truth\"\n",
+    "provided by the class label assignments of the 20 newsgroups dataset.\n",
+    "\n",
+    "This improvement is not visible in the Silhouette Coefficient which is small\n",
+    "for both as this measure seem to suffer from the phenomenon called\n",
+    "\"Concentration of Measure\" or \"Curse of Dimensionality\" for high dimensional\n",
+    "datasets such as text data. Other measures such as V-measure and Adjusted Rand\n",
+    "Index are information theoretic based evaluation scores: as they are only based\n",
+    "on cluster assignments rather than distances, hence not affected by the curse\n",
+    "of dimensionality.\n",
+    "\n",
+    "Note: as k-means is optimizing a non-convex objective function, it will likely\n",
+    "end up in a local optimum. Several runs with independent random init might be\n",
+    "necessary to get a good convergence.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n",
+    "#         Lars Buitinck\n",
+    "# License: BSD 3 clause\n",
+    "from sklearn.datasets import fetch_20newsgroups\n",
+    "from sklearn.decomposition import TruncatedSVD\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.feature_extraction.text import HashingVectorizer\n",
+    "from sklearn.feature_extraction.text import TfidfTransformer\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import Normalizer\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
+    "\n",
+    "import logging\n",
+    "from optparse import OptionParser\n",
+    "import sys\n",
+    "from time import time\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "# Display progress logs on stdout\n",
+    "logging.basicConfig(level=logging.INFO,\n",
+    "                    format='%(asctime)s %(levelname)s %(message)s')\n",
+    "\n",
+    "# parse commandline arguments\n",
+    "op = OptionParser()\n",
+    "op.add_option(\"--lsa\",\n",
+    "              dest=\"n_components\", type=\"int\",\n",
+    "              help=\"Preprocess documents with latent semantic analysis.\")\n",
+    "op.add_option(\"--no-minibatch\",\n",
+    "              action=\"store_false\", dest=\"minibatch\", default=True,\n",
+    "              help=\"Use ordinary k-means algorithm (in batch mode).\")\n",
+    "op.add_option(\"--no-idf\",\n",
+    "              action=\"store_false\", dest=\"use_idf\", default=True,\n",
+    "              help=\"Disable Inverse Document Frequency feature weighting.\")\n",
+    "op.add_option(\"--use-hashing\",\n",
+    "              action=\"store_true\", default=False,\n",
+    "              help=\"Use a hashing feature vectorizer\")\n",
+    "op.add_option(\"--n-features\", type=int, default=10000,\n",
+    "              help=\"Maximum number of features (dimensions)\"\n",
+    "                   \" to extract from text.\")\n",
+    "op.add_option(\"--verbose\",\n",
+    "              action=\"store_true\", dest=\"verbose\", default=False,\n",
+    "              help=\"Print progress reports inside k-means algorithm.\")\n",
+    "\n",
+    "print(__doc__)\n",
+    "op.print_help()\n",
+    "\n",
+    "\n",
+    "def is_interactive():\n",
+    "    return not hasattr(sys.modules['__main__'], '__file__')\n",
+    "\n",
+    "\n",
+    "# work-around for Jupyter notebook and IPython console\n",
+    "argv = [] if is_interactive() else sys.argv[1:]\n",
+    "(opts, args) = op.parse_args(argv)\n",
+    "if len(args) > 0:\n",
+    "    op.error(\"this script takes no arguments.\")\n",
+    "    sys.exit(1)\n",
+    "\n",
+    "\n",
+    "# #############################################################################\n",
+    "# Load some categories from the training set\n",
+    "categories = [\n",
+    "    'alt.atheism',\n",
+    "    'talk.religion.misc',\n",
+    "    'comp.graphics',\n",
+    "    'sci.space',\n",
+    "]\n",
+    "# Uncomment the following to do the analysis on all the categories\n",
+    "# categories = None\n",
+    "\n",
+    "print(\"Loading 20 newsgroups dataset for categories:\")\n",
+    "print(categories)\n",
+    "\n",
+    "dataset = fetch_20newsgroups(subset='all', categories=categories,\n",
+    "                             shuffle=True, random_state=42)\n",
+    "\n",
+    "print(\"%d documents\" % len(dataset.data))\n",
+    "print(\"%d categories\" % len(dataset.target_names))\n",
+    "print()\n",
+    "\n",
+    "labels = dataset.target\n",
+    "true_k = np.unique(labels).shape[0]\n",
+    "\n",
+    "print(\"Extracting features from the training dataset \"\n",
+    "      \"using a sparse vectorizer\")\n",
+    "t0 = time()\n",
+    "if opts.use_hashing:\n",
+    "    if opts.use_idf:\n",
+    "        # Perform an IDF normalization on the output of HashingVectorizer\n",
+    "        hasher = HashingVectorizer(n_features=opts.n_features,\n",
+    "                                   stop_words='english', alternate_sign=False,\n",
+    "                                   norm=None)\n",
+    "        vectorizer = make_pipeline(hasher, TfidfTransformer())\n",
+    "    else:\n",
+    "        vectorizer = HashingVectorizer(n_features=opts.n_features,\n",
+    "                                       stop_words='english',\n",
+    "                                       alternate_sign=False, norm='l2')\n",
+    "else:\n",
+    "    vectorizer = TfidfVectorizer(max_df=0.5, max_features=opts.n_features,\n",
+    "                                 min_df=2, stop_words='english',\n",
+    "                                 use_idf=opts.use_idf)\n",
+    "X = vectorizer.fit_transform(dataset.data)\n",
+    "\n",
+    "print(\"done in %fs\" % (time() - t0))\n",
+    "print(\"n_samples: %d, n_features: %d\" % X.shape)\n",
+    "print()\n",
+    "\n",
+    "if opts.n_components:\n",
+    "    print(\"Performing dimensionality reduction using LSA\")\n",
+    "    t0 = time()\n",
+    "    # Vectorizer results are normalized, which makes KMeans behave as\n",
+    "    # spherical k-means for better results. Since LSA/SVD results are\n",
+    "    # not normalized, we have to redo the normalization.\n",
+    "    svd = TruncatedSVD(opts.n_components)\n",
+    "    normalizer = Normalizer(copy=False)\n",
+    "    lsa = make_pipeline(svd, normalizer)\n",
+    "\n",
+    "    X = lsa.fit_transform(X)\n",
+    "\n",
+    "    print(\"done in %fs\" % (time() - t0))\n",
+    "\n",
+    "    explained_variance = svd.explained_variance_ratio_.sum()\n",
+    "    print(\"Explained variance of the SVD step: {}%\".format(\n",
+    "        int(explained_variance * 100)))\n",
+    "\n",
+    "    print()\n",
+    "\n",
+    "\n",
+    "# #############################################################################\n",
+    "# Do the actual clustering\n",
+    "\n",
+    "if opts.minibatch:\n",
+    "    km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1,\n",
+    "                         init_size=1000, batch_size=1000, verbose=opts.verbose)\n",
+    "else:\n",
+    "    km = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1,\n",
+    "                verbose=opts.verbose)\n",
+    "\n",
+    "print(\"Clustering sparse data with %s\" % km)\n",
+    "t0 = time()\n",
+    "km.fit(X)\n",
+    "print(\"done in %0.3fs\" % (time() - t0))\n",
+    "print()\n",
+    "\n",
+    "# Test what labels are\n",
+    "np.set_printoptions(threshold=np.inf)\n",
+    "print(\"----------Test---------------\")\n",
+    "print('labels is %s' % labels)\n",
+    "print('The type of labels is %s' % type(labels))\n",
+    "print('the shape of labels is n_samples %s' % labels.shape)\n",
+    "print('km.labels_ is %s' % km.labels_)\n",
+    "print('The type of km.labels_ is %s' % type(km.labels_))\n",
+    "print('the shape of km.labels_ is n_samples %s' % km.labels_.shape)\n",
+    "print('true_k is %s'% true_k)\n",
+    "print('np.unique(labels) is %s' % np.unique(labels))\n",
+    "print('np.unique(labels).shape is %s' % np.unique(labels).shape)\n",
+    "print(\"----------Test---------------\")\n",
+    "print()\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
+    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
+    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
+    "print(\"Adjusted Rand-Index: %.3f\"\n",
+    "      % metrics.adjusted_rand_score(labels, km.labels_))\n",
+    "print(\"Silhouette Coefficient: %0.3f\"\n",
+    "      % metrics.silhouette_score(X, km.labels_, sample_size=1000))\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "\n",
+    "if not opts.use_hashing:\n",
+    "    print(\"Top terms per cluster:\")\n",
+    "\n",
+    "    if opts.n_components:\n",
+    "        original_space_centroids = svd.inverse_transform(km.cluster_centers_)\n",
+    "        order_centroids = original_space_centroids.argsort()[:, ::-1]\n",
+    "    else:\n",
+    "        order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
+    "\n",
+    "    terms = vectorizer.get_feature_names()\n",
+    "    for i in range(true_k):\n",
+    "        print(\"Cluster %d:\" % i, end='')\n",
+    "        for ind in order_centroids[i, :10]:\n",
+    "            print(' %s' % terms[ind], end='')\n",
+    "        print()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tour_model_eval/plot_kmeans_digits.ipynb b/tour_model_eval/plot_kmeans_digits.ipynb
new file mode 100644
index 0000000..62f7cd6
--- /dev/null
+++ b/tour_model_eval/plot_kmeans_digits.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# A demo of K-Means clustering on the handwritten digits data\n",
+    "\n",
+    "In this example we compare the various initialization strategies for K-means in\n",
+    "terms of runtime and quality of the results.\n",
+    "\n",
+    "As the ground truth is known here, we also apply different cluster quality\n",
+    "metrics to judge the goodness of fit of the cluster labels to the ground truth.\n",
+    "\n",
+    "Cluster quality metrics evaluated (see `clustering_evaluation` for\n",
+    "definitions and discussions of the metrics):\n",
+    "\n",
+    "=========== ========================================================\n",
+    "Shorthand    full name\n",
+    "=========== ========================================================\n",
+    "homo         homogeneity score\n",
+    "compl        completeness score\n",
+    "v-meas       V measure\n",
+    "ARI          adjusted Rand index\n",
+    "AMI          adjusted mutual information\n",
+    "silhouette   silhouette coefficient\n",
+    "=========== ========================================================\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Automatically created module for IPython interactive environment\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(__doc__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the dataset\n",
+    "\n",
+    "We will start by loading the `digits` dataset. This dataset contains\n",
+    "handwritten digits from 0 to 9. In the context of clustering, one would like\n",
+    "to group images such that the handwritten digits on the image are the same.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# digits: 10; # samples: 1797; # features 64\n",
+      "------Test------\n",
+      "data.shape is  (1797, 64)\n",
+      "labels is [0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0\n",
+      " 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9\n",
+      " 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4\n",
+      " 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7\n",
+      " 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2\n",
+      " 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 3 1 3 9 1\n",
+      " 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 5 4 8 8 4 9 0 8 9 8 0 1 2\n",
+      " 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9\n",
+      " 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8\n",
+      " 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2\n",
+      " 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0\n",
+      " 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2\n",
+      " 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7\n",
+      " 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1\n",
+      " 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8\n",
+      " 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2\n",
+      " 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7\n",
+      " 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n",
+      " 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1\n",
+      " 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1\n",
+      " 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0\n",
+      " 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9\n",
+      " 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5\n",
+      " 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4\n",
+      " 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n",
+      " 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 2 7 8 2 0 1 2 6 3\n",
+      " 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 4 6 3 1 3 9 1 7 6 8 4\n",
+      " 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 4 9 0 8 9 8 0 1 2 3 4 5 6\n",
+      " 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7\n",
+      " 7 3 5 1 0 0 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6\n",
+      " 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9\n",
+      " 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6\n",
+      " 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3\n",
+      " 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4\n",
+      " 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7\n",
+      " 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7\n",
+      " 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7\n",
+      " 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7\n",
+      " 9 5 4 8 8 4 9 0 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7\n",
+      " 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4\n",
+      " 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0\n",
+      " 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8\n",
+      " 9 0 1 2 3 4 5 6 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1\n",
+      " 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 0 1 7 6 3 2 1 7\n",
+      " 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 2 5 7 9 5 4 4 9 0 8\n",
+      " 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6\n",
+      " 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5\n",
+      " 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7\n",
+      " 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8]\n",
+      "------Test------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "data, labels = load_digits(return_X_y=True)\n",
+    "(n_samples, n_features), n_digits = data.shape, np.unique(labels).size\n",
+    "\n",
+    "print(\n",
+    "    f\"# digits: {n_digits}; # samples: {n_samples}; # features {n_features}\"\n",
+    ")\n",
+    "\n",
+    "print('------Test------')\n",
+    "np.set_printoptions(threshold=np.inf)\n",
+    "print('data.shape is ',data.shape)\n",
+    "print(\"labels is %s\" % labels)\n",
+    "print('------Test------')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define our evaluation benchmark\n",
+    "\n",
+    "We will first our evaluation benchmark. During this benchmark, we intend to\n",
+    "compare different initialization methods for KMeans. Our benchmark will:\n",
+    "\n",
+    "* create a pipeline which will scale the data using a\n",
+    "  :class:`~sklearn.preprocessing.StandardScaler`;\n",
+    "* train and time the pipeline fitting;\n",
+    "* measure the performance of the clustering obtained via different metrics.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "from sklearn import metrics\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "\n",
+    "def bench_k_means(kmeans, name, data, labels):\n",
+    "    \"\"\"Benchmark to evaluate the KMeans initialization methods.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kmeans : KMeans instance\n",
+    "        A :class:`~sklearn.cluster.KMeans` instance with the initialization\n",
+    "        already set.\n",
+    "    name : str\n",
+    "        Name given to the strategy. It will be used to show the results in a\n",
+    "        table.\n",
+    "    data : ndarray of shape (n_samples, n_features)\n",
+    "        The data to cluster.\n",
+    "    labels : ndarray of shape (n_samples,)\n",
+    "        The labels used to compute the clustering metrics which requires some\n",
+    "        supervision.\n",
+    "    \"\"\"\n",
+    "    t0 = time()\n",
+    "    estimator = make_pipeline(StandardScaler(), kmeans).fit(data)\n",
+    "    fit_time = time() - t0\n",
+    "    results = [name, fit_time, estimator[-1].inertia_]\n",
+    "\n",
+    "    # Define the metrics which require only the true labels and estimator\n",
+    "    # labels\n",
+    "    clustering_metrics = [\n",
+    "        metrics.homogeneity_score,\n",
+    "        metrics.completeness_score,\n",
+    "        metrics.v_measure_score,\n",
+    "        metrics.adjusted_rand_score,\n",
+    "        metrics.adjusted_mutual_info_score,\n",
+    "    ]\n",
+    "    results += [m(labels, estimator[-1].labels_) for m in clustering_metrics]\n",
+    "    \n",
+    "    print('------Test------')\n",
+    "    print('estimator[-1].labels_', estimator[-1].labels_)\n",
+    "    print('------Test------')\n",
+    "\n",
+    "    # The silhouette score requires the full dataset\n",
+    "    results += [\n",
+    "        metrics.silhouette_score(data, estimator[-1].labels_,\n",
+    "                                 metric=\"euclidean\", sample_size=300,)\n",
+    "    ]\n",
+    "\n",
+    "    # Show the results\n",
+    "    formatter_result = (\"{:9s}\\t{:.3f}s\\t{:.0f}\\t{:.3f}\\t{:.3f}\"\n",
+    "                        \"\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\")\n",
+    "    print(formatter_result.format(*results))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run the benchmark\n",
+    "\n",
+    "We will compare three approaches:\n",
+    "\n",
+    "* an initialization using `kmeans++`. This method is stochastic and we will\n",
+    "  run the initialization 4 times;\n",
+    "* a random initialization. This method is stochastic as well and we will run\n",
+    "  the initialization 4 times;\n",
+    "* an initialization based on a :class:`~sklearn.decomposition.PCA`\n",
+    "  projection. Indeed, we will use the components of the\n",
+    "  :class:`~sklearn.decomposition.PCA` to initialize KMeans. This method is\n",
+    "  deterministic and a single initialization suffice.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________\n",
+      "init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette\n",
+      "------Test------\n",
+      "estimator[-1].labels_ [2 8 8 3 9 3 1 0 3 3 2 6 4 3 9 5 1 0 8 3 2 6 8 3 6 5 1 6 8 3 2 3 5 5 1 5 2\n",
+      " 3 8 3 8 9 6 0 0 3 5 6 2 2 8 6 0 8 6 2 6 8 1 3 3 0 3 5 9 1 1 1 9 8 6 5 2 3\n",
+      " 3 6 5 6 2 2 6 0 1 3 5 6 0 8 1 3 6 5 3 8 0 8 8 9 3 8 9 2 5 5 1 3 1 6 0 5 9\n",
+      " 9 0 8 8 8 8 5 0 3 8 9 8 8 9 3 2 8 3 8 2 6 5 5 9 5 1 0 8 3 2 6 8 3 9 5 1 0\n",
+      " 8 3 2 6 8 3 9 5 1 0 8 3 2 3 5 5 1 5 2 3 8 3 8 9 6 0 0 3 5 6 2 2 5 5 0 8 5\n",
+      " 2 6 8 1 3 3 0 3 3 9 1 1 1 9 3 6 5 2 3 5 8 8 8 2 2 6 0 1 3 8 6 0 3 6 3 3 6\n",
+      " 0 1 8 9 0 6 9 2 5 0 1 3 1 6 0 5 9 9 0 5 8 5 8 5 5 6 8 8 9 3 2 8 3 8 2 8 5\n",
+      " 3 9 3 1 7 3 3 2 6 5 3 9 5 1 0 8 0 2 8 5 3 9 3 1 7 8 3 2 3 3 3 1 3 2 3 8 3\n",
+      " 8 9 8 0 0 3 3 8 2 2 5 8 0 3 5 2 6 5 1 5 3 7 3 3 9 1 1 1 9 0 8 5 2 0 3 5 3\n",
+      " 5 2 2 8 0 1 3 5 6 0 9 1 3 8 3 0 8 0 1 8 9 5 8 9 2 3 3 1 0 1 8 0 5 9 9 0 5\n",
+      " 3 5 5 3 0 6 5 9 3 3 9 6 2 8 0 3 2 6 4 3 9 3 1 7 6 3 2 6 4 3 9 5 1 7 8 3 2\n",
+      " 6 5 3 9 8 1 7 3 3 2 3 3 5 1 3 2 3 3 3 3 9 6 7 7 3 6 6 2 2 4 5 7 3 5 2 4 3\n",
+      " 1 3 3 7 3 3 9 1 1 1 9 3 6 3 2 3 3 4 8 5 2 2 6 7 1 3 5 6 0 6 1 3 6 3 3 6 6\n",
+      " 1 3 9 3 6 9 2 5 3 1 3 8 6 7 5 6 9 7 5 3 5 8 5 0 3 0 6 8 8 9 3 2 3 3 3 2 4\n",
+      " 4 3 9 3 1 0 8 3 2 4 8 3 0 3 1 0 8 3 2 4 4 3 9 3 1 0 8 3 2 8 3 3 1 3 2 3 6\n",
+      " 3 8 9 4 0 0 3 3 4 2 2 4 4 0 1 4 2 4 4 1 3 3 0 3 3 9 1 1 1 9 3 4 3 2 3 3 4\n",
+      " 8 5 2 2 4 0 1 4 4 4 0 9 1 3 4 3 3 4 0 1 8 9 3 8 9 2 3 3 1 3 1 4 0 3 9 9 0\n",
+      " 4 8 5 4 0 0 3 3 9 6 8 9 3 2 6 3 6 2 8 5 3 9 0 1 0 3 7 2 8 5 3 9 5 1 0 8 6\n",
+      " 2 8 5 3 6 5 1 7 3 6 2 6 9 5 1 5 2 6 8 6 3 9 8 7 6 3 5 8 2 2 3 8 0 8 8 2 8\n",
+      " 3 1 3 3 0 3 3 9 1 1 1 9 6 8 1 2 6 0 8 3 8 2 2 8 0 1 3 5 8 7 9 1 3 8 3 6 8\n",
+      " 0 1 8 9 3 8 0 2 3 3 1 6 1 8 0 5 9 7 0 8 8 8 5 5 0 7 5 9 3 8 0 6 2 8 6 8 2\n",
+      " 8 5 3 9 5 1 0 1 3 2 8 5 3 9 5 2 0 6 6 2 8 5 3 9 5 1 0 8 3 2 3 5 0 1 5 2 3\n",
+      " 3 3 3 9 8 0 0 3 5 8 2 2 8 5 0 6 5 2 4 5 1 3 3 0 3 3 9 1 1 1 9 3 8 8 2 3 5\n",
+      " 5 8 5 2 2 8 0 1 3 0 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9 2 5 3 1 3 1 8 0 5 9 9\n",
+      " 0 5 6 0 5 5 0 3 5 9 6 3 9 3 2 8 3 8 4 5 3 9 3 1 0 3 3 2 4 5 3 9 3 1 0 3 3\n",
+      " 2 4 5 3 9 3 1 0 3 3 2 3 3 3 1 3 2 3 3 3 8 9 4 0 0 3 3 4 5 0 3 4 2 4 4 1 3\n",
+      " 3 0 3 3 6 1 1 1 9 3 4 3 2 3 3 4 3 4 2 2 4 0 1 3 5 4 9 1 3 4 3 3 4 0 1 3 7\n",
+      " 3 4 9 2 5 3 1 3 1 4 7 3 9 7 0 5 3 4 5 3 0 3 3 7 7 3 2 3 3 3 2 8 5 3 7 5 1\n",
+      " 0 0 3 2 8 4 3 7 5 1 0 8 3 2 8 4 3 9 3 1 0 5 3 2 3 5 5 1 0 2 3 5 3 8 7 8 0\n",
+      " 0 3 3 8 2 2 0 8 4 2 8 4 1 3 3 0 3 3 9 1 1 1 0 3 8 6 2 3 5 4 3 4 2 2 8 0 1\n",
+      " 3 4 8 0 9 1 3 8 0 3 8 0 1 3 9 3 8 9 2 5 3 1 3 1 8 0 3 9 9 0 5 8 4 4 5 0 3\n",
+      " 9 9 3 3 9 3 2 5 3 5 2 6 5 3 9 3 1 0 5 3 2 6 4 3 9 3 1 0 8 3 2 8 5 3 9 5 1\n",
+      " 0 5 3 2 3 3 5 1 5 2 3 5 3 5 9 8 0 0 5 1 8 2 2 5 5 0 5 5 2 8 5 1 3 3 0 3 3\n",
+      " 9 1 1 1 9 3 8 5 2 3 3 5 5 3 2 2 8 0 1 5 5 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9\n",
+      " 2 5 5 1 3 1 8 0 5 9 9 0 5 5 5 5 5 0 3 5 9 8 8 9 3 2 8 3 8 2 6 4 3 9 3 1 5\n",
+      " 8 3 2 8 4 3 9 5 1 0 5 3 2 6 3 3 9 3 1 0 8 3 2 3 5 3 1 5 2 3 8 3 8 9 8 0 0\n",
+      " 3 3 8 2 2 4 4 0 8 4 2 6 4 1 3 3 0 3 3 9 1 1 1 9 3 8 3 2 3 1 5 8 3 2 2 6 0\n",
+      " 1 3 4 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9 2 8 3 1 3 1 8 0 8 9 9 0 4 8 4 4 3 0\n",
+      " 3 5 9 8 8 9 3 2 8 2 6 5 3 9 3 1 0 3 3 2 6 5 3 9 3 1 0 3 3 2 6 5 3 9 3 1 0\n",
+      " 3 3 2 3 3 3 1 3 2 3 3 3 3 9 6 0 0 3 3 6 2 2 5 5 7 6 5 2 6 5 1 3 3 0 3 3 9\n",
+      " 1 1 1 9 3 6 3 2 3 3 5 3 5 2 2 6 0 1 3 5 6 0 9 1 3 6 3 3 6 0 1 3 9 3 6 9 2\n",
+      " 3 3 1 3 1 6 0 3 9 9 0 5 6 5 5 3 0 3 3 9 6 8 9 3 2 6 3 8 2 8 4 3 9 5 6 5 8\n",
+      " 6 2 8 4 3 9 5 1 6 2 8 4 3 9 5 8 0 8 7 9 6 5 5 1 8 2 6 8 5 8 9 8 0 5 5 5 8\n",
+      " 2 2 4 5 5 8 4 2 8 5 1 8 0 5 0 8 9 1 1 1 8 6 8 5 2 3 8 4 8 2 8 0 1 3 5 8 5\n",
+      " 8 1 3 8 3 0 8 5 1 8 9 3 8 9 2 5 3 8 3 8 8 0 8 9 9 0 4 3 5 5 3 8 5 9 5 2 8\n",
+      " 0 0 2 8 5 3 9 3 1 0 3 3 2 8 8 8 9 3 1 0 5 3 2 8 5 5 9 9 1 0 8 3 2 3 3 6 1\n",
+      " 3 2 3 3 3 5 7 8 0 0 0 3 8 2 2 5 5 0 8 5 2 8 5 1 8 0 0 3 0 9 1 1 1 9 3 8 3\n",
+      " 2 3 3 5 8 5 2 2 8 0 1 0 8 8 0 9 1 3 8 3 3 8 0 1 8 9 8 8 9 2 3 3 1 3 1 8 0\n",
+      " 3 9 9 0 5 8 5 5 3 0 3 3 9 8 8 9 3 2 8 3 3]\n",
+      "------Test------\n",
+      "k-means++\t0.157s\t69485\t0.613\t0.660\t0.636\t0.482\t0.632\t0.134\n",
+      "------Test------\n",
+      "estimator[-1].labels_ [8 5 5 7 6 7 4 0 7 7 8 0 1 7 6 9 4 0 5 7 8 0 5 0 6 5 4 0 5 7 8 7 9 9 4 9 8\n",
+      " 7 5 7 5 6 0 0 0 7 5 0 8 8 5 0 0 5 0 8 0 0 4 7 7 0 7 9 6 4 4 4 6 5 0 9 8 7\n",
+      " 5 0 5 0 8 8 0 0 4 7 9 0 0 5 4 7 0 9 7 5 0 5 5 6 7 5 6 8 5 9 4 7 4 0 0 5 6\n",
+      " 6 0 5 5 0 5 9 0 7 5 6 5 5 6 7 8 5 7 5 8 0 9 9 6 9 4 0 5 7 8 0 5 9 6 9 4 5\n",
+      " 5 7 8 0 5 0 6 9 4 5 5 7 8 7 9 9 4 9 8 7 5 7 5 6 0 0 0 7 9 0 8 8 5 5 0 5 5\n",
+      " 8 0 5 4 7 7 5 7 7 6 4 4 4 6 7 0 9 8 7 9 5 5 5 8 8 0 5 4 7 5 0 5 7 0 7 7 0\n",
+      " 0 4 5 6 0 0 6 8 9 0 4 7 4 0 5 5 6 6 5 9 5 9 5 9 9 6 5 5 6 7 8 5 7 5 8 5 9\n",
+      " 7 6 7 4 2 7 0 8 0 9 7 6 5 4 0 5 0 8 5 9 7 6 7 4 2 5 7 8 7 7 7 4 7 8 7 5 7\n",
+      " 5 6 5 0 0 7 7 5 8 8 9 5 0 7 5 8 0 9 4 9 7 2 7 7 6 4 4 4 6 0 5 5 8 0 7 9 7\n",
+      " 9 8 8 5 0 4 7 5 5 0 6 4 7 5 9 0 5 5 4 5 6 9 5 6 8 7 7 4 0 4 5 5 9 6 6 0 9\n",
+      " 7 9 9 7 0 0 9 6 7 7 6 0 8 5 0 7 8 0 1 7 6 5 4 2 0 7 8 5 1 7 6 5 4 2 5 7 8\n",
+      " 0 9 7 6 5 4 2 0 7 8 7 7 5 4 7 8 7 7 7 5 6 0 2 2 7 0 5 8 8 1 9 2 7 9 8 1 7\n",
+      " 4 7 7 2 7 7 6 4 4 4 6 7 0 0 8 7 5 1 0 9 8 8 5 2 4 7 9 5 0 6 4 7 0 7 7 0 0\n",
+      " 4 7 6 9 5 6 8 9 7 4 7 5 0 2 5 6 6 2 9 7 9 0 5 0 7 0 6 5 5 6 0 8 7 7 7 8 1\n",
+      " 1 7 6 7 4 5 0 7 8 1 5 7 6 7 4 0 5 7 8 1 1 7 6 7 4 0 5 7 8 5 7 7 4 7 8 7 0\n",
+      " 7 5 6 1 0 0 7 7 1 8 8 3 1 0 4 1 8 1 1 4 7 7 0 7 7 6 4 4 4 6 7 1 7 8 7 7 1\n",
+      " 4 9 8 8 1 0 4 1 1 1 0 6 4 7 1 7 7 1 0 4 5 6 7 5 6 8 7 7 4 7 4 1 0 7 6 6 5\n",
+      " 1 0 9 1 0 0 7 7 6 0 0 6 7 8 0 7 0 8 5 9 7 6 0 4 0 7 2 8 5 5 7 6 5 4 0 5 0\n",
+      " 8 5 7 7 6 5 4 2 7 0 8 0 6 5 4 5 8 0 5 0 5 6 0 2 0 7 5 0 8 8 7 5 0 5 5 8 5\n",
+      " 5 4 7 7 0 7 7 6 4 4 4 6 0 5 5 8 0 0 5 7 5 8 8 0 0 4 7 9 5 2 6 4 7 5 7 0 5\n",
+      " 0 4 5 6 7 5 0 8 7 7 4 0 4 5 0 5 6 2 0 5 5 5 5 5 0 2 9 6 7 5 0 2 8 5 0 5 8\n",
+      " 5 9 7 6 9 4 0 4 7 8 5 9 7 6 9 8 5 0 7 8 5 9 7 6 9 4 0 5 7 8 7 5 5 4 5 8 7\n",
+      " 7 7 7 6 5 5 0 7 5 5 8 8 5 9 5 0 9 8 1 9 4 7 7 0 0 0 6 4 4 4 6 7 5 5 8 7 9\n",
+      " 9 5 9 8 8 5 0 4 7 5 5 0 6 4 7 5 7 7 5 5 4 5 6 7 5 6 8 5 7 4 7 4 0 5 9 6 6\n",
+      " 0 9 0 0 5 5 5 7 9 6 0 7 6 7 8 5 7 5 1 9 7 6 7 4 0 7 7 8 1 9 7 6 7 4 5 7 7\n",
+      " 8 1 9 7 6 7 4 0 7 7 8 7 7 7 4 7 8 7 7 7 5 6 1 0 0 7 7 1 9 0 7 1 8 1 1 4 7\n",
+      " 7 0 7 7 6 4 4 4 6 7 1 7 8 7 7 1 7 1 8 8 1 0 4 7 9 1 6 4 7 1 7 7 1 0 4 7 2\n",
+      " 7 1 6 8 9 7 4 7 4 1 2 7 6 2 0 1 7 1 9 7 0 7 7 2 2 7 8 7 7 7 8 5 9 7 2 5 4\n",
+      " 0 0 7 8 5 5 7 2 5 4 0 5 7 8 5 1 7 6 7 4 0 9 7 8 7 5 5 4 5 8 7 9 7 5 2 5 0\n",
+      " 0 7 7 5 8 8 0 5 1 8 5 1 4 3 7 5 7 7 6 4 4 4 0 7 5 0 8 7 9 1 7 1 8 8 5 0 1\n",
+      " 7 1 5 0 6 4 7 5 0 7 5 0 4 5 6 7 5 6 8 5 7 4 7 4 5 0 7 6 6 5 9 5 1 1 5 0 7\n",
+      " 5 6 5 7 6 7 8 9 7 9 8 5 9 7 6 7 4 0 7 7 8 0 1 7 6 7 4 0 5 7 8 5 9 7 6 9 4\n",
+      " 0 5 7 8 7 7 9 4 9 8 7 9 7 5 6 5 0 0 0 4 5 8 8 9 5 0 5 9 8 5 9 4 7 7 0 7 7\n",
+      " 6 4 4 4 6 7 5 5 8 7 7 9 9 7 8 8 5 0 4 9 9 5 5 6 4 7 5 7 7 5 5 4 5 6 7 5 6\n",
+      " 8 9 9 4 7 4 3 0 5 6 6 0 9 3 9 9 9 0 7 9 6 5 5 6 7 8 5 7 5 8 0 1 9 6 7 4 9\n",
+      " 5 7 8 5 1 7 6 9 4 0 5 7 8 0 5 7 6 5 4 0 5 7 8 7 9 5 4 5 8 7 5 7 5 6 5 0 0\n",
+      " 7 7 5 8 8 1 1 0 5 1 8 0 1 4 7 7 0 7 7 6 4 4 4 6 7 5 7 8 7 4 9 5 7 8 8 0 0\n",
+      " 4 7 1 5 5 6 4 7 5 7 7 5 0 4 5 6 7 5 6 8 5 7 4 7 4 5 5 5 6 6 0 1 5 1 1 4 0\n",
+      " 7 7 6 5 5 6 7 8 5 8 0 9 7 6 7 4 0 7 7 8 0 9 7 6 7 4 0 7 7 8 0 9 7 6 7 4 0\n",
+      " 7 7 8 7 7 7 4 7 8 7 7 7 7 6 0 0 0 7 7 0 8 8 9 9 2 0 9 8 0 9 4 7 7 0 7 7 6\n",
+      " 4 4 4 6 7 0 7 8 7 7 9 0 9 8 8 0 0 4 7 9 0 0 6 4 7 0 7 7 0 0 4 0 6 7 0 6 8\n",
+      " 7 7 4 7 4 0 0 7 6 6 0 9 0 9 9 7 0 7 7 6 0 0 6 7 8 0 7 0 8 5 1 7 6 5 4 9 5\n",
+      " 0 8 5 1 0 6 5 4 0 8 5 1 7 6 5 4 5 5 2 6 0 5 9 4 5 8 0 0 5 5 6 5 0 9 5 9 5\n",
+      " 4 8 1 9 9 5 1 8 5 9 4 5 5 9 5 5 6 4 4 4 5 0 5 5 8 7 5 1 0 8 5 5 4 7 9 5 9\n",
+      " 5 4 7 5 5 0 5 9 4 5 6 7 5 6 8 5 7 4 7 5 5 0 5 6 6 5 1 7 5 9 7 5 5 6 5 8 5\n",
+      " 0 0 8 5 9 7 6 7 4 0 7 7 8 5 5 5 6 7 4 0 9 7 8 5 5 9 6 6 4 0 5 7 8 7 5 0 4\n",
+      " 5 8 7 7 7 9 2 5 0 0 0 0 5 8 8 9 9 0 5 9 8 5 9 4 5 0 5 7 0 6 4 4 4 6 7 5 7\n",
+      " 8 7 7 9 5 9 8 8 5 6 4 0 5 5 6 6 4 7 5 7 7 5 0 4 5 6 5 5 6 8 7 7 4 7 4 5 5\n",
+      " 7 6 6 0 9 5 9 5 7 5 7 7 6 5 5 6 7 8 5 7 7]\n",
+      "------Test------\n",
+      "random   \t0.074s\t69952\t0.545\t0.616\t0.578\t0.415\t0.574\t0.117\n",
+      "------Test------\n",
+      "estimator[-1].labels_ [1 2 2 7 0 8 0 5 2 8 1 9 6 7 0 4 0 5 2 8 1 9 2 7 9 4 0 9 2 8 1 8 4 3 0 4 1\n",
+      " 8 2 8 2 0 9 5 5 7 4 9 1 1 2 2 5 2 9 1 9 2 0 7 7 5 7 7 0 0 0 0 0 2 9 4 1 8\n",
+      " 4 2 2 9 1 1 9 5 0 7 3 2 5 4 0 7 9 7 8 2 5 2 2 0 7 2 0 1 4 3 0 8 2 9 5 4 0\n",
+      " 0 5 2 2 2 2 4 5 8 4 2 2 2 0 7 1 2 8 2 1 9 3 7 9 3 0 5 2 8 1 9 3 7 0 3 0 5\n",
+      " 2 8 1 9 2 7 0 4 0 5 2 8 1 8 4 3 0 4 1 8 2 8 2 0 9 5 5 7 3 9 1 1 3 3 5 2 3\n",
+      " 1 9 2 0 7 7 5 7 7 0 0 0 0 0 8 9 4 1 8 3 2 2 2 1 1 9 5 0 7 2 9 5 7 9 7 8 9\n",
+      " 5 0 2 0 7 9 0 1 4 5 0 8 0 9 5 4 0 0 5 3 2 3 2 3 3 9 2 2 0 8 1 2 8 2 1 2 3\n",
+      " 7 0 8 0 5 8 4 1 2 3 7 0 4 0 5 2 5 1 2 3 7 0 4 0 5 2 8 1 8 4 4 0 8 1 8 2 8\n",
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+      " 3 2 3 1 1 2 5 0 7 2 2 5 0 0 7 2 7 8 2 5 0 2 0 7 2 0 1 4 7 0 8 0 2 5 4 0 0\n",
+      " 5 3 2 5 7 4 5 8 4 0 8 8 0 8 1 2 8 2 6 3 8 0 4 0 5 8 8 1 6 3 7 0 8 0 5 8 8\n",
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+      " 7 6 2 5 9 0 7 2 5 8 2 5 0 2 9 7 2 0 1 4 7 0 8 0 2 5 4 0 9 5 3 2 6 6 4 5 8\n",
+      " 4 0 8 7 0 8 1 3 8 3 1 2 3 7 0 4 0 5 7 8 1 9 6 7 0 4 0 5 2 8 1 2 3 7 0 3 0\n",
+      " 5 2 8 1 8 4 4 0 4 1 8 4 8 4 0 2 5 5 4 4 2 1 1 3 7 5 4 3 1 2 3 0 7 7 5 7 7\n",
+      " 0 0 0 0 0 8 2 4 1 8 4 3 4 7 1 1 2 5 0 7 7 2 5 0 0 7 2 7 8 2 5 0 2 0 7 2 0\n",
+      " 1 3 3 0 8 0 4 5 4 0 0 5 3 3 3 3 3 5 8 3 0 2 2 0 8 1 2 8 2 1 9 6 7 0 8 0 3\n",
+      " 2 8 1 2 6 7 0 4 0 5 2 8 1 9 2 7 0 4 6 5 2 8 1 8 4 4 0 4 1 8 2 8 2 0 2 5 5\n",
+      " 8 4 2 1 1 6 6 5 2 6 1 9 6 0 7 7 5 8 8 0 0 0 0 0 8 2 4 1 8 0 3 2 8 1 1 9 5\n",
+      " 0 7 6 2 5 0 6 7 2 7 8 2 5 0 2 0 8 2 0 1 4 7 0 8 0 2 5 4 0 0 5 6 2 6 6 8 5\n",
+      " 8 4 0 2 2 0 8 1 2 1 9 3 7 0 4 0 5 8 8 1 9 3 7 0 8 0 5 8 8 1 9 3 7 0 8 0 5\n",
+      " 8 8 1 8 8 4 0 8 1 8 8 8 8 0 9 5 5 7 8 9 1 1 3 3 9 9 3 1 9 3 0 7 7 5 7 7 0\n",
+      " 0 0 0 0 8 9 8 1 7 4 3 8 3 1 1 9 5 0 7 3 9 5 0 0 7 9 7 8 9 5 0 8 0 8 9 0 1\n",
+      " 8 7 0 8 0 9 5 8 0 0 5 3 8 7 3 4 5 8 4 0 9 2 0 8 1 9 8 2 1 2 6 7 0 4 0 3 2\n",
+      " 9 1 2 6 7 0 4 0 9 1 2 6 7 0 4 0 5 4 9 0 9 4 3 0 4 1 9 2 4 2 0 2 5 3 7 3 2\n",
+      " 0 1 6 3 3 2 6 1 2 3 0 2 5 3 5 2 5 0 0 0 2 9 2 4 0 8 2 6 2 1 2 5 0 7 3 2 3\n",
+      " 2 0 7 2 7 5 2 3 0 2 0 7 2 0 1 4 7 0 8 0 2 5 4 0 5 5 6 8 4 3 7 4 4 0 4 1 2\n",
+      " 5 5 1 2 3 7 0 4 0 5 7 8 1 2 2 7 0 4 0 5 3 8 1 2 3 3 0 0 0 5 2 8 1 8 4 4 0\n",
+      " 4 1 8 8 8 3 9 2 5 5 5 4 2 1 1 3 3 5 2 3 1 2 3 0 2 2 5 8 2 9 0 0 0 0 8 2 4\n",
+      " 1 8 0 3 2 3 1 1 2 5 0 7 2 2 5 0 0 7 2 7 8 2 5 0 2 0 4 2 0 1 8 7 0 8 0 2 5\n",
+      " 8 0 0 5 3 2 3 3 8 5 8 8 0 2 2 0 8 1 2 8 8]\n",
+      "------Test------\n",
+      "PCA-based\t0.030s\t72686\t0.636\t0.658\t0.647\t0.521\t0.643\t0.138\n",
+      "__________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "print(82 * '_')\n",
+    "print('init\\t\\ttime\\tinertia\\thomo\\tcompl\\tv-meas\\tARI\\tAMI\\tsilhouette')\n",
+    "\n",
+    "kmeans = KMeans(init=\"k-means++\", n_clusters=n_digits, n_init=4,\n",
+    "                random_state=0)\n",
+    "bench_k_means(kmeans=kmeans, name=\"k-means++\", data=data, labels=labels)\n",
+    "\n",
+    "kmeans = KMeans(init=\"random\", n_clusters=n_digits, n_init=4, random_state=0)\n",
+    "bench_k_means(kmeans=kmeans, name=\"random\", data=data, labels=labels)\n",
+    "\n",
+    "pca = PCA(n_components=n_digits).fit(data)\n",
+    "kmeans = KMeans(init=pca.components_, n_clusters=n_digits, n_init=1)\n",
+    "bench_k_means(kmeans=kmeans, name=\"PCA-based\", data=data, labels=labels)\n",
+    "\n",
+    "print(82 * '_')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize the results on PCA-reduced data\n",
+    "\n",
+    ":class:`~sklearn.decomposition.PCA` allows to project the data from the\n",
+    "original 64-dimensional space into a lower dimensional space. Subsequently,\n",
+    "we can use :class:`~sklearn.decomposition.PCA` to project into a\n",
+    "2-dimensional space and plot the data and the clusters in this new space.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "reduced_data = PCA(n_components=2).fit_transform(data)\n",
+    "kmeans = KMeans(init=\"k-means++\", n_clusters=n_digits, n_init=4)\n",
+    "kmeans.fit(reduced_data)\n",
+    "\n",
+    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
+    "h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "\n",
+    "# Plot the decision boundary. For that, we will assign a color to each\n",
+    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
+    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "# Obtain labels for each point in mesh. Use last trained model.\n",
+    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "# Put the result into a color plot\n",
+    "Z = Z.reshape(xx.shape)\n",
+    "plt.figure(1)\n",
+    "plt.clf()\n",
+    "plt.imshow(Z, interpolation=\"nearest\",\n",
+    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
+    "           cmap=plt.cm.Paired, aspect=\"auto\", origin=\"lower\")\n",
+    "\n",
+    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
+    "# Plot the centroids as a white X\n",
+    "centroids = kmeans.cluster_centers_\n",
+    "plt.scatter(centroids[:, 0], centroids[:, 1], marker=\"x\", s=169, linewidths=3,\n",
+    "            color=\"w\", zorder=10)\n",
+    "plt.title(\"K-means clustering on the digits dataset (PCA-reduced data)\\n\"\n",
+    "          \"Centroids are marked with white cross\")\n",
+    "plt.xlim(x_min, x_max)\n",
+    "plt.ylim(y_min, y_max)\n",
+    "plt.xticks(())\n",
+    "plt.yticks(())\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From ffedea52ede637a710d250cd93d4e285bfb165a7 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Sat, 20 Feb 2021 12:13:37 -0800
Subject: [PATCH 02/16] clean up output

---
 tour_model_eval/plot_kmeans_digits.ipynb | 265 +----------------------
 1 file changed, 10 insertions(+), 255 deletions(-)

diff --git a/tour_model_eval/plot_kmeans_digits.ipynb b/tour_model_eval/plot_kmeans_digits.ipynb
index 62f7cd6..e88eb15 100644
--- a/tour_model_eval/plot_kmeans_digits.ipynb
+++ b/tour_model_eval/plot_kmeans_digits.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,17 +39,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Automatically created module for IPython interactive environment\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(__doc__)"
    ]
@@ -68,69 +60,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# digits: 10; # samples: 1797; # features 64\n",
-      "------Test------\n",
-      "data.shape is  (1797, 64)\n",
-      "labels is [0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0\n",
-      " 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9\n",
-      " 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4\n",
-      " 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7\n",
-      " 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2\n",
-      " 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 3 1 3 9 1\n",
-      " 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 5 4 8 8 4 9 0 8 9 8 0 1 2\n",
-      " 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9\n",
-      " 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8\n",
-      " 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2\n",
-      " 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0\n",
-      " 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2\n",
-      " 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7\n",
-      " 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1\n",
-      " 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8\n",
-      " 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2\n",
-      " 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7\n",
-      " 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n",
-      " 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1\n",
-      " 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1\n",
-      " 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0\n",
-      " 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9\n",
-      " 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5\n",
-      " 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4\n",
-      " 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n",
-      " 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 2 7 8 2 0 1 2 6 3\n",
-      " 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 4 6 3 1 3 9 1 7 6 8 4\n",
-      " 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 4 9 0 8 9 8 0 1 2 3 4 5 6\n",
-      " 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7\n",
-      " 7 3 5 1 0 0 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6\n",
-      " 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9\n",
-      " 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6\n",
-      " 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3\n",
-      " 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4\n",
-      " 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7\n",
-      " 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7\n",
-      " 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7\n",
-      " 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7\n",
-      " 9 5 4 8 8 4 9 0 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7\n",
-      " 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4\n",
-      " 6 6 6 4 9 1 5 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0\n",
-      " 5 3 6 9 6 1 7 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8 0 1 2 3 4 5 6 7 8\n",
-      " 9 0 1 2 3 4 5 6 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6 5 0 9 8 9 8 4 1 7 7 3 5 1\n",
-      " 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5 0 9 5 2 8 0 1 7 6 3 2 1 7\n",
-      " 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7 5 4 4 7 2 2 5 7 9 5 4 4 9 0 8\n",
-      " 9 8 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 9 5 5 6\n",
-      " 5 0 9 8 9 8 4 1 7 7 3 5 1 0 0 2 2 7 8 2 0 1 2 6 3 3 7 3 3 4 6 6 6 4 9 1 5\n",
-      " 0 9 5 2 8 2 0 0 1 7 6 3 2 1 7 4 6 3 1 3 9 1 7 6 8 4 3 1 4 0 5 3 6 9 6 1 7\n",
-      " 5 4 4 7 2 8 2 2 5 7 9 5 4 8 8 4 9 0 8 9 8]\n",
-      "------Test------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from sklearn.datasets import load_digits\n",
@@ -167,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -247,175 +179,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "__________________________________________________________________________________\n",
-      "init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette\n",
-      "------Test------\n",
-      "estimator[-1].labels_ [2 8 8 3 9 3 1 0 3 3 2 6 4 3 9 5 1 0 8 3 2 6 8 3 6 5 1 6 8 3 2 3 5 5 1 5 2\n",
-      " 3 8 3 8 9 6 0 0 3 5 6 2 2 8 6 0 8 6 2 6 8 1 3 3 0 3 5 9 1 1 1 9 8 6 5 2 3\n",
-      " 3 6 5 6 2 2 6 0 1 3 5 6 0 8 1 3 6 5 3 8 0 8 8 9 3 8 9 2 5 5 1 3 1 6 0 5 9\n",
-      " 9 0 8 8 8 8 5 0 3 8 9 8 8 9 3 2 8 3 8 2 6 5 5 9 5 1 0 8 3 2 6 8 3 9 5 1 0\n",
-      " 8 3 2 6 8 3 9 5 1 0 8 3 2 3 5 5 1 5 2 3 8 3 8 9 6 0 0 3 5 6 2 2 5 5 0 8 5\n",
-      " 2 6 8 1 3 3 0 3 3 9 1 1 1 9 3 6 5 2 3 5 8 8 8 2 2 6 0 1 3 8 6 0 3 6 3 3 6\n",
-      " 0 1 8 9 0 6 9 2 5 0 1 3 1 6 0 5 9 9 0 5 8 5 8 5 5 6 8 8 9 3 2 8 3 8 2 8 5\n",
-      " 3 9 3 1 7 3 3 2 6 5 3 9 5 1 0 8 0 2 8 5 3 9 3 1 7 8 3 2 3 3 3 1 3 2 3 8 3\n",
-      " 8 9 8 0 0 3 3 8 2 2 5 8 0 3 5 2 6 5 1 5 3 7 3 3 9 1 1 1 9 0 8 5 2 0 3 5 3\n",
-      " 5 2 2 8 0 1 3 5 6 0 9 1 3 8 3 0 8 0 1 8 9 5 8 9 2 3 3 1 0 1 8 0 5 9 9 0 5\n",
-      " 3 5 5 3 0 6 5 9 3 3 9 6 2 8 0 3 2 6 4 3 9 3 1 7 6 3 2 6 4 3 9 5 1 7 8 3 2\n",
-      " 6 5 3 9 8 1 7 3 3 2 3 3 5 1 3 2 3 3 3 3 9 6 7 7 3 6 6 2 2 4 5 7 3 5 2 4 3\n",
-      " 1 3 3 7 3 3 9 1 1 1 9 3 6 3 2 3 3 4 8 5 2 2 6 7 1 3 5 6 0 6 1 3 6 3 3 6 6\n",
-      " 1 3 9 3 6 9 2 5 3 1 3 8 6 7 5 6 9 7 5 3 5 8 5 0 3 0 6 8 8 9 3 2 3 3 3 2 4\n",
-      " 4 3 9 3 1 0 8 3 2 4 8 3 0 3 1 0 8 3 2 4 4 3 9 3 1 0 8 3 2 8 3 3 1 3 2 3 6\n",
-      " 3 8 9 4 0 0 3 3 4 2 2 4 4 0 1 4 2 4 4 1 3 3 0 3 3 9 1 1 1 9 3 4 3 2 3 3 4\n",
-      " 8 5 2 2 4 0 1 4 4 4 0 9 1 3 4 3 3 4 0 1 8 9 3 8 9 2 3 3 1 3 1 4 0 3 9 9 0\n",
-      " 4 8 5 4 0 0 3 3 9 6 8 9 3 2 6 3 6 2 8 5 3 9 0 1 0 3 7 2 8 5 3 9 5 1 0 8 6\n",
-      " 2 8 5 3 6 5 1 7 3 6 2 6 9 5 1 5 2 6 8 6 3 9 8 7 6 3 5 8 2 2 3 8 0 8 8 2 8\n",
-      " 3 1 3 3 0 3 3 9 1 1 1 9 6 8 1 2 6 0 8 3 8 2 2 8 0 1 3 5 8 7 9 1 3 8 3 6 8\n",
-      " 0 1 8 9 3 8 0 2 3 3 1 6 1 8 0 5 9 7 0 8 8 8 5 5 0 7 5 9 3 8 0 6 2 8 6 8 2\n",
-      " 8 5 3 9 5 1 0 1 3 2 8 5 3 9 5 2 0 6 6 2 8 5 3 9 5 1 0 8 3 2 3 5 0 1 5 2 3\n",
-      " 3 3 3 9 8 0 0 3 5 8 2 2 8 5 0 6 5 2 4 5 1 3 3 0 3 3 9 1 1 1 9 3 8 8 2 3 5\n",
-      " 5 8 5 2 2 8 0 1 3 0 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9 2 5 3 1 3 1 8 0 5 9 9\n",
-      " 0 5 6 0 5 5 0 3 5 9 6 3 9 3 2 8 3 8 4 5 3 9 3 1 0 3 3 2 4 5 3 9 3 1 0 3 3\n",
-      " 2 4 5 3 9 3 1 0 3 3 2 3 3 3 1 3 2 3 3 3 8 9 4 0 0 3 3 4 5 0 3 4 2 4 4 1 3\n",
-      " 3 0 3 3 6 1 1 1 9 3 4 3 2 3 3 4 3 4 2 2 4 0 1 3 5 4 9 1 3 4 3 3 4 0 1 3 7\n",
-      " 3 4 9 2 5 3 1 3 1 4 7 3 9 7 0 5 3 4 5 3 0 3 3 7 7 3 2 3 3 3 2 8 5 3 7 5 1\n",
-      " 0 0 3 2 8 4 3 7 5 1 0 8 3 2 8 4 3 9 3 1 0 5 3 2 3 5 5 1 0 2 3 5 3 8 7 8 0\n",
-      " 0 3 3 8 2 2 0 8 4 2 8 4 1 3 3 0 3 3 9 1 1 1 0 3 8 6 2 3 5 4 3 4 2 2 8 0 1\n",
-      " 3 4 8 0 9 1 3 8 0 3 8 0 1 3 9 3 8 9 2 5 3 1 3 1 8 0 3 9 9 0 5 8 4 4 5 0 3\n",
-      " 9 9 3 3 9 3 2 5 3 5 2 6 5 3 9 3 1 0 5 3 2 6 4 3 9 3 1 0 8 3 2 8 5 3 9 5 1\n",
-      " 0 5 3 2 3 3 5 1 5 2 3 5 3 5 9 8 0 0 5 1 8 2 2 5 5 0 5 5 2 8 5 1 3 3 0 3 3\n",
-      " 9 1 1 1 9 3 8 5 2 3 3 5 5 3 2 2 8 0 1 5 5 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9\n",
-      " 2 5 5 1 3 1 8 0 5 9 9 0 5 5 5 5 5 0 3 5 9 8 8 9 3 2 8 3 8 2 6 4 3 9 3 1 5\n",
-      " 8 3 2 8 4 3 9 5 1 0 5 3 2 6 3 3 9 3 1 0 8 3 2 3 5 3 1 5 2 3 8 3 8 9 8 0 0\n",
-      " 3 3 8 2 2 4 4 0 8 4 2 6 4 1 3 3 0 3 3 9 1 1 1 9 3 8 3 2 3 1 5 8 3 2 2 6 0\n",
-      " 1 3 4 8 0 9 1 3 8 3 3 8 0 1 8 9 3 8 9 2 8 3 1 3 1 8 0 8 9 9 0 4 8 4 4 3 0\n",
-      " 3 5 9 8 8 9 3 2 8 2 6 5 3 9 3 1 0 3 3 2 6 5 3 9 3 1 0 3 3 2 6 5 3 9 3 1 0\n",
-      " 3 3 2 3 3 3 1 3 2 3 3 3 3 9 6 0 0 3 3 6 2 2 5 5 7 6 5 2 6 5 1 3 3 0 3 3 9\n",
-      " 1 1 1 9 3 6 3 2 3 3 5 3 5 2 2 6 0 1 3 5 6 0 9 1 3 6 3 3 6 0 1 3 9 3 6 9 2\n",
-      " 3 3 1 3 1 6 0 3 9 9 0 5 6 5 5 3 0 3 3 9 6 8 9 3 2 6 3 8 2 8 4 3 9 5 6 5 8\n",
-      " 6 2 8 4 3 9 5 1 6 2 8 4 3 9 5 8 0 8 7 9 6 5 5 1 8 2 6 8 5 8 9 8 0 5 5 5 8\n",
-      " 2 2 4 5 5 8 4 2 8 5 1 8 0 5 0 8 9 1 1 1 8 6 8 5 2 3 8 4 8 2 8 0 1 3 5 8 5\n",
-      " 8 1 3 8 3 0 8 5 1 8 9 3 8 9 2 5 3 8 3 8 8 0 8 9 9 0 4 3 5 5 3 8 5 9 5 2 8\n",
-      " 0 0 2 8 5 3 9 3 1 0 3 3 2 8 8 8 9 3 1 0 5 3 2 8 5 5 9 9 1 0 8 3 2 3 3 6 1\n",
-      " 3 2 3 3 3 5 7 8 0 0 0 3 8 2 2 5 5 0 8 5 2 8 5 1 8 0 0 3 0 9 1 1 1 9 3 8 3\n",
-      " 2 3 3 5 8 5 2 2 8 0 1 0 8 8 0 9 1 3 8 3 3 8 0 1 8 9 8 8 9 2 3 3 1 3 1 8 0\n",
-      " 3 9 9 0 5 8 5 5 3 0 3 3 9 8 8 9 3 2 8 3 3]\n",
-      "------Test------\n",
-      "k-means++\t0.157s\t69485\t0.613\t0.660\t0.636\t0.482\t0.632\t0.134\n",
-      "------Test------\n",
-      "estimator[-1].labels_ [8 5 5 7 6 7 4 0 7 7 8 0 1 7 6 9 4 0 5 7 8 0 5 0 6 5 4 0 5 7 8 7 9 9 4 9 8\n",
-      " 7 5 7 5 6 0 0 0 7 5 0 8 8 5 0 0 5 0 8 0 0 4 7 7 0 7 9 6 4 4 4 6 5 0 9 8 7\n",
-      " 5 0 5 0 8 8 0 0 4 7 9 0 0 5 4 7 0 9 7 5 0 5 5 6 7 5 6 8 5 9 4 7 4 0 0 5 6\n",
-      " 6 0 5 5 0 5 9 0 7 5 6 5 5 6 7 8 5 7 5 8 0 9 9 6 9 4 0 5 7 8 0 5 9 6 9 4 5\n",
-      " 5 7 8 0 5 0 6 9 4 5 5 7 8 7 9 9 4 9 8 7 5 7 5 6 0 0 0 7 9 0 8 8 5 5 0 5 5\n",
-      " 8 0 5 4 7 7 5 7 7 6 4 4 4 6 7 0 9 8 7 9 5 5 5 8 8 0 5 4 7 5 0 5 7 0 7 7 0\n",
-      " 0 4 5 6 0 0 6 8 9 0 4 7 4 0 5 5 6 6 5 9 5 9 5 9 9 6 5 5 6 7 8 5 7 5 8 5 9\n",
-      " 7 6 7 4 2 7 0 8 0 9 7 6 5 4 0 5 0 8 5 9 7 6 7 4 2 5 7 8 7 7 7 4 7 8 7 5 7\n",
-      " 5 6 5 0 0 7 7 5 8 8 9 5 0 7 5 8 0 9 4 9 7 2 7 7 6 4 4 4 6 0 5 5 8 0 7 9 7\n",
-      " 9 8 8 5 0 4 7 5 5 0 6 4 7 5 9 0 5 5 4 5 6 9 5 6 8 7 7 4 0 4 5 5 9 6 6 0 9\n",
-      " 7 9 9 7 0 0 9 6 7 7 6 0 8 5 0 7 8 0 1 7 6 5 4 2 0 7 8 5 1 7 6 5 4 2 5 7 8\n",
-      " 0 9 7 6 5 4 2 0 7 8 7 7 5 4 7 8 7 7 7 5 6 0 2 2 7 0 5 8 8 1 9 2 7 9 8 1 7\n",
-      " 4 7 7 2 7 7 6 4 4 4 6 7 0 0 8 7 5 1 0 9 8 8 5 2 4 7 9 5 0 6 4 7 0 7 7 0 0\n",
-      " 4 7 6 9 5 6 8 9 7 4 7 5 0 2 5 6 6 2 9 7 9 0 5 0 7 0 6 5 5 6 0 8 7 7 7 8 1\n",
-      " 1 7 6 7 4 5 0 7 8 1 5 7 6 7 4 0 5 7 8 1 1 7 6 7 4 0 5 7 8 5 7 7 4 7 8 7 0\n",
-      " 7 5 6 1 0 0 7 7 1 8 8 3 1 0 4 1 8 1 1 4 7 7 0 7 7 6 4 4 4 6 7 1 7 8 7 7 1\n",
-      " 4 9 8 8 1 0 4 1 1 1 0 6 4 7 1 7 7 1 0 4 5 6 7 5 6 8 7 7 4 7 4 1 0 7 6 6 5\n",
-      " 1 0 9 1 0 0 7 7 6 0 0 6 7 8 0 7 0 8 5 9 7 6 0 4 0 7 2 8 5 5 7 6 5 4 0 5 0\n",
-      " 8 5 7 7 6 5 4 2 7 0 8 0 6 5 4 5 8 0 5 0 5 6 0 2 0 7 5 0 8 8 7 5 0 5 5 8 5\n",
-      " 5 4 7 7 0 7 7 6 4 4 4 6 0 5 5 8 0 0 5 7 5 8 8 0 0 4 7 9 5 2 6 4 7 5 7 0 5\n",
-      " 0 4 5 6 7 5 0 8 7 7 4 0 4 5 0 5 6 2 0 5 5 5 5 5 0 2 9 6 7 5 0 2 8 5 0 5 8\n",
-      " 5 9 7 6 9 4 0 4 7 8 5 9 7 6 9 8 5 0 7 8 5 9 7 6 9 4 0 5 7 8 7 5 5 4 5 8 7\n",
-      " 7 7 7 6 5 5 0 7 5 5 8 8 5 9 5 0 9 8 1 9 4 7 7 0 0 0 6 4 4 4 6 7 5 5 8 7 9\n",
-      " 9 5 9 8 8 5 0 4 7 5 5 0 6 4 7 5 7 7 5 5 4 5 6 7 5 6 8 5 7 4 7 4 0 5 9 6 6\n",
-      " 0 9 0 0 5 5 5 7 9 6 0 7 6 7 8 5 7 5 1 9 7 6 7 4 0 7 7 8 1 9 7 6 7 4 5 7 7\n",
-      " 8 1 9 7 6 7 4 0 7 7 8 7 7 7 4 7 8 7 7 7 5 6 1 0 0 7 7 1 9 0 7 1 8 1 1 4 7\n",
-      " 7 0 7 7 6 4 4 4 6 7 1 7 8 7 7 1 7 1 8 8 1 0 4 7 9 1 6 4 7 1 7 7 1 0 4 7 2\n",
-      " 7 1 6 8 9 7 4 7 4 1 2 7 6 2 0 1 7 1 9 7 0 7 7 2 2 7 8 7 7 7 8 5 9 7 2 5 4\n",
-      " 0 0 7 8 5 5 7 2 5 4 0 5 7 8 5 1 7 6 7 4 0 9 7 8 7 5 5 4 5 8 7 9 7 5 2 5 0\n",
-      " 0 7 7 5 8 8 0 5 1 8 5 1 4 3 7 5 7 7 6 4 4 4 0 7 5 0 8 7 9 1 7 1 8 8 5 0 1\n",
-      " 7 1 5 0 6 4 7 5 0 7 5 0 4 5 6 7 5 6 8 5 7 4 7 4 5 0 7 6 6 5 9 5 1 1 5 0 7\n",
-      " 5 6 5 7 6 7 8 9 7 9 8 5 9 7 6 7 4 0 7 7 8 0 1 7 6 7 4 0 5 7 8 5 9 7 6 9 4\n",
-      " 0 5 7 8 7 7 9 4 9 8 7 9 7 5 6 5 0 0 0 4 5 8 8 9 5 0 5 9 8 5 9 4 7 7 0 7 7\n",
-      " 6 4 4 4 6 7 5 5 8 7 7 9 9 7 8 8 5 0 4 9 9 5 5 6 4 7 5 7 7 5 5 4 5 6 7 5 6\n",
-      " 8 9 9 4 7 4 3 0 5 6 6 0 9 3 9 9 9 0 7 9 6 5 5 6 7 8 5 7 5 8 0 1 9 6 7 4 9\n",
-      " 5 7 8 5 1 7 6 9 4 0 5 7 8 0 5 7 6 5 4 0 5 7 8 7 9 5 4 5 8 7 5 7 5 6 5 0 0\n",
-      " 7 7 5 8 8 1 1 0 5 1 8 0 1 4 7 7 0 7 7 6 4 4 4 6 7 5 7 8 7 4 9 5 7 8 8 0 0\n",
-      " 4 7 1 5 5 6 4 7 5 7 7 5 0 4 5 6 7 5 6 8 5 7 4 7 4 5 5 5 6 6 0 1 5 1 1 4 0\n",
-      " 7 7 6 5 5 6 7 8 5 8 0 9 7 6 7 4 0 7 7 8 0 9 7 6 7 4 0 7 7 8 0 9 7 6 7 4 0\n",
-      " 7 7 8 7 7 7 4 7 8 7 7 7 7 6 0 0 0 7 7 0 8 8 9 9 2 0 9 8 0 9 4 7 7 0 7 7 6\n",
-      " 4 4 4 6 7 0 7 8 7 7 9 0 9 8 8 0 0 4 7 9 0 0 6 4 7 0 7 7 0 0 4 0 6 7 0 6 8\n",
-      " 7 7 4 7 4 0 0 7 6 6 0 9 0 9 9 7 0 7 7 6 0 0 6 7 8 0 7 0 8 5 1 7 6 5 4 9 5\n",
-      " 0 8 5 1 0 6 5 4 0 8 5 1 7 6 5 4 5 5 2 6 0 5 9 4 5 8 0 0 5 5 6 5 0 9 5 9 5\n",
-      " 4 8 1 9 9 5 1 8 5 9 4 5 5 9 5 5 6 4 4 4 5 0 5 5 8 7 5 1 0 8 5 5 4 7 9 5 9\n",
-      " 5 4 7 5 5 0 5 9 4 5 6 7 5 6 8 5 7 4 7 5 5 0 5 6 6 5 1 7 5 9 7 5 5 6 5 8 5\n",
-      " 0 0 8 5 9 7 6 7 4 0 7 7 8 5 5 5 6 7 4 0 9 7 8 5 5 9 6 6 4 0 5 7 8 7 5 0 4\n",
-      " 5 8 7 7 7 9 2 5 0 0 0 0 5 8 8 9 9 0 5 9 8 5 9 4 5 0 5 7 0 6 4 4 4 6 7 5 7\n",
-      " 8 7 7 9 5 9 8 8 5 6 4 0 5 5 6 6 4 7 5 7 7 5 0 4 5 6 5 5 6 8 7 7 4 7 4 5 5\n",
-      " 7 6 6 0 9 5 9 5 7 5 7 7 6 5 5 6 7 8 5 7 7]\n",
-      "------Test------\n",
-      "random   \t0.074s\t69952\t0.545\t0.616\t0.578\t0.415\t0.574\t0.117\n",
-      "------Test------\n",
-      "estimator[-1].labels_ [1 2 2 7 0 8 0 5 2 8 1 9 6 7 0 4 0 5 2 8 1 9 2 7 9 4 0 9 2 8 1 8 4 3 0 4 1\n",
-      " 8 2 8 2 0 9 5 5 7 4 9 1 1 2 2 5 2 9 1 9 2 0 7 7 5 7 7 0 0 0 0 0 2 9 4 1 8\n",
-      " 4 2 2 9 1 1 9 5 0 7 3 2 5 4 0 7 9 7 8 2 5 2 2 0 7 2 0 1 4 3 0 8 2 9 5 4 0\n",
-      " 0 5 2 2 2 2 4 5 8 4 2 2 2 0 7 1 2 8 2 1 9 3 7 9 3 0 5 2 8 1 9 3 7 0 3 0 5\n",
-      " 2 8 1 9 2 7 0 4 0 5 2 8 1 8 4 3 0 4 1 8 2 8 2 0 9 5 5 7 3 9 1 1 3 3 5 2 3\n",
-      " 1 9 2 0 7 7 5 7 7 0 0 0 0 0 8 9 4 1 8 3 2 2 2 1 1 9 5 0 7 2 9 5 7 9 7 8 9\n",
-      " 5 0 2 0 7 9 0 1 4 5 0 8 0 9 5 4 0 0 5 3 2 3 2 3 3 9 2 2 0 8 1 2 8 2 1 2 3\n",
-      " 7 0 8 0 5 8 4 1 2 3 7 0 4 0 5 2 5 1 2 3 7 0 4 0 5 2 8 1 8 4 4 0 8 1 8 2 8\n",
-      " 2 0 2 5 5 7 4 2 1 1 3 2 5 8 3 1 9 3 0 7 7 9 7 7 0 0 0 0 0 5 2 4 1 5 4 3 8\n",
-      " 3 1 1 2 5 0 7 7 2 5 0 0 7 2 7 5 2 5 0 2 0 7 2 5 1 8 7 0 5 0 2 5 4 0 0 5 3\n",
-      " 8 3 3 8 5 9 3 0 8 8 0 9 1 2 5 7 1 9 6 7 0 4 0 9 2 8 1 2 6 7 0 4 0 9 2 8 1\n",
-      " 9 3 8 0 4 0 9 8 8 1 8 4 4 0 8 1 8 8 8 2 0 9 9 5 7 4 2 1 1 6 3 9 8 3 1 6 7\n",
-      " 0 7 8 9 8 8 0 0 0 0 0 8 9 4 1 8 4 6 2 3 1 1 2 9 0 7 3 2 5 9 0 7 9 7 8 9 4\n",
-      " 0 8 0 7 2 0 1 4 7 0 8 2 9 9 4 9 0 9 3 8 3 2 4 5 8 4 9 2 2 0 8 1 8 8 8 1 6\n",
-      " 6 8 0 4 0 5 2 8 1 6 2 8 5 8 0 5 2 8 1 6 6 8 0 4 0 5 2 8 1 2 4 8 0 8 1 8 2\n",
-      " 8 2 0 6 5 5 8 4 6 1 1 6 6 5 0 6 1 6 6 0 8 8 5 8 8 0 0 0 0 0 8 6 4 1 8 8 6\n",
-      " 2 3 1 1 6 5 0 6 6 6 5 0 0 8 6 8 8 6 5 0 2 0 8 2 0 1 8 8 0 8 0 6 5 4 0 0 5\n",
-      " 6 2 3 6 4 5 8 4 0 9 2 5 8 1 2 8 2 1 2 3 7 0 4 0 5 8 9 1 2 7 8 0 4 0 5 2 9\n",
-      " 1 2 7 7 9 4 0 9 8 9 1 9 0 4 0 4 1 9 2 9 8 9 2 9 9 8 4 2 1 1 7 7 5 2 2 1 2\n",
-      " 8 0 7 7 5 7 7 9 0 0 0 0 9 2 4 1 9 4 7 8 2 1 1 2 5 0 7 3 2 9 0 0 7 2 7 9 2\n",
-      " 5 0 2 0 7 2 5 1 4 7 0 9 0 2 5 4 0 9 5 2 2 2 7 4 5 9 4 0 8 2 5 9 1 8 9 2 1\n",
-      " 2 3 7 0 4 0 5 2 8 1 2 3 7 0 4 1 5 9 8 1 2 3 7 0 4 0 5 2 8 1 8 4 4 0 4 1 8\n",
-      " 8 8 8 0 2 5 5 7 4 2 1 1 2 3 5 8 3 1 6 3 0 7 7 5 7 7 0 0 0 0 0 8 2 4 1 8 4\n",
-      " 3 2 3 1 1 2 5 0 7 2 2 5 0 0 7 2 7 8 2 5 0 2 0 7 2 0 1 4 7 0 8 0 2 5 4 0 0\n",
-      " 5 3 2 5 7 4 5 8 4 0 8 8 0 8 1 2 8 2 6 3 8 0 4 0 5 8 8 1 6 3 7 0 8 0 5 8 8\n",
-      " 1 6 3 7 9 8 0 5 7 8 1 8 8 8 0 8 1 8 8 8 2 9 6 5 5 7 8 6 3 5 8 6 1 6 6 0 7\n",
-      " 7 5 8 8 9 0 0 0 9 8 6 8 1 8 8 6 8 6 1 1 6 5 0 8 3 6 0 0 8 6 8 8 6 5 0 8 9\n",
-      " 7 6 9 1 4 7 0 8 0 6 5 8 0 9 5 6 8 6 3 8 5 8 7 9 9 8 1 8 8 8 1 2 3 7 9 4 0\n",
-      " 5 5 7 1 2 2 8 9 4 0 5 2 8 1 2 6 7 9 4 0 5 3 8 1 8 4 4 0 4 1 8 3 8 2 9 2 5\n",
-      " 5 7 4 2 1 1 5 2 6 1 2 6 0 7 7 5 7 7 9 0 0 0 5 8 2 4 1 4 4 6 7 6 1 1 2 5 6\n",
-      " 7 6 2 5 9 0 7 2 5 8 2 5 0 2 9 7 2 0 1 4 7 0 8 0 2 5 4 0 9 5 3 2 6 6 4 5 8\n",
-      " 4 0 8 7 0 8 1 3 8 3 1 2 3 7 0 4 0 5 7 8 1 9 6 7 0 4 0 5 2 8 1 2 3 7 0 3 0\n",
-      " 5 2 8 1 8 4 4 0 4 1 8 4 8 4 0 2 5 5 4 4 2 1 1 3 7 5 4 3 1 2 3 0 7 7 5 7 7\n",
-      " 0 0 0 0 0 8 2 4 1 8 4 3 4 7 1 1 2 5 0 7 7 2 5 0 0 7 2 7 8 2 5 0 2 0 7 2 0\n",
-      " 1 3 3 0 8 0 4 5 4 0 0 5 3 3 3 3 3 5 8 3 0 2 2 0 8 1 2 8 2 1 9 6 7 0 8 0 3\n",
-      " 2 8 1 2 6 7 0 4 0 5 2 8 1 9 2 7 0 4 6 5 2 8 1 8 4 4 0 4 1 8 2 8 2 0 2 5 5\n",
-      " 8 4 2 1 1 6 6 5 2 6 1 9 6 0 7 7 5 8 8 0 0 0 0 0 8 2 4 1 8 0 3 2 8 1 1 9 5\n",
-      " 0 7 6 2 5 0 6 7 2 7 8 2 5 0 2 0 8 2 0 1 4 7 0 8 0 2 5 4 0 0 5 6 2 6 6 8 5\n",
-      " 8 4 0 2 2 0 8 1 2 1 9 3 7 0 4 0 5 8 8 1 9 3 7 0 8 0 5 8 8 1 9 3 7 0 8 0 5\n",
-      " 8 8 1 8 8 4 0 8 1 8 8 8 8 0 9 5 5 7 8 9 1 1 3 3 9 9 3 1 9 3 0 7 7 5 7 7 0\n",
-      " 0 0 0 0 8 9 8 1 7 4 3 8 3 1 1 9 5 0 7 3 9 5 0 0 7 9 7 8 9 5 0 8 0 8 9 0 1\n",
-      " 8 7 0 8 0 9 5 8 0 0 5 3 8 7 3 4 5 8 4 0 9 2 0 8 1 9 8 2 1 2 6 7 0 4 0 3 2\n",
-      " 9 1 2 6 7 0 4 0 9 1 2 6 7 0 4 0 5 4 9 0 9 4 3 0 4 1 9 2 4 2 0 2 5 3 7 3 2\n",
-      " 0 1 6 3 3 2 6 1 2 3 0 2 5 3 5 2 5 0 0 0 2 9 2 4 0 8 2 6 2 1 2 5 0 7 3 2 3\n",
-      " 2 0 7 2 7 5 2 3 0 2 0 7 2 0 1 4 7 0 8 0 2 5 4 0 5 5 6 8 4 3 7 4 4 0 4 1 2\n",
-      " 5 5 1 2 3 7 0 4 0 5 7 8 1 2 2 7 0 4 0 5 3 8 1 2 3 3 0 0 0 5 2 8 1 8 4 4 0\n",
-      " 4 1 8 8 8 3 9 2 5 5 5 4 2 1 1 3 3 5 2 3 1 2 3 0 2 2 5 8 2 9 0 0 0 0 8 2 4\n",
-      " 1 8 0 3 2 3 1 1 2 5 0 7 2 2 5 0 0 7 2 7 8 2 5 0 2 0 4 2 0 1 8 7 0 8 0 2 5\n",
-      " 8 0 0 5 3 2 3 3 8 5 8 8 0 2 2 0 8 1 2 8 8]\n",
-      "------Test------\n",
-      "PCA-based\t0.030s\t72686\t0.636\t0.658\t0.647\t0.521\t0.643\t0.138\n",
-      "__________________________________________________________________________________\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.cluster import KMeans\n",
     "from sklearn.decomposition import PCA\n",
@@ -452,20 +218,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",

From ba0df386a4e25b5e957283fd796fac316cddd107 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Sat, 20 Feb 2021 22:33:20 -0800
Subject: [PATCH 03/16] v-measure for single user on above cutoff bins

---
 .../v-measurel_cutoff_bins_single_user.ipynb  | 271 ++++++++++++++++++
 1 file changed, 271 insertions(+)
 create mode 100644 tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb

diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
new file mode 100644
index 0000000..d4b5552
--- /dev/null
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "introductory-sunglasses",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "# import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "under-sample",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "genetic-drinking",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "rubber-montgomery",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thorough-nomination",
+   "metadata": {},
+   "source": [
+    "## Choose one user for experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "revolutionary-sussex",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user = all_users[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "crude-compatibility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the data from the database. We choose key='confirmed_trip' to get confirmed trips here\n",
+    "trips = pipeline.read_data(uuid=user,key=esda.CONFIRMED_TRIP_KEY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "right-graham",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select trips that have user_input to analyze\n",
+    "non_empty_trips = [t for t in trips if t[\"data\"][\"user_input\"] != {}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "infrared-accordance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(non_empty_trips), non_empty_trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "generic-glasgow",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_trips, bins = pipeline.remove_noise(non_empty_trips, radius)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "thick-commissioner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.debug('The list of bins is %s' % bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "macro-round",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show all user labels in all bins\n",
+    "for bin in bins:\n",
+    "    bin_user_input = (non_empty_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_df = pd.DataFrame(data = bin_user_input)\n",
+    "    print(bin_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tired-athens",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn all user_input into list without binning\n",
+    "bin_trips_user_input_ls = pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).values.tolist()\n",
+    "bin_trips_user_input_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "veterinary-courage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in bin_trips_user_input_ls:\n",
+    "    if i[2] == \"same_mode\":\n",
+    "        print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "coastal-fiction",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_df=pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).drop_duplicates()\n",
+    "no_dup_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "stainless-ancient",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_list = no_dup_df.values.tolist()\n",
+    "no_dup_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "narrative-reducing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true =[]\n",
+    "for trip in bin_trips_user_input_ls:\n",
+    "    if trip in no_dup_list:\n",
+    "        labels_true.append(no_dup_list.index(trip))\n",
+    "labels_true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "framed-sector",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "unlimited-waste",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "grateful-finance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "removable-rating",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true, labels_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 4a3c8a551b95d2da4549b4fcebb6244cea901e0e Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Mon, 22 Feb 2021 09:18:03 -0800
Subject: [PATCH 04/16] modified v-measurel_cutoff_bins_single_user, add one
 file for clusters for a single user

---
 .../v-measurel_cutoff_bins_single_user.ipynb  | 401 +++++++++++--
 ...measurel_cutoff_clusters_single_user.ipynb | 564 ++++++++++++++++++
 2 files changed, 930 insertions(+), 35 deletions(-)
 create mode 100644 tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb

diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
index d4b5552..6a55dd8 100644
--- a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "introductory-sunglasses",
+   "id": "severe-married",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,14 +18,15 @@
     "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
     "import pandas as pd\n",
     "from numpy import *\n",
-    "# import confirmed_trips_eval_bins_clusters as evaluation\n",
-    "from sklearn import metrics"
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "under-sample",
+   "id": "knowing-price",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "genetic-drinking",
+   "id": "graphic-determination",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "rubber-montgomery",
+   "id": "breathing-description",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,28 +66,28 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "revolutionary-sussex",
+   "id": "gorgeous-retailer",
    "metadata": {},
    "outputs": [],
    "source": [
-    "user = all_users[4]"
+    "user = all_users[0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "crude-compatibility",
+   "id": "homeless-father",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#read the data from the database. We choose key='confirmed_trip' to get confirmed trips here\n",
+    "#read the data from the database. We choose key=esda.CONFIRMED_TRIP_KEY to get confirmed trips here\n",
     "trips = pipeline.read_data(uuid=user,key=esda.CONFIRMED_TRIP_KEY)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "right-graham",
+   "id": "arranged-cleaning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "infrared-accordance",
+   "id": "super-spencer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "generic-glasgow",
+   "id": "higher-advocate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +118,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "thick-commissioner",
+   "id": "beautiful-parcel",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,8 +128,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "macro-round",
-   "metadata": {},
+   "id": "comic-norfolk",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# show all user labels in all bins\n",
@@ -138,47 +141,58 @@
     "    print(bin_df)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "expensive-forestry",
+   "metadata": {},
+   "source": [
+    "### Original output"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "tired-athens",
+   "id": "corporate-situation",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# turn all user_input into list without binning\n",
-    "bin_trips_user_input_ls = pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).values.tolist()\n",
-    "bin_trips_user_input_ls"
+    "bin_trips_df = pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in bin_trips])\n",
+    "bin_trips_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "veterinary-courage",
+   "id": "historic-russian",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for i in bin_trips_user_input_ls:\n",
-    "    if i[2] == \"same_mode\":\n",
-    "        print(i)"
+    "# turn all user_input into list without binning\n",
+    "bin_trips_user_input_ls = bin_trips_df.values.tolist()\n",
+    "bin_trips_user_input_ls"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "coastal-fiction",
-   "metadata": {},
+   "id": "jewish-bristol",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# drop duplicate user_input\n",
-    "no_dup_df=pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).drop_duplicates()\n",
+    "no_dup_df=bin_trips_df.drop_duplicates()\n",
     "no_dup_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "stainless-ancient",
-   "metadata": {},
+   "id": "wooden-postage",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# turn non-duplicate user_input into list\n",
@@ -189,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "narrative-reducing",
+   "id": "wicked-serial",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +218,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "framed-sector",
+   "id": "surgical-stadium",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,10 +230,55 @@
     "labels_pred"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "first-campbell",
+   "metadata": {},
+   "source": [
+    "Note: the trips order in labels_true and labels_pred should be the same. Using timestamp to compare the trips in bin_trips and those in bins"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "unlimited-waste",
+   "id": "dental-universal",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_trips_ts = pd.DataFrame(data=[i[\"data\"][\"start_ts\"]for i in bin_trips])\n",
+    "len(bin_trips_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dynamic-prize",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_ls =[]\n",
+    "for bin in bins:\n",
+    "    for index in bin:\n",
+    "        bin_ls.append(index)\n",
+    "bins_ts = pd.DataFrame(data=[non_empty_trips[i][\"data\"][\"start_ts\"]for i in bin_ls])\n",
+    "len(bins_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "republican-rabbit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compare two data frames, return nothing if two data frames are the same\n",
+    "assert_frame_equal(bins_ts,bin_trips_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "concerned-congo",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "grateful-finance",
+   "id": "alleged-alabama",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -239,12 +298,284 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "removable-rating",
-   "metadata": {},
+   "id": "material-genetics",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "metrics.v_measure_score(labels_true, labels_pred)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aware-friday",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "native-sight",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "span_eng_dict = {'revisado_bike':'test ride with bike','placas_de carro':'car plates','aseguranza':'insurance',\n",
+    " 'iglesia':'church','curso':'course','mi_hija recién aliviada':'my daughter just had a new baby',\n",
+    " 'servicio_comunitario':'community service','pago_de aseguranza':'insurance payment',\n",
+    " 'grupo_comunitario':'community group','caminata_comunitaria':'community walk'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "threaded-focus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use dict to replace the values in Spanish in the bin(this step just for showing the trips in each bin)\n",
+    "for bin in bins:\n",
+    "    bin_user_input = (non_empty_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_df = pd.DataFrame(data = bin_user_input)\n",
+    "    sp2en_bin_df = bin_df.replace(span_eng_dict)\n",
+    "    print(sp2en_bin_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "imported-reasoning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn all user_input into list without binning\n",
+    "bin_trips_user_input_sp2en_ls = pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in bin_trips]).replace(span_eng_dict).values.tolist()\n",
+    "bin_trips_user_input_sp2en_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "athletic-reasoning",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_sp2en_df=pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in bin_trips]).replace(span_eng_dict).drop_duplicates()\n",
+    "no_dup_sp2en_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "false-fields",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_sp2en_list = no_dup_sp2en_df.values.tolist()\n",
+    "no_dup_sp2en_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "formal-belgium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_sp2en =[]\n",
+    "for trip in bin_trips_user_input_sp2en_ls:\n",
+    "    if trip in no_dup_sp2en_list:\n",
+    "        labels_true_sp2en.append(no_dup_sp2en_list.index(trip))\n",
+    "labels_true_sp2en"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "indirect-lafayette",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cooked-mineral",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caring-calcium",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sunset-frequency",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "included-alberta",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "outer-hammer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',\n",
+    "               'insurance_payment':'insurance'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "developing-socket",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "partial-prerequisite",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert purpose\n",
+    "bin_trips_user_input_sp2en = pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).replace(span_eng_dict)\n",
+    "bin_trips_cvt_pur_df = bin_trips_user_input_sp2en.replace(map_pur_dict)\n",
+    "# convert mode\n",
+    "bin_trips_cvt_pur_mo_df = bin_trips_cvt_pur_df\n",
+    "for i in range(len(bin_trips_cvt_pur_mo_df)):\n",
+    "    if bin_trips_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] == \"same_mode\":\n",
+    "        print(bin_trips_cvt_pur_mo_df.iloc[i]) # to see which row will be converted\n",
+    "        bin_trips_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] = bin_trips_cvt_pur_mo_df.iloc[i]['mode_confirm']\n",
+    "print(bin_trips_cvt_pur_mo_df)\n",
+    "bin_trips_cvt_pur_mode_ls = bin_trips_cvt_pur_mo_df.values.tolist()\n",
+    "bin_trips_cvt_pur_mode_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "classical-berkeley",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_cvt_pur_mode_df = bin_trips_cvt_pur_mo_df.drop_duplicates()\n",
+    "no_dup_cvt_pur_mode_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "opening-equity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_cvt_pur_mo_ls = no_dup_cvt_pur_mode_df.values.tolist()\n",
+    "no_dup_cvt_pur_mo_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "occasional-evanescence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_cvt =[]\n",
+    "for trip in bin_trips_cvt_pur_mode_ls:\n",
+    "    if trip in no_dup_cvt_pur_mo_ls:\n",
+    "        labels_true_cvt.append(no_dup_cvt_pur_mo_ls.index(trip))\n",
+    "labels_true_cvt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fancy-barbados",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "placed-carry",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "worse-shift",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "immediate-series",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_cvt, labels_pred)"
+   ]
   }
  ],
  "metadata": {
diff --git a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
new file mode 100644
index 0000000..c99060e
--- /dev/null
+++ b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
@@ -0,0 +1,564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "worst-baseball",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "behavioral-generation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "powered-import",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "living-connection",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thorough-nomination",
+   "metadata": {},
+   "source": [
+    "## Choose one user for experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "necessary-letter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user = all_users[10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "exclusive-think",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the data from the database. We choose key=esda.CONFIRMED_TRIP_KEY to get confirmed trips here\n",
+    "trips = pipeline.read_data(uuid=user,key=esda.CONFIRMED_TRIP_KEY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "funded-canadian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select trips that have user_input to analyze\n",
+    "non_empty_trips = [t for t in trips if t[\"data\"][\"user_input\"] != {}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "iraqi-account",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(non_empty_trips), non_empty_trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "owned-advocate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_trips, bins = pipeline.remove_noise(non_empty_trips, radius)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "healthy-wildlife",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.debug('The list of bins is %s' % bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "promotional-batch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clusters,labels,cluster_trips, points = pipeline.cluster(bin_trips, len(bins))\n",
+    "# clustering the data only based on sil score (min_cluster = 0) instead of bins number (len(bins))\n",
+    "feat = featurization.featurization(bin_trips)\n",
+    "min = 0\n",
+    "max = int(math.ceil(1.5 * len(bins)))\n",
+    "feat.cluster(min_clusters=min, max_clusters=max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "attempted-afghanistan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.debug('number of clusters: %d' % feat.clusters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fatty-manner",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.debug('labels list is: %s' % feat.labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "internal-mobility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_trips = feat.data\n",
+    "cluster_trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alone-rochester",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_user_input_df = pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in cluster_trips])\n",
+    "cluster_user_input_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sixth-living",
+   "metadata": {},
+   "source": [
+    "### Original output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "amber-westminster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn cluster_trips to list without any changes\n",
+    "cluster_user_input_ls = cluster_user_input_df.values.tolist()\n",
+    "cluster_user_input_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "informative-donor",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_df=cluster_user_input_df.drop_duplicates()\n",
+    "no_dup_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "computational-punishment",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_list = no_dup_df.values.tolist()\n",
+    "no_dup_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "electric-contamination",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true =[]\n",
+    "for trip in cluster_user_input_ls:\n",
+    "    if trip in no_dup_list:\n",
+    "        labels_true.append(no_dup_list.index(trip))\n",
+    "labels_true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hindu-scholarship",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_pred = feat.labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ecological-wales",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "metric-budget",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "responsible-newport",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "level-semiconductor",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "distinct-airport",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "span_eng_dict = {'revisado_bike':'test ride with bike','placas_de carro':'car plates','aseguranza':'insurance',\n",
+    " 'iglesia':'church','curso':'course','mi_hija recién aliviada':'my daughter just had a new baby',\n",
+    " 'servicio_comunitario':'community service','pago_de aseguranza':'insurance payment',\n",
+    " 'grupo_comunitario':'community group','caminata_comunitaria':'community walk'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "enabling-equity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change language and turn data frame to list\n",
+    "cluster_sp2en_ls = cluster_user_input_df.replace(span_eng_dict).values.tolist()\n",
+    "cluster_sp2en_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "preliminary-spider",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_sp2en_df=cluster_user_input_df.replace(span_eng_dict).drop_duplicates()\n",
+    "no_dup_sp2en_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "saved-consensus",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_sp2en_list = no_dup_sp2en_df.values.tolist()\n",
+    "no_dup_sp2en_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "available-coaching",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_sp2en =[]\n",
+    "for trip in cluster_sp2en_ls:\n",
+    "    if trip in no_dup_sp2en_list:\n",
+    "        labels_true_sp2en.append(no_dup_sp2en_list.index(trip))\n",
+    "labels_true_sp2en"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "average-gauge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_pred = labels_pred = feat.labels\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fiscal-camping",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "color-repeat",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "whole-wrong",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dangerous-creek",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "certain-italy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',\n",
+    "               'insurance_payment':'insurance'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "swiss-initial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "naughty-vegetarian",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert purpose\n",
+    "cluster_sp2en_df = cluster_user_input_df.replace(span_eng_dict)\n",
+    "cluster_cvt_pur_df = cluster_sp2en_df.replace(map_pur_dict)\n",
+    "# convert mode\n",
+    "cluster_cvt_pur_mo_df = cluster_cvt_pur_df\n",
+    "for i in range(len(cluster_cvt_pur_mo_df)):\n",
+    "    if cluster_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] == \"same_mode\":\n",
+    "        print(cluster_cvt_pur_mo_df.iloc[i]) # to see which row will be converted\n",
+    "        cluster_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] = cluster_cvt_pur_mo_df.iloc[i]['mode_confirm']\n",
+    "print(cluster_cvt_pur_mo_df)\n",
+    "cluster_cvt_pur_mo_ls = cluster_cvt_pur_mo_df.values.tolist()\n",
+    "cluster_cvt_pur_mo_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "approximate-lightweight",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_cvt_pur_mode_df = cluster_cvt_pur_mo_df.drop_duplicates()\n",
+    "no_dup_cvt_pur_mode_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "active-westminster",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_cvt_pur_mo_ls = no_dup_cvt_pur_mode_df.values.tolist()\n",
+    "no_dup_cvt_pur_mo_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "documented-arlington",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_cvt =[]\n",
+    "for trip in cluster_cvt_pur_mo_ls:\n",
+    "    if trip in no_dup_cvt_pur_mo_ls:\n",
+    "        labels_true_cvt.append(no_dup_cvt_pur_mo_ls.index(trip))\n",
+    "labels_true_cvt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "noted-worst",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels_pred = labels_pred = feat.labels\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "thousand-palestine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "demanding-immigration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "racial-theory",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "precise-woman",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0951484174399ca7c31988a9f667461332f33e45 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Mon, 22 Feb 2021 23:50:18 -0800
Subject: [PATCH 05/16] modified evaluation code, put all bins and bins above
 cutoff evaluation in one notebook, evaluated clusters above cutoff, showed
 process of single user on all bins, bins above cutoff, and clusters above
 cutoff

---
 .../confirmed_trips_eval_bins_clusters.py     | 205 ++++++
 .../v-measurel_all_bins_single_user.ipynb     | 630 ++++++++++++++++++
 .../v-measurel_bins_all_user.ipynb            | 314 +++++++++
 ...urel_clusters_above_cutoff_all_users.ipynb | 195 ++++++
 ...measurel_cutoff_clusters_single_user.ipynb |  85 ++-
 5 files changed, 1410 insertions(+), 19 deletions(-)
 create mode 100644 tour_model_eval/v-measurel_all_bins_single_user.ipynb
 create mode 100644 tour_model_eval/v-measurel_bins_all_user.ipynb
 create mode 100644 tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index 9f58337..e57e4a7 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -9,6 +9,8 @@
 import emission.storage.decorations.analysis_timeseries_queries as esda
 import pandas as pd
 from numpy import *
+from sklearn import metrics
+from pandas.testing import assert_frame_equal
 
 # Spanish words to English
 span_eng_dict = {'revisado_bike':'test ride with bike','placas_de carro':'car plates','aseguranza':'insurance',
@@ -71,3 +73,206 @@ def precision_bin_all_users(all_users,radius,sp2en=None,cvt_purpose=None):
                 all_bins_preci = precision_bins(all_bins_preci, sim.bins, non_empty_trips, sp2en, cvt_purpose)
         all_users_preci.append(round(mean(all_bins_preci), 2))
     return all_users_preci
+
+
+# v_measure_bins takes 5 parameters
+# - sp2en=True: change Spanish to English
+# - cvt_pur_mo=True: convert purposes and replaced mode
+# - cutoff=True: choose to analyze bins above cutoff
+# - cutoff=None: analyze all bins
+# Note: for sp2en and cvt_pur_mo, set either one to be True as needed. cvt_pur_mo will change language first
+def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
+    homo_score = []
+    comp_score = []
+    v_score = []
+    for i in range(len(all_users)):
+        user = all_users[i]
+        trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
+        non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
+        valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
+                       'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
+        sim = similarity.similarity(valid_trips, radius)
+        filter_trips = sim.data
+
+        # filter out users that haven't enough trips (at least 10) to analyze
+        if len(filter_trips) < 10:
+            homo_score.append(NaN)
+            comp_score.append(NaN)
+            v_score.append(NaN)
+            continue
+        sim.bin_data()
+        if cutoff is None:
+            trip_index_ls = []
+            bins = sim.bins
+            for bin in bins:
+                for index in bin:
+                    trip_index_ls.append(index)
+            bin_trips = [filter_trips[num] for num in trip_index_ls]
+
+        elif cutoff:
+            sim.delete_bins()
+            bin_trips = sim.newdata
+            bins = sim.bins
+
+            if len(bin_trips) < 10:
+                homo_score.append(NaN)
+                comp_score.append(NaN)
+                v_score.append(NaN)
+                continue
+        bin_trips_df = pd.DataFrame(data=[trip["data"]["user_input"] for trip in bin_trips])
+
+        if sp2en:
+            bin_trips_df = bin_trips_df.replace(span_eng_dict)
+        elif cvt_pur_mo:
+            bin_trips_df = bin_trips_df.replace(span_eng_dict)
+            bin_trips_df = bin_trips_df.replace(map_pur_dict)
+            for a in range(len(bin_trips_df)):
+                if bin_trips_df.iloc[a]["replaced_mode"] == "same_mode":
+                    # to see which row will be converted
+                    logging.debug("The following rows will be changed: %s", bin_trips_df.iloc[a])
+                    bin_trips_df.iloc[a]["replaced_mode"] = bin_trips_df.iloc[a]['mode_confirm']
+
+        # turn all user_input into list without binning
+        bin_trips_user_input_ls = bin_trips_df.values.tolist()
+        # drop duplicate user_input
+        no_dup_df = bin_trips_df.drop_duplicates()
+        # turn non-duplicate user_input into list
+        no_dup_list = no_dup_df.values.tolist()
+
+        # collect labels_true based on user_input
+        labels_true = []
+        for trip in bin_trips_user_input_ls:
+            if trip in no_dup_list:
+                labels_true.append(no_dup_list.index(trip))
+
+        # collect labels_pred based on bins
+        labels_pred = []
+        for b in range(len(bins)):
+            for trip in bins[b]:
+                labels_pred.append(b)
+
+        # compare the trips order in bins and those in valid_trips using timestamp
+        bin_trips_ts = pd.DataFrame(data=[trip["data"]["start_ts"] for trip in bin_trips])
+        bin_ls = []
+        for bin in bins:
+            for index in bin:
+                bin_ls.append(index)
+        bins_ts = pd.DataFrame(data=[filter_trips[i]["data"]["start_ts"] for i in bin_ls])
+        # compare two data frames, return nothing if two data frames are the same
+        assert_frame_equal(bins_ts, bin_trips_ts)
+        homo = metrics.homogeneity_score(labels_true, labels_pred)
+        homo_score.append(float('%.3f' % homo))
+        comp = metrics.completeness_score(labels_true, labels_pred)
+        comp_score.append(float('%.3f' % comp))
+        v = metrics.v_measure_score(labels_true, labels_pred)
+        v_score.append(float('%.3f' % v))
+
+    return homo_score, comp_score, v_score
+
+
+# - sp2en=True: change Spanish to English
+# - cvt_pur_mo=True: convert purposes and replaced mode
+# - cutoff=True: choose to analyze bins above cutoff
+# - cutoff=None: analyze all bins
+# Note: for sp2en and cvt_pur_mo, set either one to be True as needed. cvt_pur_mo will change language first
+def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
+    homo_score = []
+    comp_score = []
+    v_score = []
+    for i in range(len(all_users)):
+        user = all_users[i]
+        trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
+        non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
+        valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
+                       'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
+        sim = similarity.similarity(valid_trips, radius)
+        filter_trips = sim.data
+        # filter out users that haven't enough trips (at least 10) to analyze
+        if len(filter_trips) < 10:
+            homo_score.append(NaN)
+            comp_score.append(NaN)
+            v_score.append(NaN)
+            continue
+        sim.bin_data()
+        sim.delete_bins()
+        bin_trips = sim.newdata
+        bins = sim.bins
+
+        if len(bin_trips) < 10:
+            homo_score.append(NaN)
+            comp_score.append(NaN)
+            v_score.append(NaN)
+            continue
+
+        # clustering the data only based on sil score (min_cluster = 0) instead of bins number (len(bins))
+        feat = featurization.featurization(bin_trips)
+        min = 0
+        max = int(math.ceil(1.5 * len(bins)))
+        feat.cluster(min_clusters=min, max_clusters=max)
+        cluster_trips = feat.data
+        cluster_user_input_df = pd.DataFrame(data=[i["data"]["user_input"] for i in cluster_trips])
+        if sp2en:
+            # change language
+            cluster_user_input_df = cluster_user_input_df.replace(span_eng_dict)
+            cluster_user_input_ls = cluster_user_input_df.values.tolist()
+        elif cvt_pur_mo:
+            # change language first
+            cluster_user_input_df = cluster_user_input_df.replace(span_eng_dict)
+            # convert purpose
+            cluster_user_input_df = cluster_user_input_df.replace(map_pur_dict)
+            # convert mode
+            for a in range(len(cluster_user_input_df)):
+                if cluster_user_input_df.iloc[a]["replaced_mode"] == "same_mode":
+                    cluster_user_input_df.iloc[a]["replaced_mode"] = cluster_user_input_df.iloc[a]['mode_confirm']
+        # turn cluster_trips to list without any changes
+        cluster_user_input_ls = cluster_user_input_df.values.tolist()
+        # drop duplicate user_input
+        no_dup_df = cluster_user_input_df.drop_duplicates()
+        # turn non-duplicate user_input into list
+        no_dup_list = no_dup_df.values.tolist()
+        # collect labels_true based on user_input
+        labels_true = []
+        for trip in cluster_user_input_ls:
+            if trip in no_dup_list:
+                labels_true.append(no_dup_list.index(trip))
+        labels_pred = feat.labels
+
+        # compare the points in cluster_trips and those in feat.points, return nothing if two data frames are the same
+        cluster_ps = []
+        for trip in cluster_trips:
+            cluster_ps.append([trip["data"]["start_loc"]["coordinates"][0],
+                               trip["data"]["start_loc"]["coordinates"][1],
+                               trip["data"]["end_loc"]["coordinates"][0],
+                               trip["data"]["end_loc"]["coordinates"][1]])
+        cluster_ps_df = pd.DataFrame(data=cluster_ps)
+        label_ps_df = pd.DataFrame(data=feat.points)
+        assert_frame_equal(cluster_ps_df, label_ps_df)
+
+        homo = metrics.homogeneity_score(labels_true, labels_pred)
+        homo_score.append(float('%.3f' % homo))
+        comp = metrics.completeness_score(labels_true, labels_pred)
+        comp_score.append(float('%.3f' % comp))
+        v = metrics.v_measure_score(labels_true, labels_pred)
+        v_score.append(float('%.3f' % v))
+
+    return homo_score, comp_score, v_score
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/tour_model_eval/v-measurel_all_bins_single_user.ipynb b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
new file mode 100644
index 0000000..4eb2013
--- /dev/null
+++ b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
@@ -0,0 +1,630 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "modified-diary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "together-twenty",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.DEBUG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sorted-lloyd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "missing-psychology",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "thorough-nomination",
+   "metadata": {},
+   "source": [
+    "## Choose one user for experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "august-valley",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user = all_users[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "diverse-running",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#read the data from the database. We choose key=esda.CONFIRMED_TRIP_KEY to get confirmed trips here\n",
+    "trips = pipeline.read_data(uuid=user,key=esda.CONFIRMED_TRIP_KEY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "crude-postage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select trips that have user_input to analyze\n",
+    "non_empty_trips = [t for t in trips if t[\"data\"][\"user_input\"] != {}]\n",
+    "len(non_empty_trips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "copyrighted-delta",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
+    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
+    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
+    "len(valid_trips),valid_trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "competent-australia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = similarity.similarity(valid_trips, radius)\n",
+    "sim.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "standing-serial",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.bin_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "annoying-antique",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_trips = sim.data\n",
+    "trip_index_ls = []\n",
+    "bins = sim.bins\n",
+    "for bin in bins:\n",
+    "    for index in bin:\n",
+    "        trip_index_ls.append(index)\n",
+    "bin_trips = [filter_trips[num]for num in trip_index_ls]\n",
+    "\n",
+    "print(len(bin_trips),len(bins))\n",
+    "bin_trips"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "latest-contemporary",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logging.debug('The list of bins is %s' % bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "thrown-hello",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# show all user labels in all bins\n",
+    "for bin in bins:\n",
+    "    bin_user_input = (filter_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_df = pd.DataFrame(data = bin_user_input)\n",
+    "    print(bin_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strange-inventory",
+   "metadata": {},
+   "source": [
+    "### Original output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "streaming-taiwan",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_trips_df = pd.DataFrame(data=[trip[\"data\"][\"user_input\"] for trip in bin_trips])\n",
+    "bin_trips_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "destroyed-filter",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn all user_input into list without binning\n",
+    "bin_trips_user_input_ls = bin_trips_df.values.tolist()\n",
+    "bin_trips_user_input_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "mature-italy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_df=bin_trips_df.drop_duplicates()\n",
+    "no_dup_df,len(no_dup_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "illegal-collect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_list = no_dup_df.values.tolist()\n",
+    "no_dup_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "sapphire-hughes",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', 300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fuzzy-marathon",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true =[]\n",
+    "for trip in bin_trips_user_input_ls:\n",
+    "    if trip in no_dup_list:\n",
+    "        labels_true.append(no_dup_list.index(trip))\n",
+    "labels_true"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "considerable-restoration",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "domestic-august",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bin_trips_ts = pd.DataFrame(data=[trip[\"data\"][\"start_ts\"]for trip in bin_trips])\n",
+    "bin_input = pd.DataFrame(data=[trip[\"data\"][\"user_input\"]for trip in bin_trips])\n",
+    "len(bin_trips_ts)\n",
+    "bin_input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "married-catholic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bins_ts = pd.DataFrame(data=[filter_trips[num][\"data\"][\"start_ts\"]for num in trip_index_ls])\n",
+    "bins_input = pd.DataFrame(data=[filter_trips[num][\"data\"][\"user_input\"]for num in trip_index_ls])\n",
+    "len(trip_index_ls)\n",
+    "bins_input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "requested-organic",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compare two data frames, return nothing if two data frames are the same\n",
+    "assert_frame_equal(bins_ts,bin_trips_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "economic-nitrogen",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "postal-trademark",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "southern-evidence",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "domestic-regression",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "amazing-jersey",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "span_eng_dict = {'revisado_bike':'test ride with bike','placas_de carro':'car plates','aseguranza':'insurance',\n",
+    " 'iglesia':'church','curso':'course','mi_hija recién aliviada':'my daughter just had a new baby',\n",
+    " 'servicio_comunitario':'community service','pago_de aseguranza':'insurance payment',\n",
+    " 'grupo_comunitario':'community group','caminata_comunitaria':'community walk'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "impressive-philadelphia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use dict to replace the values in Spanish in the bin(this step just for showing the trips in each bin)\n",
+    "for bin in bins:\n",
+    "    bin_user_input = (valid_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_df = pd.DataFrame(data = bin_user_input)\n",
+    "    sp2en_bin_df = bin_df.replace(span_eng_dict)\n",
+    "    print(sp2en_bin_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hybrid-eugene",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn all user_input into list without binning\n",
+    "bin_trips_sp2en_df = bin_trips_df.replace(span_eng_dict)\n",
+    "bin_trips_sp2en_ls = bin_trips_sp2en_df.values.tolist()\n",
+    "bin_trips_sp2en_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "viral-intermediate",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_sp2en_df=bin_trips_sp2en_df.drop_duplicates()\n",
+    "no_dup_sp2en_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "conceptual-branch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_sp2en_list = no_dup_sp2en_df.values.tolist()\n",
+    "no_dup_sp2en_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "wanted-management",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_sp2en =[]\n",
+    "for trip in bin_trips_sp2en_ls:\n",
+    "    if trip in no_dup_sp2en_list:\n",
+    "        labels_true_sp2en.append(no_dup_sp2en_list.index(trip))\n",
+    "labels_true_sp2en"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "otherwise-vault",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "adolescent-style",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "signal-backing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "upper-contribution",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_sp2en, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bearing-communications",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "completed-testing",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',\n",
+    "               'insurance_payment':'insurance'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "quality-board",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', 200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "falling-arbor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert purpose\n",
+    "bin_trips_cvt_pur_df = bin_trips_sp2en_df.replace(map_pur_dict)\n",
+    "# convert mode\n",
+    "bin_trips_cvt_pur_mo_df = bin_trips_cvt_pur_df\n",
+    "for i in range(len(bin_trips_cvt_pur_mo_df)):\n",
+    "    if bin_trips_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] == \"same_mode\":\n",
+    "        print(bin_trips_cvt_pur_mo_df.iloc[i]) # to see which row will be converted\n",
+    "        bin_trips_cvt_pur_mo_df.iloc[i][\"replaced_mode\"] = bin_trips_cvt_pur_mo_df.iloc[i]['mode_confirm']\n",
+    "print(bin_trips_cvt_pur_mo_df)\n",
+    "bin_trips_cvt_pur_mode_ls = bin_trips_cvt_pur_mo_df.values.tolist()\n",
+    "bin_trips_cvt_pur_mode_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "electrical-liechtenstein",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# drop duplicate user_input\n",
+    "no_dup_cvt_pur_mode_df = bin_trips_cvt_pur_mo_df.drop_duplicates()\n",
+    "no_dup_cvt_pur_mode_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cleared-valley",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# turn non-duplicate user_input into list\n",
+    "no_dup_cvt_pur_mo_ls = no_dup_cvt_pur_mode_df.values.tolist()\n",
+    "no_dup_cvt_pur_mo_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "printable-fusion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_true based on user_input\n",
+    "labels_true_cvt =[]\n",
+    "for trip in bin_trips_cvt_pur_mode_ls:\n",
+    "    if trip in no_dup_cvt_pur_mo_ls:\n",
+    "        labels_true_cvt.append(no_dup_cvt_pur_mo_ls.index(trip))\n",
+    "labels_true_cvt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hundred-zealand",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# collect labels_pred based on bins\n",
+    "labels_pred = []\n",
+    "for i in range(len(bins)):\n",
+    "    for trip in bins[i]:\n",
+    "        labels_pred.append(i)\n",
+    "labels_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "false-lithuania",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.homogeneity_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "documentary-power",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.completeness_score(labels_true_cvt, labels_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "elect-animation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metrics.v_measure_score(labels_true_cvt, labels_pred)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
new file mode 100644
index 0000000..12a2d49
--- /dev/null
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "computational-national",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "preceding-yugoslavia",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "perceived-impossible",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "smooth-variety",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('max_colwidth',100)\n",
+    "pd.set_option('display.max_rows', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "medical-biodiversity",
+   "metadata": {},
+   "source": [
+    "## Bins above cutoff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "corporate-heather",
+   "metadata": {},
+   "source": [
+    "### Original user input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "chief-fundamentals",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_score_ori, comp_score_ori, v_score_ori = evaluation.v_measure_bins(all_users,radius,cutoff=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "educated-philippines",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_ori=round(mean([x for x in v_score_ori if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "welcome-homeless",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "preceding-money",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "homo_score_sp2en, comp_score_sp2en, v_score_sp2en = evaluation.v_measure_bins(all_users,radius,sp2en=True,cutoff=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "laden-plate",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_sp2en=round(mean([x for x in v_score_sp2en if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sticky-denver",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and replaced mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "agreed-moment",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True,cutoff=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "handmade-dairy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_cvt=round(mean([x for x in v_score_cvt if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "funky-potato",
+   "metadata": {},
+   "source": [
+    "### DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "technological-reservation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff_df = pd.DataFrame(data={'homogeneity_score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
+    "                        'completeness_score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
+    "                       'v_measure_score':[v_score_ori,v_score_sp2en,v_score_cvt],\n",
+    "                        'mean v_measure_score':[mean_v_ori,mean_v_sp2en,mean_v_cvt]},\n",
+    "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
+    "cutoff_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strange-badge",
+   "metadata": {},
+   "source": [
+    "## All bins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rural-virtue",
+   "metadata": {},
+   "source": [
+    "### Original user input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "listed-entrance",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_homo_score_ori, ab_comp_score_ori, ab_v_score_ori = evaluation.v_measure_bins(all_users,radius)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "known-calculation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_mean_v_ori=round(mean([x for x in ab_v_score_ori if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "broadband-expression",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alone-authorization",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_homo_score_sp2en, ab_comp_score_sp2en, ab_v_score_sp2en = evaluation.v_measure_bins(all_users,radius,sp2en=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "viral-rebel",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_mean_v_sp2en=round(mean([x for x in ab_v_score_sp2en if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "infectious-armstrong",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and replaced mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "supreme-plain",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_homo_score_cvt, ab_comp_score_cvt, ab_v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "corresponding-blind",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_mean_v_cvt=round(mean([x for x in ab_v_score_cvt if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aquatic-password",
+   "metadata": {},
+   "source": [
+    "### DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "instant-broadcast",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_df = pd.DataFrame(data={'homogeneity_score':[ab_homo_score_ori,ab_homo_score_sp2en,ab_homo_score_cvt],\n",
+    "                        'completeness_score':[ab_comp_score_ori,ab_comp_score_sp2en,ab_comp_score_cvt],\n",
+    "                       'v_measure_score':[ab_v_score_ori,ab_v_score_sp2en,ab_v_score_cvt],\n",
+    "                        'mean v_measure_score':[ab_mean_v_ori,ab_mean_v_sp2en,ab_mean_v_cvt]},\n",
+    "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
+    "all_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
new file mode 100644
index 0000000..f653b5a
--- /dev/null
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "important-humanity",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "physical-improvement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "figured-buddy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "equal-release",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('max_colwidth',100)\n",
+    "pd.set_option('display.max_rows', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "standard-savannah",
+   "metadata": {},
+   "source": [
+    "## Evaluate clusters above cutoff based on silhouette_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "formed-guitar",
+   "metadata": {},
+   "source": [
+    "### Original user input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "official-victor",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_score_ori, comp_score_ori, v_score_ori = evaluation.v_measure_clusters(all_users,radius)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "loved-isolation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_ori=round(mean([x for x in v_score_ori if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "demanding-rendering",
+   "metadata": {},
+   "source": [
+    "### After changing language"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "satisfied-enough",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_score_sp2en, comp_score_sp2en, v_score_sp2en = evaluation.v_measure_clusters(all_users,radius,sp2en=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hundred-pasta",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_sp2en=round(mean([x for x in v_score_sp2en if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hungarian-wallpaper",
+   "metadata": {},
+   "source": [
+    "### After converting purposes and replaced mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "particular-scope",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_clusters(all_users,radius,cvt_pur_mo=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "neither-shooting",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_v_cvt=round(mean([x for x in v_score_cvt if str(x) != 'nan']),3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "continuing-absorption",
+   "metadata": {},
+   "source": [
+    "### DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "retained-citizenship",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data={'homogeneity_score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
+    "                        'completeness_score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
+    "                       'v_measure_score':[v_score_ori,v_score_sp2en,v_score_cvt],\n",
+    "                        'mean v_measure_score':[mean_v_ori,mean_v_sp2en,mean_v_cvt]},\n",
+    "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
index c99060e..e6fe2e0 100644
--- a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
@@ -19,7 +19,8 @@
     "import pandas as pd\n",
     "from numpy import *\n",
     "import confirmed_trips_eval_bins_clusters as evaluation\n",
-    "from sklearn import metrics"
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
    ]
   },
   {
@@ -69,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "user = all_users[10]"
+    "user = all_users[6]"
    ]
   },
   {
@@ -91,17 +92,21 @@
    "outputs": [],
    "source": [
     "# select trips that have user_input to analyze\n",
-    "non_empty_trips = [t for t in trips if t[\"data\"][\"user_input\"] != {}]"
+    "non_empty_trips = [t for t in trips if t[\"data\"][\"user_input\"] != {}]\n",
+    "len(non_empty_trips)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "iraqi-account",
+   "id": "fabulous-definition",
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(non_empty_trips), non_empty_trips"
+    "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
+    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
+    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
+    "len(valid_trips)"
    ]
   },
   {
@@ -111,7 +116,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "bin_trips, bins = pipeline.remove_noise(non_empty_trips, radius)"
+    "bin_trips, bins = pipeline.remove_noise(valid_trips, radius)"
    ]
   },
   {
@@ -127,11 +132,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "promotional-batch",
+   "id": "valid-morocco",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# clusters,labels,cluster_trips, points = pipeline.cluster(bin_trips, len(bins))\n",
     "# clustering the data only based on sil score (min_cluster = 0) instead of bins number (len(bins))\n",
     "feat = featurization.featurization(bin_trips)\n",
     "min = 0\n",
@@ -142,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "attempted-afghanistan",
+   "id": "bridal-breakfast",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fatty-manner",
+   "id": "following-heating",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,8 +166,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "internal-mobility",
-   "metadata": {},
+   "id": "beginning-crowd",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "cluster_trips = feat.data\n",
@@ -173,8 +179,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alone-rochester",
-   "metadata": {},
+   "id": "incorporated-strengthening",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "cluster_user_input_df = pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in cluster_trips])\n",
@@ -212,7 +220,7 @@
    "source": [
     "# drop duplicate user_input\n",
     "no_dup_df=cluster_user_input_df.drop_duplicates()\n",
-    "no_dup_df"
+    "no_dup_df,len(no_dup_df)"
    ]
   },
   {
@@ -254,6 +262,45 @@
     "labels_pred = feat.labels"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fallen-tulsa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_ps=[]\n",
+    "for trip in cluster_trips:\n",
+    "    cluster_ps.append([trip[\"data\"][\"start_loc\"][\"coordinates\"][0],\n",
+    "                       trip[\"data\"][\"start_loc\"][\"coordinates\"][1],\n",
+    "                       trip[\"data\"][\"end_loc\"][\"coordinates\"][0],\n",
+    "                       trip[\"data\"][\"end_loc\"][\"coordinates\"][1]])\n",
+    "cluster_ps_df = pd.DataFrame(data=cluster_ps)\n",
+    "cluster_ps_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dangerous-discussion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "label_ps_df = pd.DataFrame(data=feat.points)\n",
+    "label_ps_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "progressive-profit",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compare two data frames, return nothing if two data frames are the same\n",
+    "assert_frame_equal(cluster_ps_df,label_ps_df)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -315,7 +362,8 @@
    "outputs": [],
    "source": [
     "# change language and turn data frame to list\n",
-    "cluster_sp2en_ls = cluster_user_input_df.replace(span_eng_dict).values.tolist()\n",
+    "cluster_sp2en_df = cluster_user_input_df.replace(span_eng_dict)\n",
+    "cluster_sp2en_ls = cluster_sp2en_df.values.tolist()\n",
     "cluster_sp2en_ls"
    ]
   },
@@ -327,7 +375,7 @@
    "outputs": [],
    "source": [
     "# drop duplicate user_input\n",
-    "no_dup_sp2en_df=cluster_user_input_df.replace(span_eng_dict).drop_duplicates()\n",
+    "no_dup_sp2en_df=cluster_sp2en_df.drop_duplicates()\n",
     "no_dup_sp2en_df"
    ]
   },
@@ -436,7 +484,6 @@
    "outputs": [],
    "source": [
     "# convert purpose\n",
-    "cluster_sp2en_df = cluster_user_input_df.replace(span_eng_dict)\n",
     "cluster_cvt_pur_df = cluster_sp2en_df.replace(map_pur_dict)\n",
     "# convert mode\n",
     "cluster_cvt_pur_mo_df = cluster_cvt_pur_df\n",
@@ -534,7 +581,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "precise-woman",
+   "id": "common-manhattan",
    "metadata": {},
    "outputs": [],
    "source": []

From 4b99639aba91bce46face2c94616bf4cd61b763a Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Tue, 23 Feb 2021 15:03:42 -0800
Subject: [PATCH 06/16] delete example codes from sklearn, clean up files

---
 .../confirmed_trips_eval_bins_clusters.py     |  52 ----
 .../plot_document_clustering.ipynb            | 285 -----------------
 tour_model_eval/plot_kmeans_digits.ipynb      | 286 ------------------
 .../v-measurel_cutoff_bins_single_user.ipynb  | 154 ++++++----
 4 files changed, 89 insertions(+), 688 deletions(-)
 delete mode 100644 tour_model_eval/plot_document_clustering.ipynb
 delete mode 100644 tour_model_eval/plot_kmeans_digits.ipynb

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index e57e4a7..1bcc370 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -22,58 +22,6 @@
 map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',
                'insurance_payment':'insurance'}
 
-# precision_bins takes five parameters
-# - all_bins_preci: the list that collects precision of each bin, should pass in an empty list
-# - sp2en=None means no need to translate language
-#   sp2en='True' will use span_eng_dict to change Spanish to English
-#
-# - cvt_purpose=None means no need to convert purposes
-#   cvt_purpose='True' will use map_pur_dict to convert purposes
-#   using this parameter should also set sp2en='True'
-def precision_bins (all_bins_preci,bins,non_empty_trips,sp2en=None,cvt_purpose=None):
-    for bin in bins:
-        bin_user_input = (non_empty_trips[i].data["user_input"] for i in bin if
-                          non_empty_trips[i].data["user_input"] != {})
-        bin_df = pd.DataFrame(data=bin_user_input)
-        if sp2en == 'True':
-            bin_df = bin_df.replace(span_eng_dict)
-        if cvt_purpose == 'True':
-            bin_df = bin_df.replace(map_pur_dict)
-        duplic_trips = bin_df[bin_df.duplicated(keep=False)]
-
-        # for bin that doesn't have duplicate trips, assign 0 as precision
-        if duplic_trips.empty and len(bin_df) > 1:
-            all_bins_preci.append(0)
-        # for bin only has one trip, assign 1.0 as precision
-        elif len(bin_df) == 1:
-            all_bins_preci.append(1.0)
-        else:
-            duplic = duplic_trips.groupby(duplic_trips.columns.tolist()).apply(lambda x: tuple(x.index)).tolist()
-            max_duplic = max(duplic, key=lambda i: len(i))
-            precision = round(len(max_duplic) / len(bin), 2)
-            all_bins_preci.append(precision)
-    return all_bins_preci
-
-
-# precision_all_users takes four parameters
-# - all_users: pass in all participants' data
-# - sp2en: default None, no need to change language
-# - cvt_purpose: default None, no need to convert purpose
-def precision_bin_all_users(all_users,radius,sp2en=None,cvt_purpose=None):
-    all_users_preci = []
-    for i in range(len(all_users)):
-        user = all_users[i]
-        trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
-        all_bins_preci = []
-        non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
-        if non_empty_trips != {}:
-            sim = similarity.similarity(non_empty_trips, radius)
-            if sim.data:
-                sim.bin_data()
-                all_bins_preci = precision_bins(all_bins_preci, sim.bins, non_empty_trips, sp2en, cvt_purpose)
-        all_users_preci.append(round(mean(all_bins_preci), 2))
-    return all_users_preci
-
 
 # v_measure_bins takes 5 parameters
 # - sp2en=True: change Spanish to English
diff --git a/tour_model_eval/plot_document_clustering.ipynb b/tour_model_eval/plot_document_clustering.ipynb
deleted file mode 100644
index 828ce5a..0000000
--- a/tour_model_eval/plot_document_clustering.ipynb
+++ /dev/null
@@ -1,285 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Clustering text documents using k-means\n",
-    "\n",
-    "This is an example showing how the scikit-learn can be used to cluster\n",
-    "documents by topics using a bag-of-words approach. This example uses\n",
-    "a scipy.sparse matrix to store the features instead of standard numpy arrays.\n",
-    "\n",
-    "Two feature extraction methods can be used in this example:\n",
-    "\n",
-    "  - TfidfVectorizer uses a in-memory vocabulary (a python dict) to map the most\n",
-    "    frequent words to features indices and hence compute a word occurrence\n",
-    "    frequency (sparse) matrix. The word frequencies are then reweighted using\n",
-    "    the Inverse Document Frequency (IDF) vector collected feature-wise over\n",
-    "    the corpus.\n",
-    "\n",
-    "  - HashingVectorizer hashes word occurrences to a fixed dimensional space,\n",
-    "    possibly with collisions. The word count vectors are then normalized to\n",
-    "    each have l2-norm equal to one (projected to the euclidean unit-ball) which\n",
-    "    seems to be important for k-means to work in high dimensional space.\n",
-    "\n",
-    "    HashingVectorizer does not provide IDF weighting as this is a stateless\n",
-    "    model (the fit method does nothing). When IDF weighting is needed it can\n",
-    "    be added by pipelining its output to a TfidfTransformer instance.\n",
-    "\n",
-    "Two algorithms are demoed: ordinary k-means and its more scalable cousin\n",
-    "minibatch k-means.\n",
-    "\n",
-    "Additionally, latent semantic analysis can also be used to reduce\n",
-    "dimensionality and discover latent patterns in the data.\n",
-    "\n",
-    "It can be noted that k-means (and minibatch k-means) are very sensitive to\n",
-    "feature scaling and that in this case the IDF weighting helps improve the\n",
-    "quality of the clustering by quite a lot as measured against the \"ground truth\"\n",
-    "provided by the class label assignments of the 20 newsgroups dataset.\n",
-    "\n",
-    "This improvement is not visible in the Silhouette Coefficient which is small\n",
-    "for both as this measure seem to suffer from the phenomenon called\n",
-    "\"Concentration of Measure\" or \"Curse of Dimensionality\" for high dimensional\n",
-    "datasets such as text data. Other measures such as V-measure and Adjusted Rand\n",
-    "Index are information theoretic based evaluation scores: as they are only based\n",
-    "on cluster assignments rather than distances, hence not affected by the curse\n",
-    "of dimensionality.\n",
-    "\n",
-    "Note: as k-means is optimizing a non-convex objective function, it will likely\n",
-    "end up in a local optimum. Several runs with independent random init might be\n",
-    "necessary to get a good convergence.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n",
-    "#         Lars Buitinck\n",
-    "# License: BSD 3 clause\n",
-    "from sklearn.datasets import fetch_20newsgroups\n",
-    "from sklearn.decomposition import TruncatedSVD\n",
-    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
-    "from sklearn.feature_extraction.text import HashingVectorizer\n",
-    "from sklearn.feature_extraction.text import TfidfTransformer\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import Normalizer\n",
-    "from sklearn import metrics\n",
-    "\n",
-    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
-    "\n",
-    "import logging\n",
-    "from optparse import OptionParser\n",
-    "import sys\n",
-    "from time import time\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "# Display progress logs on stdout\n",
-    "logging.basicConfig(level=logging.INFO,\n",
-    "                    format='%(asctime)s %(levelname)s %(message)s')\n",
-    "\n",
-    "# parse commandline arguments\n",
-    "op = OptionParser()\n",
-    "op.add_option(\"--lsa\",\n",
-    "              dest=\"n_components\", type=\"int\",\n",
-    "              help=\"Preprocess documents with latent semantic analysis.\")\n",
-    "op.add_option(\"--no-minibatch\",\n",
-    "              action=\"store_false\", dest=\"minibatch\", default=True,\n",
-    "              help=\"Use ordinary k-means algorithm (in batch mode).\")\n",
-    "op.add_option(\"--no-idf\",\n",
-    "              action=\"store_false\", dest=\"use_idf\", default=True,\n",
-    "              help=\"Disable Inverse Document Frequency feature weighting.\")\n",
-    "op.add_option(\"--use-hashing\",\n",
-    "              action=\"store_true\", default=False,\n",
-    "              help=\"Use a hashing feature vectorizer\")\n",
-    "op.add_option(\"--n-features\", type=int, default=10000,\n",
-    "              help=\"Maximum number of features (dimensions)\"\n",
-    "                   \" to extract from text.\")\n",
-    "op.add_option(\"--verbose\",\n",
-    "              action=\"store_true\", dest=\"verbose\", default=False,\n",
-    "              help=\"Print progress reports inside k-means algorithm.\")\n",
-    "\n",
-    "print(__doc__)\n",
-    "op.print_help()\n",
-    "\n",
-    "\n",
-    "def is_interactive():\n",
-    "    return not hasattr(sys.modules['__main__'], '__file__')\n",
-    "\n",
-    "\n",
-    "# work-around for Jupyter notebook and IPython console\n",
-    "argv = [] if is_interactive() else sys.argv[1:]\n",
-    "(opts, args) = op.parse_args(argv)\n",
-    "if len(args) > 0:\n",
-    "    op.error(\"this script takes no arguments.\")\n",
-    "    sys.exit(1)\n",
-    "\n",
-    "\n",
-    "# #############################################################################\n",
-    "# Load some categories from the training set\n",
-    "categories = [\n",
-    "    'alt.atheism',\n",
-    "    'talk.religion.misc',\n",
-    "    'comp.graphics',\n",
-    "    'sci.space',\n",
-    "]\n",
-    "# Uncomment the following to do the analysis on all the categories\n",
-    "# categories = None\n",
-    "\n",
-    "print(\"Loading 20 newsgroups dataset for categories:\")\n",
-    "print(categories)\n",
-    "\n",
-    "dataset = fetch_20newsgroups(subset='all', categories=categories,\n",
-    "                             shuffle=True, random_state=42)\n",
-    "\n",
-    "print(\"%d documents\" % len(dataset.data))\n",
-    "print(\"%d categories\" % len(dataset.target_names))\n",
-    "print()\n",
-    "\n",
-    "labels = dataset.target\n",
-    "true_k = np.unique(labels).shape[0]\n",
-    "\n",
-    "print(\"Extracting features from the training dataset \"\n",
-    "      \"using a sparse vectorizer\")\n",
-    "t0 = time()\n",
-    "if opts.use_hashing:\n",
-    "    if opts.use_idf:\n",
-    "        # Perform an IDF normalization on the output of HashingVectorizer\n",
-    "        hasher = HashingVectorizer(n_features=opts.n_features,\n",
-    "                                   stop_words='english', alternate_sign=False,\n",
-    "                                   norm=None)\n",
-    "        vectorizer = make_pipeline(hasher, TfidfTransformer())\n",
-    "    else:\n",
-    "        vectorizer = HashingVectorizer(n_features=opts.n_features,\n",
-    "                                       stop_words='english',\n",
-    "                                       alternate_sign=False, norm='l2')\n",
-    "else:\n",
-    "    vectorizer = TfidfVectorizer(max_df=0.5, max_features=opts.n_features,\n",
-    "                                 min_df=2, stop_words='english',\n",
-    "                                 use_idf=opts.use_idf)\n",
-    "X = vectorizer.fit_transform(dataset.data)\n",
-    "\n",
-    "print(\"done in %fs\" % (time() - t0))\n",
-    "print(\"n_samples: %d, n_features: %d\" % X.shape)\n",
-    "print()\n",
-    "\n",
-    "if opts.n_components:\n",
-    "    print(\"Performing dimensionality reduction using LSA\")\n",
-    "    t0 = time()\n",
-    "    # Vectorizer results are normalized, which makes KMeans behave as\n",
-    "    # spherical k-means for better results. Since LSA/SVD results are\n",
-    "    # not normalized, we have to redo the normalization.\n",
-    "    svd = TruncatedSVD(opts.n_components)\n",
-    "    normalizer = Normalizer(copy=False)\n",
-    "    lsa = make_pipeline(svd, normalizer)\n",
-    "\n",
-    "    X = lsa.fit_transform(X)\n",
-    "\n",
-    "    print(\"done in %fs\" % (time() - t0))\n",
-    "\n",
-    "    explained_variance = svd.explained_variance_ratio_.sum()\n",
-    "    print(\"Explained variance of the SVD step: {}%\".format(\n",
-    "        int(explained_variance * 100)))\n",
-    "\n",
-    "    print()\n",
-    "\n",
-    "\n",
-    "# #############################################################################\n",
-    "# Do the actual clustering\n",
-    "\n",
-    "if opts.minibatch:\n",
-    "    km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1,\n",
-    "                         init_size=1000, batch_size=1000, verbose=opts.verbose)\n",
-    "else:\n",
-    "    km = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1,\n",
-    "                verbose=opts.verbose)\n",
-    "\n",
-    "print(\"Clustering sparse data with %s\" % km)\n",
-    "t0 = time()\n",
-    "km.fit(X)\n",
-    "print(\"done in %0.3fs\" % (time() - t0))\n",
-    "print()\n",
-    "\n",
-    "# Test what labels are\n",
-    "np.set_printoptions(threshold=np.inf)\n",
-    "print(\"----------Test---------------\")\n",
-    "print('labels is %s' % labels)\n",
-    "print('The type of labels is %s' % type(labels))\n",
-    "print('the shape of labels is n_samples %s' % labels.shape)\n",
-    "print('km.labels_ is %s' % km.labels_)\n",
-    "print('The type of km.labels_ is %s' % type(km.labels_))\n",
-    "print('the shape of km.labels_ is n_samples %s' % km.labels_.shape)\n",
-    "print('true_k is %s'% true_k)\n",
-    "print('np.unique(labels) is %s' % np.unique(labels))\n",
-    "print('np.unique(labels).shape is %s' % np.unique(labels).shape)\n",
-    "print(\"----------Test---------------\")\n",
-    "print()\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels, km.labels_))\n",
-    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels, km.labels_))\n",
-    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels, km.labels_))\n",
-    "print(\"Adjusted Rand-Index: %.3f\"\n",
-    "      % metrics.adjusted_rand_score(labels, km.labels_))\n",
-    "print(\"Silhouette Coefficient: %0.3f\"\n",
-    "      % metrics.silhouette_score(X, km.labels_, sample_size=1000))\n",
-    "\n",
-    "print()\n",
-    "\n",
-    "\n",
-    "if not opts.use_hashing:\n",
-    "    print(\"Top terms per cluster:\")\n",
-    "\n",
-    "    if opts.n_components:\n",
-    "        original_space_centroids = svd.inverse_transform(km.cluster_centers_)\n",
-    "        order_centroids = original_space_centroids.argsort()[:, ::-1]\n",
-    "    else:\n",
-    "        order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n",
-    "\n",
-    "    terms = vectorizer.get_feature_names()\n",
-    "    for i in range(true_k):\n",
-    "        print(\"Cluster %d:\" % i, end='')\n",
-    "        for ind in order_centroids[i, :10]:\n",
-    "            print(' %s' % terms[ind], end='')\n",
-    "        print()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/tour_model_eval/plot_kmeans_digits.ipynb b/tour_model_eval/plot_kmeans_digits.ipynb
deleted file mode 100644
index e88eb15..0000000
--- a/tour_model_eval/plot_kmeans_digits.ipynb
+++ /dev/null
@@ -1,286 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# A demo of K-Means clustering on the handwritten digits data\n",
-    "\n",
-    "In this example we compare the various initialization strategies for K-means in\n",
-    "terms of runtime and quality of the results.\n",
-    "\n",
-    "As the ground truth is known here, we also apply different cluster quality\n",
-    "metrics to judge the goodness of fit of the cluster labels to the ground truth.\n",
-    "\n",
-    "Cluster quality metrics evaluated (see `clustering_evaluation` for\n",
-    "definitions and discussions of the metrics):\n",
-    "\n",
-    "=========== ========================================================\n",
-    "Shorthand    full name\n",
-    "=========== ========================================================\n",
-    "homo         homogeneity score\n",
-    "compl        completeness score\n",
-    "v-meas       V measure\n",
-    "ARI          adjusted Rand index\n",
-    "AMI          adjusted mutual information\n",
-    "silhouette   silhouette coefficient\n",
-    "=========== ========================================================\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(__doc__)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load the dataset\n",
-    "\n",
-    "We will start by loading the `digits` dataset. This dataset contains\n",
-    "handwritten digits from 0 to 9. In the context of clustering, one would like\n",
-    "to group images such that the handwritten digits on the image are the same.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.datasets import load_digits\n",
-    "\n",
-    "data, labels = load_digits(return_X_y=True)\n",
-    "(n_samples, n_features), n_digits = data.shape, np.unique(labels).size\n",
-    "\n",
-    "print(\n",
-    "    f\"# digits: {n_digits}; # samples: {n_samples}; # features {n_features}\"\n",
-    ")\n",
-    "\n",
-    "print('------Test------')\n",
-    "np.set_printoptions(threshold=np.inf)\n",
-    "print('data.shape is ',data.shape)\n",
-    "print(\"labels is %s\" % labels)\n",
-    "print('------Test------')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define our evaluation benchmark\n",
-    "\n",
-    "We will first our evaluation benchmark. During this benchmark, we intend to\n",
-    "compare different initialization methods for KMeans. Our benchmark will:\n",
-    "\n",
-    "* create a pipeline which will scale the data using a\n",
-    "  :class:`~sklearn.preprocessing.StandardScaler`;\n",
-    "* train and time the pipeline fitting;\n",
-    "* measure the performance of the clustering obtained via different metrics.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from time import time\n",
-    "from sklearn import metrics\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
-    "\n",
-    "def bench_k_means(kmeans, name, data, labels):\n",
-    "    \"\"\"Benchmark to evaluate the KMeans initialization methods.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    kmeans : KMeans instance\n",
-    "        A :class:`~sklearn.cluster.KMeans` instance with the initialization\n",
-    "        already set.\n",
-    "    name : str\n",
-    "        Name given to the strategy. It will be used to show the results in a\n",
-    "        table.\n",
-    "    data : ndarray of shape (n_samples, n_features)\n",
-    "        The data to cluster.\n",
-    "    labels : ndarray of shape (n_samples,)\n",
-    "        The labels used to compute the clustering metrics which requires some\n",
-    "        supervision.\n",
-    "    \"\"\"\n",
-    "    t0 = time()\n",
-    "    estimator = make_pipeline(StandardScaler(), kmeans).fit(data)\n",
-    "    fit_time = time() - t0\n",
-    "    results = [name, fit_time, estimator[-1].inertia_]\n",
-    "\n",
-    "    # Define the metrics which require only the true labels and estimator\n",
-    "    # labels\n",
-    "    clustering_metrics = [\n",
-    "        metrics.homogeneity_score,\n",
-    "        metrics.completeness_score,\n",
-    "        metrics.v_measure_score,\n",
-    "        metrics.adjusted_rand_score,\n",
-    "        metrics.adjusted_mutual_info_score,\n",
-    "    ]\n",
-    "    results += [m(labels, estimator[-1].labels_) for m in clustering_metrics]\n",
-    "    \n",
-    "    print('------Test------')\n",
-    "    print('estimator[-1].labels_', estimator[-1].labels_)\n",
-    "    print('------Test------')\n",
-    "\n",
-    "    # The silhouette score requires the full dataset\n",
-    "    results += [\n",
-    "        metrics.silhouette_score(data, estimator[-1].labels_,\n",
-    "                                 metric=\"euclidean\", sample_size=300,)\n",
-    "    ]\n",
-    "\n",
-    "    # Show the results\n",
-    "    formatter_result = (\"{:9s}\\t{:.3f}s\\t{:.0f}\\t{:.3f}\\t{:.3f}\"\n",
-    "                        \"\\t{:.3f}\\t{:.3f}\\t{:.3f}\\t{:.3f}\")\n",
-    "    print(formatter_result.format(*results))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run the benchmark\n",
-    "\n",
-    "We will compare three approaches:\n",
-    "\n",
-    "* an initialization using `kmeans++`. This method is stochastic and we will\n",
-    "  run the initialization 4 times;\n",
-    "* a random initialization. This method is stochastic as well and we will run\n",
-    "  the initialization 4 times;\n",
-    "* an initialization based on a :class:`~sklearn.decomposition.PCA`\n",
-    "  projection. Indeed, we will use the components of the\n",
-    "  :class:`~sklearn.decomposition.PCA` to initialize KMeans. This method is\n",
-    "  deterministic and a single initialization suffice.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "print(82 * '_')\n",
-    "print('init\\t\\ttime\\tinertia\\thomo\\tcompl\\tv-meas\\tARI\\tAMI\\tsilhouette')\n",
-    "\n",
-    "kmeans = KMeans(init=\"k-means++\", n_clusters=n_digits, n_init=4,\n",
-    "                random_state=0)\n",
-    "bench_k_means(kmeans=kmeans, name=\"k-means++\", data=data, labels=labels)\n",
-    "\n",
-    "kmeans = KMeans(init=\"random\", n_clusters=n_digits, n_init=4, random_state=0)\n",
-    "bench_k_means(kmeans=kmeans, name=\"random\", data=data, labels=labels)\n",
-    "\n",
-    "pca = PCA(n_components=n_digits).fit(data)\n",
-    "kmeans = KMeans(init=pca.components_, n_clusters=n_digits, n_init=1)\n",
-    "bench_k_means(kmeans=kmeans, name=\"PCA-based\", data=data, labels=labels)\n",
-    "\n",
-    "print(82 * '_')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize the results on PCA-reduced data\n",
-    "\n",
-    ":class:`~sklearn.decomposition.PCA` allows to project the data from the\n",
-    "original 64-dimensional space into a lower dimensional space. Subsequently,\n",
-    "we can use :class:`~sklearn.decomposition.PCA` to project into a\n",
-    "2-dimensional space and plot the data and the clusters in this new space.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "reduced_data = PCA(n_components=2).fit_transform(data)\n",
-    "kmeans = KMeans(init=\"k-means++\", n_clusters=n_digits, n_init=4)\n",
-    "kmeans.fit(reduced_data)\n",
-    "\n",
-    "# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
-    "h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
-    "\n",
-    "# Plot the decision boundary. For that, we will assign a color to each\n",
-    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
-    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
-    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
-    "\n",
-    "# Obtain labels for each point in mesh. Use last trained model.\n",
-    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
-    "\n",
-    "# Put the result into a color plot\n",
-    "Z = Z.reshape(xx.shape)\n",
-    "plt.figure(1)\n",
-    "plt.clf()\n",
-    "plt.imshow(Z, interpolation=\"nearest\",\n",
-    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
-    "           cmap=plt.cm.Paired, aspect=\"auto\", origin=\"lower\")\n",
-    "\n",
-    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
-    "# Plot the centroids as a white X\n",
-    "centroids = kmeans.cluster_centers_\n",
-    "plt.scatter(centroids[:, 0], centroids[:, 1], marker=\"x\", s=169, linewidths=3,\n",
-    "            color=\"w\", zorder=10)\n",
-    "plt.title(\"K-means clustering on the digits dataset (PCA-reduced data)\\n\"\n",
-    "          \"Centroids are marked with white cross\")\n",
-    "plt.xlim(x_min, x_max)\n",
-    "plt.ylim(y_min, y_max)\n",
-    "plt.xticks(())\n",
-    "plt.yticks(())\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
index 6a55dd8..fb50a58 100644
--- a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "severe-married",
+   "id": "outer-institute",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "knowing-price",
+   "id": "precious-chassis",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "graphic-determination",
+   "id": "demonstrated-presentation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "breathing-description",
+   "id": "according-salem",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,17 +66,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "gorgeous-retailer",
+   "id": "dynamic-wallace",
    "metadata": {},
    "outputs": [],
    "source": [
-    "user = all_users[0]"
+    "user = all_users[6]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "homeless-father",
+   "id": "proved-vintage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "arranged-cleaning",
+   "id": "postal-lecture",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,27 +98,45 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "super-spencer",
+   "id": "governmental-chamber",
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(non_empty_trips), non_empty_trips"
+    "len(non_empty_trips),non_empty_trips"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "higher-advocate",
+   "id": "complicated-movie",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bin_trips, bins = pipeline.remove_noise(non_empty_trips, radius)"
+    "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
+    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
+    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
+    "len(valid_trips),valid_trips"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "beautiful-parcel",
+   "id": "waiting-courage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = similarity.similarity(valid_trips, radius)\n",
+    "filter_trips = sim.data\n",
+    "sim.bin_data()\n",
+    "sim.delete_bins()\n",
+    "bin_trips = sim.newdata\n",
+    "bins = sim.bins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "organized-multiple",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "comic-norfolk",
+   "id": "packed-redhead",
    "metadata": {
     "scrolled": true
    },
@@ -136,14 +154,14 @@
    "source": [
     "# show all user labels in all bins\n",
     "for bin in bins:\n",
-    "    bin_user_input = (non_empty_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_user_input = (filter_trips[i].data[\"user_input\"] for i in bin)\n",
     "    bin_df = pd.DataFrame(data = bin_user_input)\n",
     "    print(bin_df)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "expensive-forestry",
+   "id": "egyptian-sessions",
    "metadata": {},
    "source": [
     "### Original output"
@@ -152,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "corporate-situation",
+   "id": "proved-slave",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,8 +181,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "historic-russian",
-   "metadata": {},
+   "id": "small-tattoo",
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": [
     "# turn all user_input into list without binning\n",
@@ -175,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "jewish-bristol",
+   "id": "greek-management",
    "metadata": {
     "scrolled": true
    },
@@ -183,13 +203,13 @@
    "source": [
     "# drop duplicate user_input\n",
     "no_dup_df=bin_trips_df.drop_duplicates()\n",
-    "no_dup_df"
+    "no_dup_df,len(no_dup_df)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "wooden-postage",
+   "id": "discrete-secretary",
    "metadata": {
     "scrolled": true
    },
@@ -203,7 +223,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "wicked-serial",
+   "id": "economic-madison",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +238,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "surgical-stadium",
+   "id": "plain-lotus",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "first-campbell",
+   "id": "saved-coral",
    "metadata": {},
    "source": [
     "Note: the trips order in labels_true and labels_pred should be the same. Using timestamp to compare the trips in bin_trips and those in bins"
@@ -241,18 +261,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dental-universal",
+   "id": "prescribed-former",
    "metadata": {},
    "outputs": [],
    "source": [
-    "bin_trips_ts = pd.DataFrame(data=[i[\"data\"][\"start_ts\"]for i in bin_trips])\n",
-    "len(bin_trips_ts)"
+    "bin_trips_ts = pd.DataFrame(data=[trip[\"data\"][\"start_ts\"]for trip in bin_trips])\n",
+    "bin_input = pd.DataFrame(data=[trip[\"data\"][\"user_input\"]for trip in bin_trips])\n",
+    "len(bin_trips_ts)\n",
+    "bin_input"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dynamic-prize",
+   "id": "supposed-style",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,14 +282,16 @@
     "for bin in bins:\n",
     "    for index in bin:\n",
     "        bin_ls.append(index)\n",
-    "bins_ts = pd.DataFrame(data=[non_empty_trips[i][\"data\"][\"start_ts\"]for i in bin_ls])\n",
-    "len(bins_ts)"
+    "bins_ts = pd.DataFrame(data=[filter_trips[i][\"data\"][\"start_ts\"]for i in bin_ls])\n",
+    "bins_input = pd.DataFrame(data=[filter_trips[num][\"data\"][\"user_input\"]for num in bin_ls])\n",
+    "len(bins_ts)\n",
+    "bins_input"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "republican-rabbit",
+   "id": "functioning-triangle",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -278,7 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "concerned-congo",
+   "id": "distinguished-chapter",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,7 +312,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alleged-alabama",
+   "id": "public-anime",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +322,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "material-genetics",
+   "id": "single-watson",
    "metadata": {
     "scrolled": true
    },
@@ -309,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aware-friday",
+   "id": "chief-finish",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -318,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "native-sight",
+   "id": "noticed-operations",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -331,13 +355,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "threaded-focus",
+   "id": "worse-print",
    "metadata": {},
    "outputs": [],
    "source": [
     "# use dict to replace the values in Spanish in the bin(this step just for showing the trips in each bin)\n",
     "for bin in bins:\n",
-    "    bin_user_input = (non_empty_trips[i].data[\"user_input\"] for i in bin)\n",
+    "    bin_user_input = (filter_trips[i].data[\"user_input\"] for i in bin)\n",
     "    bin_df = pd.DataFrame(data = bin_user_input)\n",
     "    sp2en_bin_df = bin_df.replace(span_eng_dict)\n",
     "    print(sp2en_bin_df)"
@@ -346,33 +370,34 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "imported-reasoning",
+   "id": "tutorial-graphic",
    "metadata": {},
    "outputs": [],
    "source": [
     "# turn all user_input into list without binning\n",
-    "bin_trips_user_input_sp2en_ls = pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in bin_trips]).replace(span_eng_dict).values.tolist()\n",
-    "bin_trips_user_input_sp2en_ls"
+    "bin_trips_sp2en_df = bin_trips_df.replace(span_eng_dict)\n",
+    "bin_trips_sp2en_ls = bin_trips_sp2en_df.values.tolist()\n",
+    "bin_trips_sp2en_ls"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "athletic-reasoning",
+   "id": "verbal-makeup",
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [],
    "source": [
     "# drop duplicate user_input\n",
-    "no_dup_sp2en_df=pd.DataFrame(data=[i[\"data\"][\"user_input\"] for i in bin_trips]).replace(span_eng_dict).drop_duplicates()\n",
+    "no_dup_sp2en_df=bin_trips_sp2en_df.drop_duplicates()\n",
     "no_dup_sp2en_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "false-fields",
+   "id": "forced-relaxation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,13 +409,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "formal-belgium",
+   "id": "invisible-assist",
    "metadata": {},
    "outputs": [],
    "source": [
     "# collect labels_true based on user_input\n",
     "labels_true_sp2en =[]\n",
-    "for trip in bin_trips_user_input_sp2en_ls:\n",
+    "for trip in bin_trips_sp2en_ls:\n",
     "    if trip in no_dup_sp2en_list:\n",
     "        labels_true_sp2en.append(no_dup_sp2en_list.index(trip))\n",
     "labels_true_sp2en"
@@ -399,7 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "indirect-lafayette",
+   "id": "operating-myrtle",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +439,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cooked-mineral",
+   "id": "dietary-clone",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "caring-calcium",
+   "id": "breathing-garbage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -434,7 +459,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sunset-frequency",
+   "id": "fabulous-thanksgiving",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "included-alberta",
+   "id": "comparable-student",
    "metadata": {},
    "source": [
     "### After converting purposes and mode"
@@ -452,7 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "outer-hammer",
+   "id": "average-contrary",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "developing-socket",
+   "id": "collect-eating",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -473,13 +498,12 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "partial-prerequisite",
+   "id": "valued-requirement",
    "metadata": {},
    "outputs": [],
    "source": [
     "# convert purpose\n",
-    "bin_trips_user_input_sp2en = pd.DataFrame(data=[bin_trips[i][\"data\"][\"user_input\"] for i in range(len(bin_trips))]).replace(span_eng_dict)\n",
-    "bin_trips_cvt_pur_df = bin_trips_user_input_sp2en.replace(map_pur_dict)\n",
+    "bin_trips_cvt_pur_df = bin_trips_sp2en_df.replace(map_pur_dict)\n",
     "# convert mode\n",
     "bin_trips_cvt_pur_mo_df = bin_trips_cvt_pur_df\n",
     "for i in range(len(bin_trips_cvt_pur_mo_df)):\n",
@@ -494,7 +518,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "classical-berkeley",
+   "id": "intimate-microphone",
    "metadata": {
     "scrolled": true
    },
@@ -508,7 +532,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "opening-equity",
+   "id": "naked-turkish",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "occasional-evanescence",
+   "id": "governing-treat",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -535,7 +559,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fancy-barbados",
+   "id": "outstanding-credit",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -550,7 +574,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "placed-carry",
+   "id": "bearing-generator",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,7 +584,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "worse-shift",
+   "id": "periodic-barrier",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +594,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "immediate-series",
+   "id": "sixth-nowhere",
    "metadata": {},
    "outputs": [],
    "source": [

From ed7ac2c9e047018a567824d2e899a84319289861 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Tue, 23 Feb 2021 17:25:04 -0800
Subject: [PATCH 07/16] clean up evaluation code

---
 .../confirmed_trips_eval_bins_clusters.py     | 25 ++++++++++---------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index 1bcc370..4f9652d 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -23,6 +23,16 @@
                'insurance_payment':'insurance'}
 
 
+def filter_data(user,radius):
+    trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
+    non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
+    valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
+                   'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
+    sim = similarity.similarity(valid_trips, radius)
+    filter_trips = sim.data
+    return filter_trips,sim
+
+
 # v_measure_bins takes 5 parameters
 # - sp2en=True: change Spanish to English
 # - cvt_pur_mo=True: convert purposes and replaced mode
@@ -35,12 +45,7 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
     v_score = []
     for i in range(len(all_users)):
         user = all_users[i]
-        trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
-        non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
-        valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
-                       'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
-        sim = similarity.similarity(valid_trips, radius)
-        filter_trips = sim.data
+        filter_trips,sim = filter_data(user,radius)
 
         # filter out users that haven't enough trips (at least 10) to analyze
         if len(filter_trips) < 10:
@@ -129,12 +134,8 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
     v_score = []
     for i in range(len(all_users)):
         user = all_users[i]
-        trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
-        non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
-        valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
-                       'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
-        sim = similarity.similarity(valid_trips, radius)
-        filter_trips = sim.data
+        filter_trips,sim = filter_data(user,radius)
+
         # filter out users that haven't enough trips (at least 10) to analyze
         if len(filter_trips) < 10:
             homo_score.append(NaN)

From 5aa469f2df7545c113b39add1ab82731146fd56a Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Tue, 23 Feb 2021 23:19:21 -0800
Subject: [PATCH 08/16] change radius, add query times notebook

---
 tour_model_eval/query_times_all_users.ipynb   | 265 ++++++++++++++++++
 .../v-measurel_all_bins_single_user.ipynb     | 100 +++----
 .../v-measurel_bins_all_user.ipynb            |  58 ++--
 ...urel_clusters_above_cutoff_all_users.ipynb |  34 +--
 .../v-measurel_cutoff_bins_single_user.ipynb  |  98 +++----
 ...measurel_cutoff_clusters_single_user.ipynb |  28 +-
 6 files changed, 420 insertions(+), 163 deletions(-)
 create mode 100644 tour_model_eval/query_times_all_users.ipynb

diff --git a/tour_model_eval/query_times_all_users.ipynb b/tour_model_eval/query_times_all_users.ipynb
new file mode 100644
index 0000000..8877cfe
--- /dev/null
+++ b/tour_model_eval/query_times_all_users.ipynb
@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "expanded-implement",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "# Our imports\n",
+    "import emission.core.get_database as edb\n",
+    "import emission.analysis.modelling.tour_model.cluster_pipeline as pipeline\n",
+    "import emission.analysis.modelling.tour_model.similarity as similarity\n",
+    "import emission.analysis.modelling.tour_model.featurization as featurization\n",
+    "import emission.analysis.modelling.tour_model.representatives as representatives\n",
+    "import emission.storage.decorations.analysis_timeseries_queries as esda\n",
+    "import pandas as pd\n",
+    "from numpy import *\n",
+    "import confirmed_trips_eval_bins_clusters as evaluation\n",
+    "from sklearn import metrics\n",
+    "from pandas.testing import assert_frame_equal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "functional-birthday",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# logger = logging.getLogger()\n",
+    "# logger.setLevel(logging.DEBUG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "turned-watch",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_uuid_obj = list(edb.get_profile_db().find({\"install_group\": \"participant\"}, {\"user_id\": 1, \"_id\": 0}))\n",
+    "all_users = [u[\"user_id\"] for u in participant_uuid_obj]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "encouraging-killer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tender-volleyball",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_day = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "perfect-promotion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_month = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ordered-juvenile",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def match_day(trip,bin):\n",
+    "    if bin:\n",
+    "        t = filter_trips[bin[0]]\n",
+    "        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']and trip['data']['start_local_dt']['day']==t['data']['start_local_dt']['day']:\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "raising-brisbane",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def match_month(trip,bin):\n",
+    "    if bin:\n",
+    "        t = filter_trips[bin[0]]\n",
+    "        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']:\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "comic-apache",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(len(all_users)):\n",
+    "    user = all_users[i]\n",
+    "    filter_trips,sim = evaluation.filter_data(user,radius)\n",
+    "    logging.debug(\"len(filter_trips)is %s \"% len(filter_trips))\n",
+    "\n",
+    "    # filter out users that don't have valid labeled trips\n",
+    "    if len(filter_trips) == 0:\n",
+    "        query_day.append(NaN)\n",
+    "        query_month.append(NaN)\n",
+    "        continue\n",
+    "        \n",
+    "    sim.bin_data()\n",
+    "    sim.delete_bins()\n",
+    "    # bins below cutoff\n",
+    "    bl_bins = sim.below_cutoff\n",
+    "    \n",
+    "    # a list of trip indices from bl_bins\n",
+    "    bl_trip_ls = []\n",
+    "    for bin in bl_bins:\n",
+    "        for index in bin:\n",
+    "            bl_trip_ls.append(index)\n",
+    "\n",
+    "    # collect all the trips that below cutoff\n",
+    "    bl_trips = [filter_trips[num]for num in bl_trip_ls]\n",
+    "    \n",
+    "    \n",
+    "    # collect query times in a day\n",
+    "    bin_day = []\n",
+    "    for trip_index in bl_trip_ls:\n",
+    "        added = False\n",
+    "        trip = filter_trips[trip_index]\n",
+    "        for bin in bin_day:\n",
+    "            if match_day(trip,bin):\n",
+    "                bin.append(trip_index)\n",
+    "                added = True\n",
+    "        if not added:\n",
+    "            bin_day.append([trip_index])\n",
+    "    query_day_ls = []\n",
+    "    for bin in bin_day:\n",
+    "        query_day_ls.append(len(bin))\n",
+    "\n",
+    "    # average query times for a day\n",
+    "    query_day.append(math.ceil(mean(query_day_ls)))\n",
+    "    \n",
+    "    \n",
+    "    # collect query times in a month\n",
+    "    bin_month = []\n",
+    "    for trip_index in bl_trip_ls:\n",
+    "        added = False\n",
+    "        trip = filter_trips[trip_index]\n",
+    "        for bin in bin_month:\n",
+    "            if match_month(trip,bin):\n",
+    "                bin.append(trip_index)\n",
+    "                added = True\n",
+    "        if not added:\n",
+    "            bin_month.append([trip_index])\n",
+    "        query_month_ls = []\n",
+    "    for bin in bin_month:\n",
+    "        query_month_ls.append(len(bin))\n",
+    "\n",
+    "    # average query times for a month\n",
+    "    query_month.append(math.ceil(mean(query_month_ls)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alternative-dialogue",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "tracked-pattern",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "instant-somerset",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_day = math.ceil(mean([x for x in query_day if str(x) != 'nan']))\n",
+    "mean_day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "documented-municipality",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_month = math.ceil(mean([x for x in query_month if str(x) != 'nan']))\n",
+    "mean_month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "burning-elephant",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data = {'query times in a day':query_day,'query times in a month':query_month})\n",
+    "df.loc['mean'] = [mean_day,mean_month]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "incoming-gibson",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.plot(kind = 'bar')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tour_model_eval/v-measurel_all_bins_single_user.ipynb b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
index 4eb2013..cf2aa3a 100644
--- a/tour_model_eval/v-measurel_all_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "modified-diary",
+   "id": "republican-pleasure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "together-twenty",
+   "id": "decent-passion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sorted-lloyd",
+   "id": "preliminary-example",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,11 +48,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "missing-psychology",
+   "id": "executive-heather",
    "metadata": {},
    "outputs": [],
    "source": [
-    "radius = 300"
+    "radius = 100"
    ]
   },
   {
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "august-valley",
+   "id": "genuine-shipping",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "diverse-running",
+   "id": "sorted-juvenile",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "crude-postage",
+   "id": "primary-friendly",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "copyrighted-delta",
+   "id": "medical-spider",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "competent-australia",
+   "id": "latest-reconstruction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "standing-serial",
+   "id": "stone-omega",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "annoying-antique",
+   "id": "individual-insert",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "latest-contemporary",
+   "id": "continued-borough",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "thrown-hello",
+   "id": "outside-fairy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "strange-inventory",
+   "id": "extraordinary-penalty",
    "metadata": {},
    "source": [
     "### Original output"
@@ -184,7 +184,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "streaming-taiwan",
+   "id": "advised-wiring",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "destroyed-filter",
+   "id": "careful-sally",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "mature-italy",
+   "id": "elect-hardwood",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +219,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "illegal-collect",
+   "id": "dynamic-grace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sapphire-hughes",
+   "id": "romance-chrome",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fuzzy-marathon",
+   "id": "further-moscow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "considerable-restoration",
+   "id": "welsh-trustee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +271,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "domestic-august",
+   "id": "minimal-fever",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "married-catholic",
+   "id": "corporate-missile",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "requested-organic",
+   "id": "radio-meter",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "economic-nitrogen",
+   "id": "fancy-mount",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +318,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "postal-trademark",
+   "id": "brown-traveler",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +328,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "southern-evidence",
+   "id": "chemical-rider",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "domestic-regression",
+   "id": "excessive-spell",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -346,7 +346,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "amazing-jersey",
+   "id": "christian-legislature",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "impressive-philadelphia",
+   "id": "ready-delay",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +374,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hybrid-eugene",
+   "id": "blind-router",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +387,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "viral-intermediate",
+   "id": "suited-institution",
    "metadata": {
     "scrolled": true
    },
@@ -401,7 +401,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "conceptual-branch",
+   "id": "incomplete-confusion",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,7 +413,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "wanted-management",
+   "id": "ahead-hawaiian",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "otherwise-vault",
+   "id": "collectible-australia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +443,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adolescent-style",
+   "id": "changed-pollution",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "signal-backing",
+   "id": "judicial-printer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +463,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "upper-contribution",
+   "id": "female-confidentiality",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bearing-communications",
+   "id": "foster-reading",
    "metadata": {},
    "source": [
     "### After converting purposes and mode"
@@ -481,7 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "completed-testing",
+   "id": "opposite-aurora",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "quality-board",
+   "id": "western-commissioner",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "falling-arbor",
+   "id": "vocal-saudi",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,7 +522,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "electrical-liechtenstein",
+   "id": "recreational-label",
    "metadata": {
     "scrolled": true
    },
@@ -536,7 +536,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cleared-valley",
+   "id": "adequate-notice",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "printable-fusion",
+   "id": "stuck-survivor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,7 +563,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hundred-zealand",
+   "id": "floppy-flight",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,7 +578,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "false-lithuania",
+   "id": "broken-demand",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -588,7 +588,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "documentary-power",
+   "id": "nasty-potato",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +598,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "elect-animation",
+   "id": "regular-glance",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
index 12a2d49..a4af2bd 100644
--- a/tour_model_eval/v-measurel_bins_all_user.ipynb
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "computational-national",
+   "id": "hungry-polish",
    "metadata": {
     "scrolled": true
    },
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "preceding-yugoslavia",
+   "id": "coupled-transportation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,17 +39,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "perceived-impossible",
+   "id": "interstate-queue",
    "metadata": {},
    "outputs": [],
    "source": [
-    "radius = 300"
+    "radius = 100"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "smooth-variety",
+   "id": "norwegian-victoria",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "medical-biodiversity",
+   "id": "loved-estonia",
    "metadata": {},
    "source": [
     "## Bins above cutoff"
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "corporate-heather",
+   "id": "laughing-macintosh",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "chief-fundamentals",
+   "id": "amber-photograph",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "educated-philippines",
+   "id": "collected-throat",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "welcome-homeless",
+   "id": "headed-dating",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "preceding-money",
+   "id": "blank-arnold",
    "metadata": {
     "scrolled": true
    },
@@ -116,7 +116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "laden-plate",
+   "id": "maritime-twins",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sticky-denver",
+   "id": "consistent-kuwait",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "agreed-moment",
+   "id": "confused-sensitivity",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "handmade-dairy",
+   "id": "advance-passing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "funky-potato",
+   "id": "bronze-operations",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "technological-reservation",
+   "id": "pacific-reflection",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "strange-badge",
+   "id": "documented-vacation",
    "metadata": {},
    "source": [
     "## All bins"
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rural-virtue",
+   "id": "selected-compiler",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -193,7 +193,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "listed-entrance",
+   "id": "tight-contract",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "known-calculation",
+   "id": "regular-shadow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "broadband-expression",
+   "id": "brutal-trinidad",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -221,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "alone-authorization",
+   "id": "decent-machinery",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "viral-rebel",
+   "id": "lasting-synthetic",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "infectious-armstrong",
+   "id": "sexual-opportunity",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -249,7 +249,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "supreme-plain",
+   "id": "periodic-freedom",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "corresponding-blind",
+   "id": "stylish-thailand",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aquatic-password",
+   "id": "floppy-theory",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "instant-broadcast",
+   "id": "british-working",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
index f653b5a..67a0f56 100644
--- a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "important-humanity",
+   "id": "adverse-recipient",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "physical-improvement",
+   "id": "pleased-fence",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,17 +37,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "figured-buddy",
+   "id": "global-leave",
    "metadata": {},
    "outputs": [],
    "source": [
-    "radius = 300"
+    "radius = 100"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "equal-release",
+   "id": "hundred-surge",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "standard-savannah",
+   "id": "funky-darkness",
    "metadata": {},
    "source": [
     "## Evaluate clusters above cutoff based on silhouette_score"
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "formed-guitar",
+   "id": "critical-prime",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "official-victor",
+   "id": "copyrighted-vehicle",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "loved-isolation",
+   "id": "stainless-exposure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "demanding-rendering",
+   "id": "returning-skiing",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "satisfied-enough",
+   "id": "authorized-complement",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hundred-pasta",
+   "id": "hispanic-drunk",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hungarian-wallpaper",
+   "id": "united-grade",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "particular-scope",
+   "id": "outer-usage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "neither-shooting",
+   "id": "positive-grass",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "continuing-absorption",
+   "id": "assured-oriental",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "retained-citizenship",
+   "id": "authentic-compression",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
index fb50a58..271ef9f 100644
--- a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "outer-institute",
+   "id": "primary-friendship",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "precious-chassis",
+   "id": "composed-sewing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "demonstrated-presentation",
+   "id": "diagnostic-stand",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,11 +48,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "according-salem",
+   "id": "unexpected-mineral",
    "metadata": {},
    "outputs": [],
    "source": [
-    "radius = 300"
+    "radius = 100"
    ]
   },
   {
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dynamic-wallace",
+   "id": "stock-facing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "proved-vintage",
+   "id": "genetic-liberty",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "postal-lecture",
+   "id": "patient-spirit",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "governmental-chamber",
+   "id": "obvious-imaging",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "complicated-movie",
+   "id": "sufficient-baseball",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "waiting-courage",
+   "id": "inclusive-theory",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "organized-multiple",
+   "id": "sticky-indianapolis",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "packed-redhead",
+   "id": "recreational-diesel",
    "metadata": {
     "scrolled": true
    },
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "egyptian-sessions",
+   "id": "complete-feeling",
    "metadata": {},
    "source": [
     "### Original output"
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "proved-slave",
+   "id": "innocent-circumstances",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "small-tattoo",
+   "id": "lightweight-connectivity",
    "metadata": {
     "scrolled": false
    },
@@ -195,7 +195,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "greek-management",
+   "id": "ceramic-sense",
    "metadata": {
     "scrolled": true
    },
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "discrete-secretary",
+   "id": "intensive-rings",
    "metadata": {
     "scrolled": true
    },
@@ -223,7 +223,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "economic-madison",
+   "id": "little-cuisine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "plain-lotus",
+   "id": "absent-emerald",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "saved-coral",
+   "id": "agricultural-welcome",
    "metadata": {},
    "source": [
     "Note: the trips order in labels_true and labels_pred should be the same. Using timestamp to compare the trips in bin_trips and those in bins"
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "prescribed-former",
+   "id": "appropriate-convert",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "supposed-style",
+   "id": "billion-yield",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "functioning-triangle",
+   "id": "subjective-victim",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "distinguished-chapter",
+   "id": "numeric-worth",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +312,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "public-anime",
+   "id": "tired-citation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +322,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "single-watson",
+   "id": "romance-investment",
    "metadata": {
     "scrolled": true
    },
@@ -333,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "chief-finish",
+   "id": "increasing-baghdad",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -342,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "noticed-operations",
+   "id": "hidden-source",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +355,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "worse-print",
+   "id": "reduced-hebrew",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +370,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "tutorial-graphic",
+   "id": "minor-reason",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +383,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "verbal-makeup",
+   "id": "acknowledged-spanish",
    "metadata": {
     "scrolled": false
    },
@@ -397,7 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "forced-relaxation",
+   "id": "competent-audience",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "invisible-assist",
+   "id": "egyptian-request",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,7 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "operating-myrtle",
+   "id": "communist-tomorrow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,7 +439,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dietary-clone",
+   "id": "beneficial-freeze",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +449,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "breathing-garbage",
+   "id": "corrected-processing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +459,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fabulous-thanksgiving",
+   "id": "overall-terrace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "comparable-student",
+   "id": "collectible-heater",
    "metadata": {},
    "source": [
     "### After converting purposes and mode"
@@ -477,7 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "average-contrary",
+   "id": "pacific-newport",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "collect-eating",
+   "id": "unique-russia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -498,7 +498,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "valued-requirement",
+   "id": "alone-external",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,7 +518,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "intimate-microphone",
+   "id": "parallel-position",
    "metadata": {
     "scrolled": true
    },
@@ -532,7 +532,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "naked-turkish",
+   "id": "weighted-arbitration",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "governing-treat",
+   "id": "severe-stable",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +559,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "outstanding-credit",
+   "id": "natural-commitment",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +574,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bearing-generator",
+   "id": "million-authentication",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -584,7 +584,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "periodic-barrier",
+   "id": "downtown-nelson",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +594,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sixth-nowhere",
+   "id": "understanding-equity",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
index e6fe2e0..c1c1ea9 100644
--- a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
@@ -52,7 +52,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "radius = 300"
+    "radius = 100"
    ]
   },
   {
@@ -99,7 +99,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fabulous-definition",
+   "id": "excited-closure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "valid-morocco",
+   "id": "pleased-generic",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,7 +146,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bridal-breakfast",
+   "id": "north-instruction",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "following-heating",
+   "id": "boring-nerve",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "beginning-crowd",
+   "id": "neither-toilet",
    "metadata": {
     "scrolled": true
    },
@@ -179,7 +179,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "incorporated-strengthening",
+   "id": "artificial-serum",
    "metadata": {
     "scrolled": true
    },
@@ -265,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fallen-tulsa",
+   "id": "least-bristol",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,7 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dangerous-discussion",
+   "id": "remarkable-alliance",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "progressive-profit",
+   "id": "sticky-teaching",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -577,14 +577,6 @@
    "source": [
     "metrics.v_measure_score(labels_true_cvt, labels_pred)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "common-manhattan",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 84d640463826a99f78d085323c8dfe071590e7a2 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Fri, 26 Feb 2021 10:08:09 -0800
Subject: [PATCH 09/16] plot all data for all valid users

---
 tour_model_eval/query_times_all_users.ipynb   | 914 ++++++++++++++++--
 .../v-measurel_bins_all_user.ipynb            | 242 ++++-
 ...urel_clusters_above_cutoff_all_users.ipynb | 127 ++-
 3 files changed, 1170 insertions(+), 113 deletions(-)

diff --git a/tour_model_eval/query_times_all_users.ipynb b/tour_model_eval/query_times_all_users.ipynb
index 8877cfe..20164ba 100644
--- a/tour_model_eval/query_times_all_users.ipynb
+++ b/tour_model_eval/query_times_all_users.ipynb
@@ -2,10 +2,21 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "expanded-implement",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 1,
+   "id": "pending-avatar",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage not configured, falling back to sample, default configuration\n",
+      "Connecting to database URL localhost\n"
+     ]
+    }
+   ],
    "source": [
     "import logging\n",
     "\n",
@@ -20,24 +31,15 @@
     "from numpy import *\n",
     "import confirmed_trips_eval_bins_clusters as evaluation\n",
     "from sklearn import metrics\n",
-    "from pandas.testing import assert_frame_equal"
+    "from pandas.testing import assert_frame_equal\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.ticker import MaxNLocator"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "functional-birthday",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# logger = logging.getLogger()\n",
-    "# logger.setLevel(logging.DEBUG)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "turned-watch",
+   "execution_count": 2,
+   "id": "handmade-burning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,8 +49,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "encouraging-killer",
+   "execution_count": 3,
+   "id": "outstanding-representation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,8 +59,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "tender-volleyball",
+   "execution_count": 4,
+   "id": "needed-backing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,8 +69,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "perfect-promotion",
+   "execution_count": 5,
+   "id": "pacific-ranking",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,8 +79,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "ordered-juvenile",
+   "execution_count": 6,
+   "id": "minute-anniversary",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,8 +94,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "raising-brisbane",
+   "execution_count": 7,
+   "id": "chief-renewal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -107,8 +109,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "comic-apache",
+   "execution_count": 8,
+   "id": "iraqi-festival",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_day(query_day_ls):    \n",
+    "    query_day_ls_df = pd.DataFrame(data = query_day_ls)\n",
+    "    query_day_df=query_day_ls_df.value_counts(sort = False).rename_axis('query times').to_frame('frequecy').reset_index()\n",
+    "    query_day_df.set_index(['query times'], inplace=True)\n",
+    "    query_day=query_day_df.plot(kind = 'bar',title='query times in a day')\n",
+    "    query_day.yaxis.set_major_locator(MaxNLocator(integer=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "stopped-tractor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,22 +142,43 @@
     "        \n",
     "    sim.bin_data()\n",
     "    sim.delete_bins()\n",
+    "    bins = sim.bins\n",
+    "    \n",
+    "    # collect query trips indices above cutoff\n",
+    "    ab_trip_ls = []\n",
+    "    for bin in bins:\n",
+    "        early_trip = filter_trips[bin[0]]\n",
+    "        trip_index = 0\n",
+    "        for i in range(1,len(bin)):\n",
+    "            compare_trip = filter_trips[bin[i]]\n",
+    "            if early_trip['data']['start_local_dt']['year']>compare_trip['data']['start_local_dt']['year']:\n",
+    "                early_trip = compare_trip\n",
+    "                trip_index = i\n",
+    "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']>compare_trip['data']['start_local_dt']['month']:\n",
+    "                early_trip = compare_trip\n",
+    "                trip_index = i\n",
+    "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']==compare_trip['data']['start_local_dt']['month'] and early_trip['data']['start_local_dt']['day']>compare_trip['data']['start_local_dt']['day']:\n",
+    "                early_trip = compare_trip\n",
+    "                trip_index = i\n",
+    "        ab_trip_ls.append(bin[trip_index])\n",
+    "\n",
+    "\n",
+    "    \n",
     "    # bins below cutoff\n",
     "    bl_bins = sim.below_cutoff\n",
     "    \n",
-    "    # a list of trip indices from bl_bins\n",
+    "    # collect query trips indices below cutoff\n",
     "    bl_trip_ls = []\n",
     "    for bin in bl_bins:\n",
     "        for index in bin:\n",
     "            bl_trip_ls.append(index)\n",
-    "\n",
-    "    # collect all the trips that below cutoff\n",
-    "    bl_trips = [filter_trips[num]for num in bl_trip_ls]\n",
-    "    \n",
+    "            \n",
+    "    # whole list of query trips indices\n",
+    "    query_trips_ls=ab_trip_ls+bl_trip_ls    \n",
     "    \n",
     "    # collect query times in a day\n",
     "    bin_day = []\n",
-    "    for trip_index in bl_trip_ls:\n",
+    "    for trip_index in query_trips_ls:\n",
     "        added = False\n",
     "        trip = filter_trips[trip_index]\n",
     "        for bin in bin_day:\n",
@@ -153,13 +191,13 @@
     "    for bin in bin_day:\n",
     "        query_day_ls.append(len(bin))\n",
     "\n",
-    "    # average query times for a day\n",
-    "    query_day.append(math.ceil(mean(query_day_ls)))\n",
+    "    # collect query times in a day for every user\n",
+    "    query_day.append(query_day_ls)\n",
     "    \n",
     "    \n",
     "    # collect query times in a month\n",
     "    bin_month = []\n",
-    "    for trip_index in bl_trip_ls:\n",
+    "    for trip_index in query_trips_ls:\n",
     "        added = False\n",
     "        trip = filter_trips[trip_index]\n",
     "        for bin in bin_month:\n",
@@ -172,72 +210,810 @@
     "    for bin in bin_month:\n",
     "        query_month_ls.append(len(bin))\n",
     "\n",
-    "    # average query times for a month\n",
-    "    query_month.append(math.ceil(mean(query_month_ls)))\n"
+    "    # collect query times in a month for every user\n",
+    "    query_month.append(query_month_ls)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "alternative-dialogue",
+   "execution_count": 10,
+   "id": "victorian-rating",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[5,\n",
+       "  6,\n",
+       "  6,\n",
+       "  9,\n",
+       "  8,\n",
+       "  1,\n",
+       "  1,\n",
+       "  1,\n",
+       "  4,\n",
+       "  1,\n",
+       "  1,\n",
+       "  2,\n",
+       "  2,\n",
+       "  1,\n",
+       "  3,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  1,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  3,\n",
+       "  2,\n",
+       "  1,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  1,\n",
+       "  1,\n",
+       "  1,\n",
+       "  3,\n",
+       "  2,\n",
+       "  2,\n",
+       "  2,\n",
+       "  1,\n",
+       "  1,\n",
+       "  5,\n",
+       "  4,\n",
+       "  3,\n",
+       "  2,\n",
+       "  4,\n",
+       "  2,\n",
+       "  4,\n",
+       "  2,\n",
+       "  3,\n",
+       "  1],\n",
+       " nan,\n",
+       " [2,\n",
+       "  4,\n",
+       "  3,\n",
+       "  4,\n",
+       "  2,\n",
+       "  4,\n",
+       "  7,\n",
+       "  2,\n",
+       "  5,\n",
+       "  6,\n",
+       "  3,\n",
+       "  3,\n",
+       "  9,\n",
+       "  1,\n",
+       "  2,\n",
+       "  7,\n",
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+       "  1]]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "query_day"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "tracked-pattern",
+   "execution_count": 11,
+   "id": "victorian-venue",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[26, 61, 18, 7, 12],\n",
+       " nan,\n",
+       " [36, 52, 75],\n",
+       " [42, 28, 25],\n",
+       " [2, 3, 2],\n",
+       " [14, 38, 50],\n",
+       " [18, 7, 24, 1],\n",
+       " [131, 148, 4],\n",
+       " [71, 91, 28, 8],\n",
+       " [48, 50, 47],\n",
+       " [16, 2],\n",
+       " nan,\n",
+       " [153, 182, 152]]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "query_month"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "instant-somerset",
+   "cell_type": "markdown",
+   "id": "seven-istanbul",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "mean_day = math.ceil(mean([x for x in query_day if str(x) != 'nan']))\n",
-    "mean_day"
+    "### Plot query times in a day"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "documented-municipality",
+   "cell_type": "markdown",
+   "id": "supreme-search",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "mean_month = math.ceil(mean([x for x in query_month if str(x) != 'nan']))\n",
-    "mean_month"
+    "Note: frequecy represents the number of days have specific query times. Each graph represents query times for a user"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "burning-elephant",
+   "execution_count": 12,
+   "id": "corresponding-graphics",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJaqqAJd0saRlkp6V9O2iijIzs8q6HeCSRgPfBCYBY4ATJI0qqjAzM+tYNWfgBwOPRMS7EbER+AUwrZiyzMyskmoCfBlwtKQhknYGvgzsU0xZZmZWSd/uvjEiVkj6PnAvsAFYCmwsX05SA9AAMGLEiO5uzszMylR1ETMi5kTE+Ig4GngTeK6NZWZHRH1E1NfV1VWzOTMzK9HtM3AASZ+OiNcljQBOAj5bTFlmZlZJVQEO/FTSEOAj4E8jYl0BNZmZWSdUFeARcVRRhZiZWdf4Tkwzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLVFUBLmmwpFslrZS0QtJniyrMzMw61rfK988AFkTE1yTtCOxcQE1mZtYJ3Q5wSbsBRwNnAUTEh8CHxZRlZmaVVNOE8jtAM/BDSU9JukHSwPKFJDVIapTU2NzcXMXmzMysVDUB3hcYD1wfEeOAd4BLyxeKiNkRUR8R9XV1dVVszszMSlUT4E1AU0Q8mo/fShboZma2FXQ7wCPiNeBlSZ/JJx0LLC+kKjMzq6jaXijfAubmPVBWAd+oviQzM+uMqgI8IpYA9QXVYmZmXeA7Mc3MEuUANzNLlAPczCxRDnAzs0Q5wM3MElVtN0JLwMhL7yp8nauvOr7wdZpZ1/gM3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUb4T03oN3zFq1jU+AzczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUd0OcEn9JT0maamkZyVdWWRhZmbWsWr6gX8AfCEiNkjqBzwoaX5EPFJQbWZm1oFuB3hEBLAhH+2Xv6KIoszMrLKq2sAl9ZG0BHgduDciHm1jmQZJjZIam5ubq9mcmZmVqCrAI2JTRIwFhgOTJI1uY5nZEVEfEfV1dXXVbM7MzEoU0gslIt4CHgCmFrE+MzOrrJpeKHWSBufDA4AvAiuLKszMzDpWTS+UvYB/k9SH7EDwk4i4s5iyzMyskmp6oTwNjCuwFjMz6wLfiWlmligHuJlZohzgZmaJcoCbmSXKAW5mlih/qbFZF6Xy5cup1Gnd5zNwM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NE+U5MM6upou8Y3Z7uavUZuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJqirAJU2V9CtJz0u6tKiizMyssm4HuKQ+wD8DXwIOAU6TdEhRhZmZWceqOQOfBDwfEasi4kPgFuCrxZRlZmaVKCK690bpa8DUiDg3H/9j4IiIuLBsuQagIR/9DPCr7pfbpqHAGwWvsye4zuKkUCO4zqJtz3XuGxF15ROruZVebUzb4mgQEbOB2VVsp+MipMaIqO+p9RfFdRYnhRrBdRbNdW6pmiaUJmCfkvHhwCvVlWNmZp1VTYA/DoyStJ+kHYE/BP6rmLLMzKySbjehRMRGSRcCdwN9gBsj4tnCKuu8HmueKZjrLE4KNYLrLJrrLNPti5hmZlZbvhPTzCxRDnAzs0Q5wM3MEuWvVOsBkg4C9gYejYgNJdOnRsSC2lX2SZImARERj+ePQZgKrIyI/65xaR2S9KOIOLPWdXRE0pFkdysvi4h7al1PK0lHACsiYr2kAcClwHhgOfD3EfHbmhaYK+nZ9kpE3CfpdOD3gBXA7Ij4qKYF5iTtD0wj61K9EXgOuHlr7cdt5iKmpG9ExA97QR0XAX9K9oM2Frg4In6ez3syIsbXsr5Wki4ne45NX+Be4AjgAeCLwN0R8Xe1q+5jksq7pgr4PHA/QEScuNWLaoOkxyJiUj78TbKfgduBKcAdEXFVLetrJelZYEzei2w28C5wK3BsPv2kmhaYkzSX7GdzZ+AtYBfgNrI6FRFfr2F5wObf9a8AvwC+DCwB1pEF+gUR8UCPFxER28QLeKnWNeR1PAPskg+PBBrJQhzgqVrXV1ZnH7JfkPXAbvn0AcDTta6vpM4ngR8Dk4Fj8n9fzYePqXV9JXU+VTL8OFCXDw8Enql1fSW1rSjdt2XzltS6vpJans7/7QusAfrk4+otP5+tv0P58M7AA/nwiK31u55UE4qkp9ubBeyxNWvpQJ/Im00iYrWkycCtkval7ccP1MrGiNgEvCvphYhYDxAR70lqqXFtpeqBi4HLgD+PiCWS3ouIX9S4rnI7SNqd7LqSIqIZICLekbSxtqV9wrKSv1aXSqqPiEZJBwK9olkit0PejDKQLBwHAW8COwH9allYmb7AJrK6dgWIiJckbZUakwpwspA+juzPlFICFm/9ctr0mqSxEbEEICI2SDoBuBE4rLalfcKHknaOiHeBCa0TJQ0Cek2AR0QLcK2kefm/a+idP7eDgCfIfhZD0p4R8ZqkXehdB+5zgRmS/orsgUsPS3oZeDmf11vMAVaS/ZV4GTBP0irgd8mefNob3AA8LukR4Gjg+wCS6sgONj0uqTZwSXOAH0bEg23M+4+IOL0GZZXXMZzs7Pa1NuZ9LiIeqkFZW5C0U0R80Mb0ocBeEfFMDcqqSNLxwOci4ru1rqUzJO0M7BERL9a6llKSdgV+h+xg2BQRa2pc0hYkDQOIiFckDSa7PvNSRDxW28o+JulQ4GCyi9Urt/r2UwpwMzP7mPuBm5klygFuZpYoB7hZCUmDJV1QMj5M0q21rMmsPW4Dt+2CpD55t8lKy40E7oyI0T1elFmVfAZuvY6kyyT9StJ9km6W9Gf59Ack1efDQyWtzof7SLpa0uOSnpZ0Xj59sqSFkv4DeEbS30i6uGQ7f5ffTVfqKmB/SUvydY6UtCxf/ixJP5N0h6QXJV0o6f9IekrSI5I+lS+3v6QFkp6Q9Mv80QpIOkXSMklLJS3q2b1o24Pe2J/WtmOSJpA9A2Mc2c/nk2T9qztyDvDbiJgoaSfgIUmtzx+ZBIyOiBfzs+vbyPpB75BvZ1LZui7Nlx+b1zOybP7ovLb+wPPAdyJinKRrgTOBfyJ7oP/5EfFc/uyRHwBfAKYDx0XEb/JucWZVcYBbb3MUcHt+g1Fbz0JpyxTgcElfy8cHAaOAD4HHWvtg53fGrpU0juymsKciYm0X61sYEW8Db0v6LXBHPv2ZvIZdyB66NE/afP/OTvm/DwE3SfoJ2YHErCoOcOuN2rsws5GPm/36l0wX8K2IuLt04fwxBu+UreMG4CxgT7K7Y7uq9OanlpLxFrLfpx2At1rP4EtFxPn5GfnxwJL8jt2uHkDMNnMbuPU2i4Bpkgbkdwt+pWTeaj6+7f9rJdPvBv6k9fkTkg6UNLCd9d9O9tjcifn7yr1N/kyL7sifKfOipFPyWiRpTD68f0Q8GhHTyW5j36e72zEDB7j1MhHxJPCfZI/m/Cnwy5LZ15AF9WJgaMn0G8ieZ/1kfsHxX2jnr8uI+BBYCPykrV4p+RnxQ/nFxqu7+THOAM6RtBR4FvhqPv1qSc/kNS4ClnZz/WaAuxFaLyfpCmBDRFxT0Pp2ILswekpEPFfEOs1qxWfgtt1Q9q1DzwP/4/C2bYHPwM3MEuUzcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS9b9I1n9DL4xbgwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "for i in range (len(query_day)):\n",
+    "    if query_day[i] is not NaN:\n",
+    "        plot_day(query_day[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "worst-musical",
+   "metadata": {},
+   "source": [
+    "### Plot query times in a month"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adverse-bruce",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "df = pd.DataFrame(data = {'query times in a day':query_day,'query times in a month':query_month})\n",
-    "df.loc['mean'] = [mean_day,mean_month]\n",
-    "df"
+    "Note: not all users have data on the same number of months. Each graph represents query times for a user in a month."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "incoming-gibson",
+   "execution_count": 13,
+   "id": "removed-sheffield",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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NzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEFfNItdslbZBUk7fsMEmPSVqd/Xto+5ZpZmaFijkCnwtMLFg2A3g8IoYCj2fzZmbWgVoM8Ih4EthcsPgs4NfZ9K+Bs8tcl5mZtaCtY+AfjYg3ALJ/P1K+kszMrBjt/iGmpKmSlkpaWl9f397dmZkdMNoa4OslfRwg+3dDUw0jYk5EVEdEdWVlZRu7MzOzQm0N8AeAi7Ppi4H/U55yzMysWMWcRng38AwwTFKdpMuA64HPSVoNfC6bNzOzDtStpQYRMaWJVRPKXIuZmbWCr8Q0M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0tUSQEu6ZuSXpBUI+luST3LVZiZmTWvzQEuaQDwDaA6IoYDXYELy1WYmZk1r9QhlG7AhyV1Aw4C1pVekpmZFaPNAR4RfwZuBF4H3gDeiYhHy1WYmZk1r5QhlEOBs4DBQH/gYEkXNdJuqqSlkpbW19e3vVIzM9tDKUMopwKvRUR9RGwH7gX+S2GjiJgTEdURUV1ZWVlCd2Zmlq+UAH8dOFHSQZIETABWlacsMzNrSSlj4IuAe4Bngeezbc0pU11mZtaCbqW8OCKuBa4tUy1mZtYKvhLTzCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRJQW4pL6S7pH0kqRVkv6mXIWZmVnzSnqkGnAT8HBEnCupB3BQGWoyM7MitDnAJR0CnAxcAhARHwAflKcsMzNrSSlDKJ8E6oFfSVou6ZeSDi5TXWZm1oJSArwbMAa4JSJGA+8BMwobSZoqaamkpfX19SV0Z2Zm+UoJ8DqgLiIWZfP3kAv0PUTEnIiojojqysrKErozM7N8bQ7wiHgTWCtpWLZoAvBiWaoyM7MWlXoWyt8D87IzUF4FvlJ6SWZmVoySAjwiVgDVZarFzMxawVdimpklygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klquQAl9RV0nJJvy1HQWZmVpxyHIFfCawqw3bMzKwVSgpwSQOBzwO/LE85ZmZWrFKPwP8J+BbQUIZazMysFdoc4JL+DtgQEctaaDdV0lJJS+vr69vanZmZFSjlCHwscKakWmA+cIqkuwobRcSciKiOiOrKysoSujMzs3xtDvCIuDoiBkZEFXAh8EREXFS2yszMrFk+D9zMLFHdyrGRiFgILCzHtszMrDg+AjczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS1QpT6X/hKT/K2mVpBckXVnOwszMrHmlPFJtB/A/I+JZSb2BZZIei4gXy1SbmZk1o5Sn0r8REc9m01uAVcCAchVmZmbNK8sYuKQqYDSwqBzbMzOzlpX8VHpJvYB/B66KiL80sn4qMBVg0KBBpXZndkCrmvFgZ5ewX6m9/vOdXUJJSjoCl9SdXHjPi4h7G2sTEXMiojoiqisrK0vpzszM8pRyFoqA24BVEfGT8pVkZmbFKOUIfCzwZeAUSSuyr78tU11mZtaCNo+BR8RTgMpYi5mZtYKvxDQzS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS1SpDzWeKOmPkl6RNKNcRZmZWctKeahxV+CfgTOAo4Apko4qV2FmZta8Uo7AjwdeiYhXI+IDYD5wVnnKMjOzlrT5ocbAAGBt3nwdcEJhI0lTganZ7LuS/lhCn7anfsDGzi6iJbqhsyuwTuCfzfI6vLGFpQR4Y0+kj70WRMwB5pTQjzVB0tKIqO7sOswK+WezY5QyhFIHfCJvfiCwrrRyzMysWKUE+BJgqKTBknoAFwIPlKcsMzNrSZuHUCJih6SvA48AXYHbI+KFslVmxfDQlO2r/LPZARSx17C1mZklwFdimpklygFuZpYoB7iZWaJKOQ/cOpCkT5O70nUAufPt1wEPRMSqTi3MzDqNj8ATIOnb5G5VIGAxuVM4Bdztm4jZvkzSVzq7hv2Zz0JJgKSXgaMjYnvB8h7ACxExtHMqM2uepNcjYlBn17G/8hBKGhqA/sCfCpZ/PFtn1mkkrWxqFfDRjqzlQOMAT8NVwOOSVvOfNxAbBAwBvt5pVZnlfBQ4HXirYLmApzu+nAOHAzwBEfGwpCPI3cJ3ALlfjDpgSUTs7NTizOC3QK+IWFG4QtLCji/nwOExcDOzRPksFDOzRDnAzcwS5QC3/Z6kvpKuyJsfJ+m3RbxulqRTW9lXraR+banTrLUc4HYg6Atc0WKrAhHx/Yj4XTvUY1YWDnDbZ0iqkvSSpF9KqpE0T9Kpkv5D0mpJx2ftDpN0v6SVkv4gaUS2fKak2yUtlPSqpG9km74e+JSkFZL+MVvWS9I9WX/zJO31iEBJcyWdm03XSvqBpGclPZ/d2gBJFZIelbRc0q3kPWpQ0kWSFmf93iqpq6Tjsrp7SjpY0guShrffXrX9mQPc9jVDgJuAEcCngS8CnwGmAd/J2vwAWB4RI7Jld+S9/tPkzkk+HrhWUndgBrAmIkZFxPSs3Why59cfBXwSGFtEbRsjYgxwS1YPwLXAUxExmtwTqQYBSDoSuAAYGxGjgJ3AlyJiSdbuOuDHwF0RUVPkvjHbg88Dt33NaxHxPICkF4DHIyIkPQ9UZW0+A3wBICKeyI6C+2TrHoyIbcA2SRto+krAxRFRl/WzItv2Uy3Udm/27zJgcjZ98q7piHhQ0q6LWSYAxwJLsoP7DwMbsnWzyN3PZiuw668Es1ZzgNu+ZlvedEPefAP/+fO613AHuTs0Fr5+J03/jBfbrrHXFLZv7GIKAb+OiKsbWXcY0AvoDvQE3iuib7O9eAjFUvQk8CXInVFCbmjjL8203wL07oBazgAOzZY/Dpwr6SPZusMkHZ6tmwN8D5gH3NBOddkBwEfglqKZwK+ymyj9Fbi4ucYRsSn7ILQGeAh4sIy1/IDcbX2fBf4f8HrW54uSvgs8KqkLsB34mqTPAjsi4l8kdQWelnRKRDxRxprsAOFL6c3MEuUhFDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFH/H1fLxShMsElrAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAETCAYAAADNpUayAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaPUlEQVR4nO3deZRU5Z3/8fcHRImCoNAaAbFR0KgozSJmDoEB8ecyE1SIC2TTqCGJOtGcn0xQk0g4OtGMxp+JE5GMDG4jikZjYtwdYhwTWQSxXRHtSAdkdcEFwvL9/VG3SdlW09Vd1TQ8fF7n1Ol7n/vce791Gz51+6l7qxQRmJlZWtq0dgFmZlZ+DnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53G2HJOlSSf/ZyjU8JOnM1qyhpUmqlBSSdmntWqxp5OvcbXsnaThwe0T0aO1aUiepBjg3Ih7P5iuBN4F2EbGx9SqzpvKZu21zPgs0a3kOd9tCUn9Jz0laK+kuSTMkXZEtO0vS0/X6h6Te2fRukq6R9Jak5ZKmSPpMtmy4pFpJ35f0NvBfkqoljcrbVjtJqyRV1dvHHsBDQDdJH2SPbpImSbo961M3dPANSUskvSPp25KOkrRQ0ruSbqi33bMlvZz1fUTSAVm7JF0naYWk97L1+zZwvGZJOjf/+GTH4B1Jb0o6cSvHeqKkxdmxfknS6K30nSRppqTbs/4vSDpY0iVZnUskHZfXv5ukByStkfS6pG/W29bdkm7NtvWipEHZstuAnsBvs+P8r3llfCX73a6SdFlDtdr2w+FuAEjaFbgfuA3YG5gJfKkJm7gaOBioAnoD3YEf5S3/bLbdA4DxwK3AV/OW/xOwLCIW5G80Ij4ETgSWRkSH7LG0gRqOBvoAZwD/D7gMOBY4HDhd0j9mz/UU4FJgDFAB/BG4M9vGccCw7Ll0zra1ushjcDTwKtAV+ClwsyQ10HcxMBToBPwYuF3SflvZ9ihyv5u9gPnAI+T+/3YHJgM35fW9E6gFugGnAv8maWTe8pOAGdnzewC4ASAivga8BYzKjvNP89b5AnAIMBL4kaRDt1KrbQ8iwg8/IBdoS8neh8nangGuyKbPAp6ut06QC3IBHwIH5S37B+DNbHo48Degfd7ybsBaYM9s/h7gXxuobThQW69tErlxeIDKrJbuectXA2fkzd8LXJRNPwSck7esDfARuReeY4DXgM8DbRo5ZrPIjU/XHZ/X85btntX02SKP/wLg5AaWTQIey5sfBXwAtM3mO2b76gzsD2wCOub1/wkwPW9bj+ctOwz4OG++Bjg2b77u2PbIa5sNjG3tf7N+bP3hM3er0w34a2T/ezN/KXLdCnJhNi8bAnkXeDhrr7MyItbVzUTu7Pt/gS9J6kzu7PyOUp4AsDxv+uMC8x2y6QOA6/NqXUPuBap7RDxJ7kz2P4DlkqZK2rPI/b9dNxERH2WTHQp1lPR1SQvyauhL7oy/2Oe2KiI25c3X7asbsCYi1ub1/wu5M/xP1UnuRa19Ee+D1F+n4POy7YfD3eosA7rXG0bomTf9IbkAB0DSZ/OWrSIXMIdHROfs0Ski8gOg0GVZt5AbmjkN+FNE/LWB2sp9SdcS4Ft5tXaOiM9ExDMAEfHziBhIbjjnYGBCOXeeje//CrgA6BIRnYFqci8wpVoK7C2pY15bT6ChY1ufL59LhMPd6vwJ2Ah8V9IuksYAg/OWPw8cLqlKUntyf94DEBGbyYXVdZL2AZDUXdLxjezzfmAAcCG5MfiGLAe6SOrUxOfUkCnAJZIOB5DUSdJp2fRRko6W1I7cC9o6csMc5bQHuRBdme3zG+TO3EsWEUvIDaf9RFJ7SUcC51D8X0XLgQPLUYu1Loe7ARARfyP3BuNZwDvk3kj8dd7y18i9cfc4sAh4ut4mvg+8DvxZ0vtZv0Ma2efH5MbCe+Xvq0C/V8i9SfhGNozRrSnPrcD27iP3BvCMrNZqcsNCAHuSe6F6h9xwxmrgmlL2V2D/LwHXkntBXQ4cQW6IqlzGkRsrXwrcB1weEY8Vue5PgB9kx/niMtZk25hvYrIGSZpO7o3MH7TgPn4EHBwRX220s5kVzTeTWKuRtDe5IYOvtXYtZqnxsIy1iuzGmiXAQxHxVGvXY5YaD8uYmSXIZ+5mZglyuJuZJWi7eEO1a9euUVlZ2dplmJntUObNm7cqIioKLdsuwr2yspK5c+e2dhlmZjsUSQ1+RIiHZczMEuRwNzNLkMPdzCxB28WYu5lt/zZs2EBtbS3r1q1rvLOVVfv27enRowft2rUreh2Hu5kVpba2lo4dO1JZWUnDXzBl5RYRrF69mtraWnr16lX0eh6WMbOirFu3ji5dujjYtzFJdOnSpcl/MTnczaxoDvbW0Zzj7nA3MyvCu+++yy9/+cst80uXLuXUU09txYq2zmPutsOpnPhga5fQomqu+ufWLqEo5f49tNbz3rRpE23btm20X124n3feeQB069aNe+65p6XLazafuZvZDuPKK6/kkEMO4dhjj2XcuHFcc03uS7KGDx++5S73VatWUfdxJps2bWLChAkcddRRHHnkkdx0000AzJo1ixEjRvDlL3+ZI444gh/+8Idcf/31W/Zz2WWX8fOf//wT+544cSKLFy+mqqqKCRMmUFNTQ9++uW9HnD59OqeccgqjRo2iV69e3HDDDfzsZz+jf//+fP7zn2fNmjUALF68mBNOOIGBAwcydOhQXnnlFQBmzpxJ37596devH8OGDSvLsfKZu5ntEObNm8eMGTOYP38+GzduZMCAAQwcOHCr69x888106tSJOXPmsH79eoYMGcJxxx0HwOzZs6murqZXr17U1NQwZswYLrzwQjZv3syMGTOYPXv2J7Z11VVXUV1dzYIFCwCoqan5xPLq6mrmz5/PunXr6N27N1dffTXz58/ne9/7HrfeeisXXXQR48ePZ8qUKfTp04dnn32W8847jyeffJLJkyfzyCOP0L17d959992yHC+Hu5ntEP74xz8yevRodt99dwBOOumkRtd59NFHWbhw4Zbhk/fee49Fixax6667Mnjw4C2XFlZWVtKlSxfmz5/P8uXL6d+/P126dGlSfSNGjKBjx4507NiRTp06MWrUKACOOOIIFi5cyAcffMAzzzzDaaedtmWd9evXAzBkyBDOOussTj/9dMaMGdOk/TbE4W5mO4yGrhrZZZdd2Lx5M8AnLhmMCH7xi19w/PHHf6L/rFmz2GOPPT7Rdu655zJ9+nTefvttzj777CbXtttuu22ZbtOmzZb5Nm3asHHjRjZv3kznzp23nPnnmzJlCs8++ywPPvggVVVVLFiwoMkvLvU1OuYuaZqkFZKq89rukrQge9RIWpC1V0r6OG/ZlJKqMzPLDBs2jPvuu4+PP/6YtWvX8tvf/nbLssrKSubNmwfwiTc5jz/+eG688UY2bNgAwGuvvcaHH35YcPujR4/m4YcfZs6cOZ96MQDo2LEja9eubXb9e+65J7169WLmzJlA7oXn+eefB3Jj8UcffTSTJ0+ma9euLFmypNn7qVPMmft04Abg1rqGiDijblrStcB7ef0XR0RVyZWZmeUZMGAAZ5xxBlVVVRxwwAEMHTp0y7KLL76Y008/ndtuu41jjjlmS/u5555LTU0NAwYMICKoqKjg/vvvL7j9XXfdlREjRtC5c+eCV8906dKFIUOG0LdvX0488UTOP//8Jj+HO+64g+985ztcccUVbNiwgbFjx9KvXz8mTJjAokWLiAhGjhxJv379mrzt+or6DlVJlcDvIqJvvXYBbwHHRMSihvo1ZtCgQeHPc7di+VLI1vHyyy9z6KGHtnYZW0yaNIkOHTpw8cUXl2V7mzdvZsCAAcycOZM+ffqUZZvlVOj4S5oXEYMK9S/1UsihwPKIWJTX1kvSfEl/kDS0oRUljZc0V9LclStXlliGmVnzvfTSS/Tu3ZuRI0dul8HeHKW+oToOuDNvfhnQMyJWSxoI3C/p8Ih4v/6KETEVmAq5M/cS6zCzncykSZPKtq3DDjuMN954o2zb2x40+8xd0i7AGOCuuraIWB8Rq7PpecBi4OBSizQzs6YpZVjmWOCViKita5BUIaltNn0g0AdI6+XQbCdWzHt0Vn7NOe7FXAp5J/An4BBJtZLOyRaN5ZNDMgDDgIWSngfuAb4dEWuaXJWZbXfat2/P6tWrHfDbWN3nubdv375J6zU65h4R4xpoP6tA273AvU2qwMx2CD169KC2thZfALHt1X0TU1P4DlUzK0q7du2a9E1A1rr8qZBmZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZglyuJuZJajRr9mTNA34IrAiIvpmbZOAbwJ1X6Z4aUT8Plt2CXAOsAn4bkQ80gJ1l6Ry4oOtXUKLqrnqn1u7BDNrZcV8h+p04Abg1nrt10XENfkNkg4DxgKHA92AxyUdHBGbylCrme3gfGK17TQ6LBMRTwFritzeycCMiFgfEW8CrwODS6jPzMyaoZQx9wskLZQ0TdJeWVt3YElen9qszczMtqHmhvuNwEFAFbAMuDZrV4G+UWgDksZLmitp7sqVKwt1MTOzZmpWuEfE8ojYFBGbgV/x96GXWmD/vK49gKUNbGNqRAyKiEEVFRXNKcPMzBrQrHCXtF/e7GigOpt+ABgraTdJvYA+wOzSSjQzs6Yq5lLIO4HhQFdJtcDlwHBJVeSGXGqAbwFExIuS7gZeAjYC5/tKGTOzba/RcI+IcQWab95K/yuBK0spyszMSuM7VM3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBjYa7pGmSVkiqzmv7d0mvSFoo6T5JnbP2SkkfS1qQPaa0ZPFmZlZYMWfu04ET6rU9BvSNiCOB14BL8pYtjoiq7PHt8pRpZmZN0Wi4R8RTwJp6bY9GxMZs9s9AjxaozczMmqkcY+5nAw/lzfeSNF/SHyQNLcP2zcysiXYpZWVJlwEbgTuypmVAz4hYLWkgcL+kwyPi/QLrjgfGA/Ts2bOUMszMrJ5mn7lLOhP4IvCViAiAiFgfEauz6XnAYuDgQutHxNSIGBQRgyoqKppbhpmZFdCscJd0AvB94KSI+CivvUJS22z6QKAP8EY5CjUzs+I1Oiwj6U5gONBVUi1wObmrY3YDHpME8OfsyphhwGRJG4FNwLcjYk3BDZuZWYtpNNwjYlyB5psb6HsvcG+pRZmZWWl8h6qZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpYgh7uZWYIc7mZmCXK4m5klyOFuZpagRsNd0jRJKyRV57XtLekxSYuyn3vlLbtE0uuSXpV0fEsVbmZmDSvmzH06cEK9tonAExHRB3gim0fSYcBY4PBsnV9Kalu2as3MrCiNhntEPAWsqdd8MnBLNn0LcEpe+4yIWB8RbwKvA4PLVKuZmRWpuWPu+0bEMoDs5z5Ze3dgSV6/2qzNzMy2oXK/oaoCbVGwozRe0lxJc1euXFnmMszMdm7NDfflkvYDyH6uyNprgf3z+vUAlhbaQERMjYhBETGooqKimWWYmVkhzQ33B4Azs+kzgd/ktY+VtJukXkAfYHZpJZqZWVPt0lgHSXcCw4GukmqBy4GrgLslnQO8BZwGEBEvSrobeAnYCJwfEZtaqHYzM2tAo+EeEeMaWDSygf5XAleWUpSZmZXGd6iamSXI4W5mliCHu5lZghzuZmYJcribmSXI4W5mliCHu5lZghzuZmYJcribmSXI4W5mliCHu5lZghzuZmYJcribmSXI4W5mliCHu5lZghzuZmYJcribmSXI4W5mliCHu5lZghr9DtWGSDoEuCuv6UDgR0Bn4JvAyqz90oj4fbMrNDOzJmt2uEfEq0AVgKS2wF+B+4BvANdFxDVlqdDMzJqsXMMyI4HFEfGXMm3PzMxKUK5wHwvcmTd/gaSFkqZJ2qtM+zAzsyKVHO6SdgVOAmZmTTcCB5EbslkGXNvAeuMlzZU0d+XKlYW6mJlZM5XjzP1E4LmIWA4QEcsjYlNEbAZ+BQwutFJETI2IQRExqKKiogxlmJlZnXKE+zjyhmQk7Ze3bDRQXYZ9mJlZEzT7ahkASbsD/wf4Vl7zTyVVAQHU1FtmZmbbQEnhHhEfAV3qtX2tpIrMzKxkvkPVzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLkMPdzCxBDnczswQ53M3MEuRwNzNLUElfkC2pBlgLbAI2RsQgSXsDdwGVQA1wekS8U1qZZmbWFOU4cx8REVURMSibnwg8ERF9gCeyeTMz24ZaYljmZOCWbPoW4JQW2IeZmW1FqeEewKOS5kkan7XtGxHLALKf+5S4DzMza6KSxtyBIRGxVNI+wGOSXil2xezFYDxAz549SyzDzMzylXTmHhFLs58rgPuAwcBySfsBZD9XNLDu1IgYFBGDKioqSinDzMzqaXa4S9pDUse6aeA4oBp4ADgz63Ym8JtSizQzs6YpZVhmX+A+SXXb+e+IeFjSHOBuSecAbwGnlV6mmZk1RbPDPSLeAPoVaF8NjCylKDMzK43vUDUzS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MENTvcJe0v6X8kvSzpRUkXZu2TJP1V0oLs8U/lK9fMzIrR7C/IBjYC/zcinpPUEZgn6bFs2XURcU3p5ZmZWXM0O9wjYhmwLJteK+lloHu5CjMzs+Yry5i7pEqgP/Bs1nSBpIWSpknaqxz7MDOz4pUc7pI6APcCF0XE+8CNwEFAFbkz+2sbWG+8pLmS5q5cubLUMszMLE9J4S6pHblgvyMifg0QEcsjYlNEbAZ+BQwutG5ETI2IQRExqKKiopQyzMysnlKulhFwM/ByRPwsr32/vG6jgerml2dmZs1RytUyQ4CvAS9IWpC1XQqMk1QFBFADfKukCs3MrMlKuVrmaUAFFv2++eWYmVk5+A5VM7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQQ53M7MEOdzNzBLkcDczS5DD3cwsQS0W7pJOkPSqpNclTWyp/ZiZ2ae1SLhLagv8B3AicBgwTtJhLbEvMzP7tJY6cx8MvB4Rb0TE34AZwMkttC8zM6tnlxbabndgSd58LXB0fgdJ44Hx2ewHkl5toVq2B12BVdtqZ7p6W+1pp+Hf344r9d/dAQ0taKlwV4G2+MRMxFRgagvtf7siaW5EDGrtOqx5/Pvbce3Mv7uWGpapBfbPm+8BLG2hfZmZWT0tFe5zgD6SeknaFRgLPNBC+zIzs3paZFgmIjZKugB4BGgLTIuIF1tiXzuInWL4KWH+/e24dtrfnSKi8V5mZrZD8R2qZmYJcribmSXI4W5mlqCWus59pybpc+TuyO1O7vr+pcADEfFyqxZmlrjs/1534NmI+CCv/YSIeLj1Ktv2fOZeZpK+T+7jFgTMJndZqIA7/QFqOzZJ32jtGqxhkr4L/Ab4F6BaUv5Hnvxb61TVeny1TJlJeg04PCI21GvfFXgxIvq0TmVWKklvRUTP1q7DCpP0AvAPEfGBpErgHuC2iLhe0vyI6N+qBW5jHpYpv81AN+Av9dr3y5bZdkzSwoYWAftuy1qsydrWDcVERI2k4cA9kg6g8EeiJM3hXn4XAU9IWsTfPzytJ9AbuKDVqrJi7QscD7xTr13AM9u+HGuCtyVVRcQCgOwM/ovANOCI1i1t23O4l1lEPCzpYHIfe9ydXCjUAnMiYlOrFmfF+B3QoS4g8kmate3LsSb4OrAxvyEiNgJfl3RT65TUejzmbmaWIF8tY2aWIIe7mVmCHO6205LUWdJ5efPDJf2uiPUmSzq2ifuqkdS1OXWaNYfD3XZmnYHzGu1VT0T8KCIeb4F6zMrG4W7bPUmVkl6R9J+SqiXdIelYSf8raZGkwVm/vSXdL2mhpD9LOjJrnyRpmqRZkt7I7mQEuAo4SNICSf+etXWQdE+2vzskfer6aEnTJZ2aTddI+rGk5yS9kN3+jqQukh6VND+7UkN5639V0uxsvzdJaivpqKzu9pL2kPSipL4td1QtdQ5321H0Bq4HjgQ+B3wZ+AJwMXBp1ufHwPyIODJruzVv/c+Ru359MHC5pHbARGBxRFRFxISsX39y9yocBhwIDCmitlURMQC4MasH4HLg6eyuyAfI3euApEOBM4AhEVEFbAK+EhFzsn5XAD8Fbo+I6iKPjdmn+Dp321G8GREvAEh6EXgiIiK75bwy6/MF4EsAEfFkdvbcKVv2YESsB9ZLWkHDd5vOjojabD8Lsm0/3Uhtv85+zgPGZNPD6qYj4kFJdTdFjQQGAnOyPwo+A6zIlk0m91lE64C6vy7MmsXhbjuK9XnTm/PmN/P3f8eFbjGvu5Ejf/1NNPxvv9h+hdap37/QTSQCbomISwos2xvoALQD2gMfFrFvs4I8LGMpeQr4CuSufCE3XPL+VvqvBTpug1pOBPbK2p8ATpW0T7Zs7+yzTyD3fZ8/BO4Arm6humwn4TN3S8kk4L+yD//6CDhza50jYnX2pmw18BDwYBlr+TG5j3l+DvgD8Fa2z5ck/QB4VFIbYANwvqR/BDZGxH9Lags8I+mYiHiyjDXZTsQfP2BmliAPy5iZJcjhbmaWIIe7mVmCHO5mZglyuJuZJcjhbmaWIIe7mVmCHO5mZgn6/1NzxCLTPFwfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "df.plot(kind = 'bar')"
+    "for i in range (len(query_month)):\n",
+    "    if query_month[i] is not NaN:\n",
+    "        query_month_df = pd.DataFrame(data = {'query times':query_month[i]})\n",
+    "        query_month_df.plot(xlabel='month index',kind = 'bar',title='query times in a month')"
    ]
   }
  ],
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
index a4af2bd..c74fe3c 100644
--- a/tour_model_eval/v-measurel_bins_all_user.ipynb
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hungry-polish",
+   "id": "spoken-acrobat",
    "metadata": {
     "scrolled": true
    },
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "coupled-transportation",
+   "id": "solid-decimal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "interstate-queue",
+   "id": "acute-departure",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "norwegian-victoria",
+   "id": "practical-flour",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "loved-estonia",
+   "id": "indirect-retrieval",
    "metadata": {},
    "source": [
     "## Bins above cutoff"
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "laughing-macintosh",
+   "id": "deadly-microwave",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "amber-photograph",
+   "id": "strange-dining",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "collected-throat",
+   "id": "black-mobility",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "headed-dating",
+   "id": "composite-possibility",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -104,7 +104,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "blank-arnold",
+   "id": "wanted-mustang",
    "metadata": {
     "scrolled": true
    },
@@ -116,7 +116,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "maritime-twins",
+   "id": "productive-palestine",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "consistent-kuwait",
+   "id": "heard-florist",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -134,7 +134,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "confused-sensitivity",
+   "id": "smaller-creek",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "advance-passing",
+   "id": "square-importance",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +153,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bronze-operations",
+   "id": "approximate-groove",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -162,7 +162,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "pacific-reflection",
+   "id": "studied-saint",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +176,100 @@
   },
   {
    "cell_type": "markdown",
-   "id": "documented-vacation",
+   "id": "wrapped-rebate",
+   "metadata": {},
+   "source": [
+    "#### homogeneity_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "suburban-insertion",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
+    "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
+    "                           index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                                  'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "everyday-conditioning",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "engaged-injury",
+   "metadata": {},
+   "source": [
+    "#### completeness_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "mighty-consolidation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
+    "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
+    "                      index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                                  'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "christian-revelation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "lovely-particle",
+   "metadata": {},
+   "source": [
+    "#### v_measure_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "documented-perfume",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
+    "                          'after converting purposes and replaced mode':v_score_cvt},\n",
+    "                   index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                                  'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "published-kruger",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-cement",
    "metadata": {},
    "source": [
     "## All bins"
@@ -184,7 +277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "selected-compiler",
+   "id": "wrapped-channels",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -193,7 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "tight-contract",
+   "id": "assigned-pearl",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +296,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "regular-shadow",
+   "id": "rotary-macro",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +305,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brutal-trinidad",
+   "id": "communist-harvard",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -221,7 +314,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "decent-machinery",
+   "id": "median-speed",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +324,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "lasting-synthetic",
+   "id": "present-installation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +333,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sexual-opportunity",
+   "id": "armed-former",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -249,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "periodic-freedom",
+   "id": "august-maintenance",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +352,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "stylish-thailand",
+   "id": "shaped-strip",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +361,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "floppy-theory",
+   "id": "gentle-plenty",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -277,7 +370,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "british-working",
+   "id": "architectural-perspective",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -288,6 +381,99 @@
     "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
     "all_df"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "periodic-password",
+   "metadata": {},
+   "source": [
+    "#### homogeneity_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "theoretical-oliver",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_homo_df=pd.DataFrame(data={'original user input':ab_homo_score_ori,'after translation':ab_homo_score_sp2en,\n",
+    "                              'after converting purposes and replaced mode':ab_homo_score_cvt},\n",
+    "                        index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                               'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "seasonal-coalition",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "outdoor-pavilion",
+   "metadata": {},
+   "source": [
+    "#### completeness_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "activated-schedule",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_comp_df = pd.DataFrame(data={'original user input':ab_comp_score_ori,'after translation':ab_comp_score_sp2en,\n",
+    "                                'after converting purposes and replaced mode':ab_comp_score_cvt},\n",
+    "                          index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                                 'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "graphic-revision",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "golden-philadelphia",
+   "metadata": {},
+   "source": [
+    "#### v_measure_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "relevant-nebraska",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_v_df = pd.DataFrame(data={'original user input':ab_v_score_ori,'after translation':ab_v_score_sp2en,\n",
+    "                             'after converting purposes and replaced mode':ab_v_score_cvt},\n",
+    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                              'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "assisted-franchise",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ab_v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
   }
  ],
  "metadata": {
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
index 67a0f56..f44bfdc 100644
--- a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adverse-recipient",
+   "id": "secondary-armor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "pleased-fence",
+   "id": "smooth-reviewer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "global-leave",
+   "id": "accessible-jaguar",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hundred-surge",
+   "id": "endangered-device",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "funky-darkness",
+   "id": "prescription-disease",
    "metadata": {},
    "source": [
     "## Evaluate clusters above cutoff based on silhouette_score"
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "critical-prime",
+   "id": "fatal-castle",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -74,7 +74,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "copyrighted-vehicle",
+   "id": "federal-convertible",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "stainless-exposure",
+   "id": "polyphonic-astronomy",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "returning-skiing",
+   "id": "later-share",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "authorized-complement",
+   "id": "lesbian-realtor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "hispanic-drunk",
+   "id": "defensive-numbers",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "united-grade",
+   "id": "amended-girlfriend",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -130,7 +130,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "outer-usage",
+   "id": "synthetic-debate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "positive-grass",
+   "id": "finite-ireland",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "assured-oriental",
+   "id": "inclusive-champagne",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -158,7 +158,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "authentic-compression",
+   "id": "protective-appraisal",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,6 +169,101 @@
     "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
     "df"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "intended-campaign",
+   "metadata": {},
+   "source": [
+    "#### homogeneity_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "robust-matthew",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
+    "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
+    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                              'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "surrounded-karma",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "furnished-valve",
+   "metadata": {},
+   "source": [
+    "#### completeness_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "regulation-storage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
+    "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
+    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                              'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "liked-palestine",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "gorgeous-denver",
+   "metadata": {},
+   "source": [
+    "#### v_measure_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "quiet-capital",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
+    "                          'after converting purposes and replaced mode':v_score_cvt},\n",
+    "                    index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
+    "                           'user9','user10','user11','user12','user13'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "behavioral-embassy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+   ]
   }
  ],
  "metadata": {

From 11f018beca808057d4bf40d124919d0b48af3fe5 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Tue, 9 Mar 2021 04:15:27 -0800
Subject: [PATCH 10/16] modified evaluation code, got subplots for query times

---
 .../confirmed_trips_eval_bins_clusters.py     |   51 +-
 tour_model_eval/query_times_all_users.ipynb   | 1018 ++++-------------
 .../v-measurel_all_bins_single_user.ipynb     |  108 +-
 .../v-measurel_bins_all_user.ipynb            |  120 +-
 ...urel_clusters_above_cutoff_all_users.ipynb |   69 +-
 .../v-measurel_cutoff_bins_single_user.ipynb  |    6 +-
 ...measurel_cutoff_clusters_single_user.ipynb |   10 +-
 7 files changed, 413 insertions(+), 969 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index 4f9652d..a538910 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -22,15 +22,31 @@
 map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',
                'insurance_payment':'insurance'}
 
+def get_user_ls(all_users):
+    user_ls = []
+    for i in range(len(all_users)):
+        curr_user = 'user' + str(i + 1)
+        user_ls.append(curr_user)
+    return user_ls
+
 
 def filter_data(user,radius):
     trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
     non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
-    valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t["data"]["user_input"] and
-                   'purpose_confirm' in t["data"]["user_input"] and 'replaced_mode' in t["data"]["user_input"]]
+    non_empty_trips_df = pd.DataFrame(t["data"]["user_input"] for t in non_empty_trips)
+    valid_trips_df = non_empty_trips_df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
+    valid_trips_idx_ls = valid_trips_df.index.tolist()
+    valid_trips = [non_empty_trips[i]for i in valid_trips_idx_ls]
+
     sim = similarity.similarity(valid_trips, radius)
     filter_trips = sim.data
-    return filter_trips,sim
+    return filter_trips,sim,trips
+
+def valid_user(filter_trips,trips):
+    valid = False
+    if len(filter_trips) >= 10 and len(filter_trips) / len(trips) >= 0.5:
+        valid = True
+    return valid
 
 
 # v_measure_bins takes 5 parameters
@@ -45,10 +61,11 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
     v_score = []
     for i in range(len(all_users)):
         user = all_users[i]
-        filter_trips,sim = filter_data(user,radius)
+        filter_trips,sim,trips = filter_data(user,radius)
 
-        # filter out users that haven't enough trips (at least 10) to analyze
-        if len(filter_trips) < 10:
+        # filter out users that haven't enough trips (at least 10 valid trips
+        # and 50% of total trips are valid) to analyze
+        if not valid_user(filter_trips,trips):
             homo_score.append(NaN)
             comp_score.append(NaN)
             v_score.append(NaN)
@@ -67,11 +84,6 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
             bin_trips = sim.newdata
             bins = sim.bins
 
-            if len(bin_trips) < 10:
-                homo_score.append(NaN)
-                comp_score.append(NaN)
-                v_score.append(NaN)
-                continue
         bin_trips_df = pd.DataFrame(data=[trip["data"]["user_input"] for trip in bin_trips])
 
         if sp2en:
@@ -111,7 +123,7 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
             for index in bin:
                 bin_ls.append(index)
         bins_ts = pd.DataFrame(data=[filter_trips[i]["data"]["start_ts"] for i in bin_ls])
-        # compare two data frames, return nothing if two data frames are the same
+        # compare two data frames, the program will continue to score calculation if two data frames are the same
         assert_frame_equal(bins_ts, bin_trips_ts)
         homo = metrics.homogeneity_score(labels_true, labels_pred)
         homo_score.append(float('%.3f' % homo))
@@ -134,10 +146,10 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
     v_score = []
     for i in range(len(all_users)):
         user = all_users[i]
-        filter_trips,sim = filter_data(user,radius)
+        filter_trips,sim,trips = filter_data(user,radius)
 
-        # filter out users that haven't enough trips (at least 10) to analyze
-        if len(filter_trips) < 10:
+        # filter out users that haven't enough trips to analyze
+        if not valid_user(filter_trips,trips):
             homo_score.append(NaN)
             comp_score.append(NaN)
             v_score.append(NaN)
@@ -147,12 +159,6 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
         bin_trips = sim.newdata
         bins = sim.bins
 
-        if len(bin_trips) < 10:
-            homo_score.append(NaN)
-            comp_score.append(NaN)
-            v_score.append(NaN)
-            continue
-
         # clustering the data only based on sil score (min_cluster = 0) instead of bins number (len(bins))
         feat = featurization.featurization(bin_trips)
         min = 0
@@ -186,7 +192,8 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
                 labels_true.append(no_dup_list.index(trip))
         labels_pred = feat.labels
 
-        # compare the points in cluster_trips and those in feat.points, return nothing if two data frames are the same
+        # compare the points in cluster_trips and those in feat.points, the program will continue to score calculation
+        # if the frames are the same
         cluster_ps = []
         for trip in cluster_trips:
             cluster_ps.append([trip["data"]["start_loc"]["coordinates"][0],
diff --git a/tour_model_eval/query_times_all_users.ipynb b/tour_model_eval/query_times_all_users.ipynb
index 20164ba..a15e626 100644
--- a/tour_model_eval/query_times_all_users.ipynb
+++ b/tour_model_eval/query_times_all_users.ipynb
@@ -2,21 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "pending-avatar",
+   "execution_count": null,
+   "id": "inner-sacramento",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "storage not configured, falling back to sample, default configuration\n",
-      "Connecting to database URL localhost\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import logging\n",
     "\n",
@@ -33,13 +24,15 @@
     "from sklearn import metrics\n",
     "from pandas.testing import assert_frame_equal\n",
     "import matplotlib.pyplot as plt\n",
-    "from matplotlib.ticker import MaxNLocator"
+    "from matplotlib.ticker import MaxNLocator\n",
+    "import numpy as np\n",
+    "# import matplotlib.ticker as ticker"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "handmade-burning",
+   "execution_count": null,
+   "id": "subjective-cruise",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,8 +42,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "outstanding-representation",
+   "execution_count": null,
+   "id": "posted-broadcasting",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,8 +52,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "needed-backing",
+   "execution_count": null,
+   "id": "lesbian-employee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,8 +62,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "pacific-ranking",
+   "execution_count": null,
+   "id": "wound-thomson",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,8 +72,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "minute-anniversary",
+   "execution_count": null,
+   "id": "destroyed-attention",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,8 +87,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "chief-renewal",
+   "execution_count": null,
+   "id": "breeding-cream",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,43 +102,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "iraqi-festival",
+   "execution_count": null,
+   "id": "formed-drive",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_day(query_day_ls):    \n",
-    "    query_day_ls_df = pd.DataFrame(data = query_day_ls)\n",
-    "    query_day_df=query_day_ls_df.value_counts(sort = False).rename_axis('query times').to_frame('frequecy').reset_index()\n",
-    "    query_day_df.set_index(['query times'], inplace=True)\n",
-    "    query_day=query_day_df.plot(kind = 'bar',title='query times in a day')\n",
-    "    query_day.yaxis.set_major_locator(MaxNLocator(integer=True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "stopped-tractor",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(len(all_users)):\n",
-    "    user = all_users[i]\n",
-    "    filter_trips,sim = evaluation.filter_data(user,radius)\n",
+    "# build a base dataframe for query times in January \n",
+    "date_df = pd.DataFrame(data = {'date':np.arange(1,32),'drop_col':np.arange(1,32)})\n",
+    "date_df.set_index(['date'], inplace=True)\n",
+    "\n",
+    "\n",
+    "for a in range(len(all_users)):\n",
+    "    user = all_users[a]\n",
+    "    filter_trips,sim,trips = evaluation.filter_data(user,radius)\n",
     "    logging.debug(\"len(filter_trips)is %s \"% len(filter_trips))\n",
     "\n",
-    "    # filter out users that don't have valid labeled trips\n",
-    "    if len(filter_trips) == 0:\n",
+    "    # filter out users that don't have enough valid labeled trips\n",
+    "    if not evaluation.valid_user(filter_trips,trips):\n",
     "        query_day.append(NaN)\n",
-    "        query_month.append(NaN)\n",
+    "        query_month.append(NaN)      \n",
     "        continue\n",
     "        \n",
     "    sim.bin_data()\n",
     "    sim.delete_bins()\n",
     "    bins = sim.bins\n",
     "    \n",
-    "    # collect query trips indices above cutoff\n",
+    "    # collect query trips and common trips(no need to query) indices above cutoff\n",
     "    ab_trip_ls = []\n",
+    "    no_query_trip_ls = []\n",
     "    for bin in bins:\n",
     "        early_trip = filter_trips[bin[0]]\n",
     "        trip_index = 0\n",
@@ -161,6 +145,10 @@
     "                early_trip = compare_trip\n",
     "                trip_index = i\n",
     "        ab_trip_ls.append(bin[trip_index])\n",
+    "        \n",
+    "        for k in range(len(bin)):\n",
+    "            if k != trip_index:\n",
+    "                no_query_trip_ls.append(bin[k])\n",
     "\n",
     "\n",
     "    \n",
@@ -174,7 +162,8 @@
     "            bl_trip_ls.append(index)\n",
     "            \n",
     "    # whole list of query trips indices\n",
-    "    query_trips_ls=ab_trip_ls+bl_trip_ls    \n",
+    "    query_trips_ls=ab_trip_ls+bl_trip_ls\n",
+    "    \n",
     "    \n",
     "    # collect query times in a day\n",
     "    bin_day = []\n",
@@ -190,11 +179,23 @@
     "    query_day_ls = []\n",
     "    for bin in bin_day:\n",
     "        query_day_ls.append(len(bin))\n",
+    "        \n",
+    "    # collect 0 query days \n",
+    "    for trip_index in no_query_trip_ls:\n",
+    "        trip = filter_trips[trip_index]\n",
+    "        match = False\n",
+    "        for bin in bin_day:\n",
+    "            if match_day(trip,bin):\n",
+    "                match = True\n",
+    "                break\n",
+    "        if not match:\n",
+    "            query_day_ls.append(0)\n",
+    "            \n",
     "\n",
     "    # collect query times in a day for every user\n",
     "    query_day.append(query_day_ls)\n",
     "    \n",
-    "    \n",
+    "   \n",
     "    # collect query times in a month\n",
     "    bin_month = []\n",
     "    for trip_index in query_trips_ls:\n",
@@ -211,652 +212,120 @@
     "        query_month_ls.append(len(bin))\n",
     "\n",
     "    # collect query times in a month for every user\n",
-    "    query_month.append(query_month_ls)\n"
+    "    query_month.append(query_month_ls)\n",
+    "    \n",
+    "    # select the trips that are in Jan 2021\n",
+    "    jan_trips = []\n",
+    "    for trip_index in query_trips_ls:\n",
+    "        if filter_trips[trip_index]['data']['start_local_dt']['year']==2021 and filter_trips[trip_index]['data']['start_local_dt']['month']==1:\n",
+    "            jan_trips.append(trip_index)\n",
+    "    \n",
+    "    # create the data frame for query times in Jan 2021\n",
+    "    date = []\n",
+    "    for trip_index in jan_trips:\n",
+    "        trip_date = filter_trips[trip_index]['data']['start_local_dt']['day']\n",
+    "        date.append(trip_date)\n",
+    "        new_date_df = pd.DataFrame(data = date)\n",
+    "        new_date_df=new_date_df.value_counts(sort = False).rename_axis('date').to_frame('user'+str(a+1)).reset_index()\n",
+    "        new_date_df.set_index(['date'], inplace=True)\n",
+    "    date_df = date_df.join(new_date_df,how='outer')\n",
+    "date_df=date_df.drop(columns=['drop_col'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "victorian-rating",
+   "execution_count": null,
+   "id": "functional-proposal",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "  4,\n",
-       "  5,\n",
-       "  8,\n",
-       "  3,\n",
-       "  1]]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "query_day"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "victorian-venue",
+   "execution_count": null,
+   "id": "logical-adolescent",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[26, 61, 18, 7, 12],\n",
-       " nan,\n",
-       " [36, 52, 75],\n",
-       " [42, 28, 25],\n",
-       " [2, 3, 2],\n",
-       " [14, 38, 50],\n",
-       " [18, 7, 24, 1],\n",
-       " [131, 148, 4],\n",
-       " [71, 91, 28, 8],\n",
-       " [48, 50, 47],\n",
-       " [16, 2],\n",
-       " nan,\n",
-       " [153, 182, 152]]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "query_month"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "seven-istanbul",
+   "id": "ruled-dictionary",
    "metadata": {},
    "source": [
     "### Plot query times in a day"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "supreme-search",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "biological-middle",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Note: frequecy represents the number of days have specific query times. Each graph represents query times for a user"
+    "# show query times in a data frame\n",
+    "base = 0\n",
+    "for i in range (len(query_day)):\n",
+    "    if query_day[i] is not NaN:\n",
+    "            query_day_ls_df = pd.DataFrame(data = query_day[i])\n",
+    "            query_day_df=query_day_ls_df.value_counts(sort = False).rename_axis('query times').to_frame('user'+str(i+1)).reset_index()\n",
+    "            query_day_df.set_index(['query times'], inplace=True)\n",
+    "            base = i\n",
+    "            break\n",
+    "            \n",
+    "for i in range (base+1,len(query_day)):\n",
+    "    if query_day[i] is not NaN:\n",
+    "        new_day_df = pd.DataFrame(data = query_day[i])\n",
+    "        new_day_df = new_day_df.value_counts(sort = False).rename_axis('query times').to_frame('user'+str(i+1)).reset_index()\n",
+    "        new_day_df.set_index(['query times'], inplace=True)\n",
+    "        query_day_df=query_day_df.join(new_day_df,how='outer',sort='query times')\n",
+    "        \n",
+    "query_day_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "corresponding-graphics",
+   "execution_count": null,
+   "id": "revised-venice",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot a graph for all valid users\n",
+    "yticks_max = query_day_df.max().max()\n",
+    "graph_day = query_day_df.plot(kind='bar',figsize=(14,16),title='query times in a day',fontsize=18,yticks=np.arange(0, yticks_max+4, step=4))\n",
+    "graph_day.title.set_size(20)\n",
+    "plt.xlabel('query times',fontsize=16)\n",
+    "plt.ylabel('days', fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "requested-canyon",
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
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-     "data": {
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WKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiKg5wSX0kPSTptiIKMjOzziliD3wGsK6AdszMrAsqCnBJw4FpwNXFlGNmZp1V6R74t4B/ApoLqMXMzLqg21+pJuk04LmIWClpYjvzNQANALW1td1dnFmv4a8qs96ikj3w9wKnS9oI3AC8X9J15TNFxPyIqI+I+pqamgoWZ2Zmpbod4BHxzxExPCLqgI8CSyLik4VVZmZm7fJ54GZmiep2H3ipiLgbuLuItszMrHO8B25mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mlqhuB7ikd0paKmmdpEclzSiyMDMza1/fCl67A/j7iFgl6SBgpaSfR8TagmozM7N2dHsPPCKejYhV+fA2YB1wWFGFmZlZ+wrpA5dUB4wB7i+iPTMz61glXSgASDoQ+AnwtxGxtZXpDUADQG1tbaWLM7O9TN3M2wttb+OV0wptD4qvEYqps6I9cEn9yMJ7QUTc1No8ETE/Iuojor6mpqaSxZmZWYlKzkIRcA2wLiL+o7iSzMysMyrZA38v8Cng/ZJW548PFVSXmZl1oNt94BFxD6ACazEzsy7wlZhmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZomqKMAlTZX0G0lPSJpZVFFmZtaxbge4pD7At4EPAqOAj0kaVVRhZmbWvkr2wE8EnoiI9RHxGnADcEYxZZmZWUcUEd17ofRhYGpEXJA//xQwISI+VzZfA9CQP3038Jvul9uqocDzBbe5J7jO4qRQI7jOor2V6zw8ImrKR/atoEG1Mu5NW4OImA/Mr2A57RchrYiI+j3VflFcZ3FSqBFcZ9Fc55tV0oXSCLyz5Plw4JnKyjEzs86qJMAfBEZKGiFpX+CjwM+KKcvMzDrS7S6UiNgh6XPAHUAf4HsR8WhhlXXeHuueKZjrLE4KNYLrLJrrLNPtg5hmZlZdvhLTzCxRDnAzs0Q5wM3MEuUA3wMkHS1pkqQDy8ZPrVZNrZF0oqTx+fAoSX8n6UPVrqsjkn5Y7Ro6IumkfH1OrnYtpSRNkDQwHx4g6QpJt0r6uqRB1a6vhaRLJL2z4zmrS9IRkv5B0hxJ/y7pop5cj3vNQUxJn4mI7/eCOi4B/gZYB5wAzIiIW/JpqyJibDXrayHpMrL72PQFfg5MAO4GPgDcERFfq151b5BUfmqqgPcBSwAi4vQeL6oVkh6IiBPz4b8i+xu4GZgM3BoRV1azvhaSHgVG52eRzQdeAm4EJuXjz6xqgTlJfwReBJ4ErgcWRsSW6la1u/x//S+A/wU+BKwGXgCmAxdHxN17vIiI2CsewFPVriGv4xHgwHy4DlhBFuIAD1W7vrI6+wD7A1uBgfn4AcCvq11fSZ2rgOuAicCp+c9n8+FTq11fSZ0PlQw/CNTkwwcAj1S7vpLa1pWu27Jpq6tdX+n6JOshmAxcA2wBFgOfBg6qdn15jY8AffLh/YG78+Hanvpfr+RS+h4n6ddtTQIO6cla2tEnIrYDRMRGSROBGyUdTuu3H6iWHRGxE3hJ0pMRsRUgIl6W1Fzl2krVAzOAS4F/jIjVkl6OiP+tcl3l9pF0MFnoKPK9xYh4UdKO6pa2mzUln1YfllQfESskHQW8Xu3iSkRENAN3AndK6kf2ifFjwGzgTfcFqZK+wE5gP+AggIh4Kq+3RxaekkOAKWQfU0oJWN7z5bRqk6QTImI1QERsl3Qa8D3g+OqWtpvXJO0fES8B41pG5v13vSbA83/ib0pamP/cTO/8ux0ErCT7WwxJh0bEpvw4SG/acF8AzJH0L2Q3XLpP0tPA0/m03mK3dRYRr5Nd6f0zSQOqU9KbXA08KOlXwCnA1wEk1QC/74kCkuoDl3QN8P2IuKeVaf8TER+vQlnldQwn27vd1Mq090bEvVUo600k7RcRr7Yyfijwjoh4pApldUjSNOC9EfGlatfSGZL2Bw6JiA3VrqWUpIOAd5FtDBsjYnOVS9qNpKMi4rfVrqMjko4FjgHWRMRjPb78lALczMze4NMIzcwS5QA3M0uUA9yshKTBki4ueT5M0o3VrMmsLe4Dt7cESX3y0yY7mq8OuC0ijtvjRZlVyHvg1utIulTSbyTdJel6Sf+Qj79bUn0+PFTSxny4j6RvSHpQ0q8lXZiPnyhpqaT/AR6R9BVJM0qW87X8arpSVwJHSFqdt1knaU0+/3mSfppfer5B0ufyy+UfkvQrSW/L5ztC0mJJKyX9UtLR+fizJa2R9LCkZXt2LdpbQW88n9bewiSNI/t2pzFkf5+ryM6vbs/5wB8jYryk/YB7Jd2ZTzsROC4iNuR71zeRnQe9T76cE8vampnPf0JeT13Z9OPy2voDTwBfjIgxkr4JnAt8i+yG/hdFxOOSJgDfAd4PzAKmRMTvJA3u5Coxa5MD3Hqbk4Gb8wuMWrsXSmsmA++R9OH8+SBgJPAa8EDLOdj5lbFNksaQXRT2UEQ0dbG+pRGxDdiW36/j1nz8I3kNBwJ/BiyUdl2Lsl/+817gWkk/JtuQmFXEAW69UVsHZnbwRrdf/5LxAj4fEXeUzpzfxuDFsjauBs4DDiW7OrarSi9+ai553kz2/7QP8IeWPfhSEXFRvkc+DVidX7Hb1Q2I2S7uA7feZhkwXdmtTg8iu9tbi428cdn/h0vG3wH8dcv9JyQdJemANtq/GZgKjM9fV24b+T0tuiO/p8wGSWfntUjS6Hz4iIi4PyJmkV3G3utvl2q9mwPcepWIWAX8iOzWnD8BflkyeTZZUC8HhpaMvxpYC6zKDzj+J218uoyI14ClwI9bOysl3yO+Nz/Y+I1uvo1PAOdLehh4FDgjH/8NSY/kNS4DHu5m+2aATyO0Xk7S5cD2iJhdUHv7kB0YPTsiHi+iTbNq8R64vWVIGkV25sgvHN62N/AeuJlZorwHbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmi/h/5N7rDipLd5gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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MEuUANzNLlAPczCxR1TwPfI6k9ZJWlkz7oKR7JT2V/9xr25ZpZmblqjkCnwtMKZt2KfCriDgI+FU+bmZmfahigEfEIuDlssmnAjflwzcBf11wXWZmVkFv28D3jogXAfKfH+pqQUnNkloktbS1tfVyc2ZmVm6bn8SMiNkR0RQRTQ0NDdt6c2Zm7xu9DfB1kj4MkP9cX1xJZmZWjd4G+C+Ac/Phc4E7iinHzMyqVc1lhDcDDwOHSGqVNA24GvikpKeAT+bjZmbWh/pXWiAizupi1qSCazEzsx7wnZhmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJaqmAJd0iaTHJa2UdLOkgUUVZmZm3et1gEvaF7gYaIqIw4F+wGeLKszMzLpXaxNKf2AXSf2BXYEXai/JzMyq0esAj4jngRnAc8CLwB8j4p7y5SQ1S2qR1NLW1tb7Ss3M7F1qaULZCzgVGAkMA3aTdHb5chExOyKaIqKpoaGh95Wamdm71NKE8gng2Yhoi4g/AbcDHyumLDMzq6SWAH8O+IikXSWJrJPj1cWUZWZmldTSBv4IcCuwFHgsX9fsguoyM7MK+tfy5oi4AriioFrMzKwHfCemmVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZomq6TJCM+ta46W/rHcJVVl79cn1LsF6yUfgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mlqiaAlzSYEm3SnpC0mpJHy2qMDMz616tN/LMBO6KiM9I2pmsZ3ozM+sDvQ5wSXsCxwLnAUTE28DbxZRlZmaV1HIE/hdAG/ADSWOAJcD0iHi9dCFJzUAzwIgRI2rYnJm9n/nRBO9VSxt4f2Ac8P2IGAu8DlxavlBEzI6IpohoamhoqGFzZmZWqpYAbwVa886NIevgeFztJZmZWTVq6ZX+JeD3kg7JJ00CVhVSlZmZVVTrVShfAeblV6CsAb5Qe0lmZlaNmgI8IpYDTQXVYmZmPeA7Mc3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLVM0BLqmfpGWS7iyiIDMzq04RR+DTgdUFrMfMzHqgpgCXNBw4GbihmHLMzKxatR6Bfwf4OtDe1QKSmiW1SGppa2urcXNmZtah1wEu6RRgfUQs6W65iJgdEU0R0dTQ0NDbzZmZWZlajsD/EpgqaS1wC3CipB8XUpWZmVXU6wCPiG9GxPCIaAQ+C9wfEWcXVpmZmXXL14GbmSWqfxEriYgHgAeKWJeZmVXHR+BmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJaqWLtX2k7RQ0mpJj0uaXmRhZmbWvVqeB74F+B8RsVTSHsASSfdGxKqCajMzs27U0qXaixGxNB9+FVgN7FtUYWZm1r1C2sAlNQJjgUc6mdcsqUVSS1tbWxGbMzMzCghwSbsDtwFfjYhN5fMjYnZENEVEU0NDQ62bMzOzXE0BLmkAWXjPi4jbiynJzMyqUctVKAJuBFZHxP8triQzM6tGLUfgfwl8HjhR0vL89emC6jIzswp6fRlhRDwIqMBazMysB3wnpplZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJqrVHnimSnpT0tKRLiyrKzMwqq6VHnn7AvwCfAkYBZ0kaVVRhZmbWvVqOwI8Gno6INRHxNnALcGoxZZmZWSWKiN69UfoMMCUiLsjHPw9MjIgvly3XDDTno4cAT/a+3D4zFNhQ7yJ2IN6fxfG+LFYq+3P/iGgon9jrLtXovDu193wbRMRsYHYN2+lzkloioqnedewovD+L431ZrNT3Zy1NKK3AfiXjw4EXaivHzMyqVUuAPwocJGmkpJ2BzwK/KKYsMzOrpJZe6bdI+jJwN9APmBMRjxdWWX0l1eSTAO/P4nhfFivp/dnrk5hmZlZfvhPTzCxRDnAzs0Q5wM3MEuUAt0JJOlTSJEm7l02fUq+aUibpaEkT8uFRkv67pE/Xu64dgaQf1ruGWvkkZjckfSEiflDvOlIh6WLgvwGrgaOA6RFxRz5vaUSMq2d9qZF0BdmzhvoD9wITgQeATwB3R8Q/16+6tEgqv8RZwAnA/QARMbXPiyqAA7wbkp6LiBH1riMVkh4DPhoRr0lqBG4FfhQRMyUti4ixdS0wMfn+PAr4APASMDwiNknaBXgkIo6sa4EJkbQUWAXcQHbHuICbye5fISL+s37V9V4tt9LvECT9tqtZwN59WcsOoF9EvAYQEWslHQ/cKml/On/0gnVvS0S8A7wh6ZmI2AQQEW9Kaq9zbalpAqYDlwFfi4jlkt5MNbg7vO8DnCykTwJeKZsuYHHfl5O0lyQdFRHLAfIj8VOAOcAR9S0tSW9L2jUi3gDGd0yUNAhwgPdARLQD10qan/9cxw6Qf8l/gALcCezeETqlJD3Q9+Uk7RxgS+mEiNgCnCPpX+tTUtKOjYjNsDWAOgwAzq1PSWmLiFbgDEknA5vqXU+t3AZuZpYoX0ZoZpYoB7iZWaIc4GYlJA2W9KWS8WGSbq1nTWZdcRu4vS9I6pdfkldpuUbgzog4fJsXZVYjH4HbdkfSZZKelHSfpJsl/X0+/QFJTfnwUElr8+F+kq6R9Kik30q6MJ9+vKSFkn4CPCbpHyVNL9nOP+d3j5a6GjhA0vJ8nY2SVubLnyfp55L+Q9Kzkr6c39q+TNJvJH0wX+4ASXdJWiLp15IOzaefIWmlpBWSFm3bvWjvB76M0LYrksaT3R03luzf51JgSYW3TQP+GBETJH0AeEjSPfm8o4HDI+LZ/Oj6dmCmpJ3y7Rxdtq5L8+WPyutpLJt/eF7bQOBp4BsRMVbStWSXUX6HrJOAiyLiKUkTge8BJwKXAydFxPOSBle5S8y65AC37c0xwM/ym1c6e4ZFZyYDR0r6TD4+CDgIeBv4r4h4FrbeHbpR0liyG7iWRcTGHta3MCJeBV6V9EfgP/Lpj+U17A58DJgvbb359AP5z4eAuZJ+SvZFYlYTB7htj7o6MbOFPzf7DSyZLuArEXF36cL5rfyvl63jBuA8YB+yO0R7anPJcHvJeDvZ/6edgD90HMGXioiL8iPyk4Hl+V2rPf0CMdvKbeC2vVkEnCZpF0l7AH9VMm8tf76l/DMl0+8GvihpAICkgyXt1sX6fwZMASbk7yv3KrBHb4vPn1fyrKQz8lokaUw+fEBEPBIRlwMbgP16ux0zcIDbdiYilgL/DiwHbgN+XTJ7BllQLwaGlky/gexJc0vzE47/Shd/XUbE28BC4KedXZWSHxE/lJ9svKaXH+NzwDRJK4DHgVPz6ddIeiyvcRGwopfrNwN8GaFt5yRdCbwWETMKWt9OZCdGz4iIp4pYp1m9+Ajc3jckjSK7cuRXDm/bEfgI3MwsUT4CNzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxR/x+6UmuRqrfQJAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for i in range (len(query_day)):\n",
-    "    if query_day[i] is not NaN:\n",
-    "        plot_day(query_day[i])"
+    "# subplots\n",
+    "day_ax_arr = query_day_df.plot(subplots=True,layout=(2,4),kind='bar',figsize=(16,8),fontsize=15,sharey=True)\n",
+    "\n",
+    "for ax in day_ax_arr[-1]:\n",
+    "    ax.set_xlabel('query times', fontsize=16)\n",
+    "    \n",
+    "for ax_arr in day_ax_arr:\n",
+    "    ax_arr[0].yaxis.set_major_locator(MaxNLocator(integer=True))\n",
+    "    ax_arr[0].set_ylabel('days', fontsize=16)\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "worst-musical",
+   "id": "confident-capacity",
    "metadata": {},
    "source": [
     "### Plot query times in a month"
@@ -864,156 +333,107 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adverse-bruce",
+   "id": "younger-destiny",
    "metadata": {},
    "source": [
-    "Note: not all users have data on the same number of months. Each graph represents query times for a user in a month."
+    "Note: not all users have data on the same number of months. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "removed-sheffield",
+   "execution_count": null,
+   "id": "quiet-minister",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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cgY+PiOqIaPhw42nAgogYCSzIls3MrIO0ZwjlfGB2Nj8bmNT+cszMrFCFBngAT0paJmlK1jYoIjYAZNOBTW0oaYqkpZKW1tfXt79iMzMDCr8LZVxErJc0EJgv6aVCDxARM4GZADU1NdGGGs3MrAkFXYFHxPpsugl4CDgR2ChpMEA23VSqIs3MbF8tBrikgyT1bZgHzgJWAfOAyVm3ycAjpSrSzMz2VcgQyiDgIUkN/f81Ih6XtAR4QNKVwGvAxaUr08zMGmsxwCPiVWB0E+1bgAmlKMrMzFrmJzHNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS1TBAS6pu6Tlkn6VLQ+QNF/Smmx6SOnKNDOzxlpzBX4tsDpveRqwICJGAguyZTMz6yAFBbikYcAngZ/mNZ8PzM7mZwOTiluamZntT6FX4P8EfBXYk9c2KCI2AGTTgUWuzczM9qPFAJf0N8CmiFjWlgNImiJpqaSl9fX1bdmFmZk1oZAr8HHAeZJqgTnA6ZLuBTZKGgyQTTc1tXFEzIyImoioqaysLFLZZmbWYoBHxA0RMSwiqoDLgKci4rPAPGBy1m0y8EjJqjQzs3205z7wGcCZktYAZ2bLZmbWQXq0pnNELAQWZvNbgAnFL8nMzArhJzHNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS1SrbiNMTdW0R8tdQknVzvhkuUswszLyFbiZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpaoFgNcUm9JiyU9J+kFSTdm7QMkzZe0JpseUvpyzcysQSFX4DuA0yNiNFANnCPpZGAasCAiRgILsmUzM+sgLQZ45LyVLfbMvgI4H5idtc8GJpWkQjMza1JBY+CSuktaAWwC5kfEImBQRGwAyKYDS1emmZk1VlCAR8TuiKgGhgEnShpV6AEkTZG0VNLS+vr6ttZpZmaNtOoulIjYCiwEzgE2ShoMkE03NbPNzIioiYiaysrKdpZrZmYNCrkLpVJS/2z+g8AZwEvAPGBy1m0y8EipijQzs30V8pmYg4HZkrqTC/wHIuJXkn4HPCDpSuA14OIS1mlmZo20GOARsRIY00T7FmBCKYoyM7OW+UlMM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBJVyIcaf0TSf0haLekFSddm7QMkzZe0JpseUvpyzcysQSFX4LuA/xMRRwEnA9dIOhqYBiyIiJHAgmzZzMw6SIsBHhEbIuLZbH4bsBoYCpwPzM66zQYmlapIMzPbV6vGwCVVkfuE+kXAoIjYALmQBwYWuzgzM2tewQEuqQ/w78B1EfHnVmw3RdJSSUvr6+vbUqOZmTWhoACX1JNceN8XEb/ImjdKGpytHwxsamrbiJgZETURUVNZWVmMms3MjMLuQhFwF7A6In6Yt2oeMDmbnww8UvzyzMysOT0K6DMO+BzwvKQVWdvXgRnAA5KuBF4DLi5NiWZm1pQWAzwingbUzOoJxS3HzMwK5ScxzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFGFfCr9zyRtkrQqr22ApPmS1mTTQ0pbppmZNVbIFfgs4JxGbdOABRExEliQLZuZWQdqMcAj4jfAG42azwdmZ/OzgUlFrsvMzFrQ1jHwQRGxASCbDixeSWZmVoiSv4gpaYqkpZKW1tfXl/pwZmZdRlsDfKOkwQDZdFNzHSNiZkTURERNZWVlGw9nZmaNtTXA5wGTs/nJwCPFKcfMzApVyG2E9wO/A46UVCfpSmAGcKakNcCZ2bKZmXWgHi11iIjLm1k1oci1mJlZK/hJTDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0tUuwJc0jmSfi/pFUnTilWUmZm1rM0BLqk78M/AucDRwOWSji5WYWZmtn/tuQI/EXglIl6NiL8Ac4Dzi1OWmZm1pEc7th0KrMtbrgNOatxJ0hRgSrb4lqTft+OYnd2hwOaOOphu6agjdQn+2aXtQP/5Hd5UY3sCXE20xT4NETOBme04TjIkLY2ImnLXYa3nn13auurPrz1DKHXAR/KWhwHr21eOmZkVqj0BvgQYKWm4pF7AZcC84pRlZmYtafMQSkTskvRl4AmgO/CziHihaJWlqUsMFR2g/LNLW5f8+Slin2FrMzNLgJ/ENDNLlAPczCxRDnAzs0S15z7wLk3Sx8g9eTqU3P3v64F5EbG6rIWZdQHZ/7+hwKKIeCuv/ZyIeLx8lXUsX4G3gaSvkXvrAAGLyd1SKeB+v6lX2iR9odw12P5J+jvgEeBvgVWS8t/C4+/LU1V5+C6UNpD0MnBMROxs1N4LeCEiRpanMmsvSa9FxGHlrsOaJ+l54OMR8ZakKuBB4J6IuFXS8ogYU9YCO5CHUNpmDzAE+GOj9sHZOuvEJK1sbhUwqCNrsTbp3jBsEhG1kk4DHpR0OE2/xccBywHeNtcBCySt4b039DoMGAF8uWxVWaEGAWcDbzZqF/BMx5djrfS6pOqIWAGQXYn/DfAz4NjyltaxHOBtEBGPSzqC3FvqDiX3H78OWBIRu8tanBXiV0CfhgDIJ2lhx5djrfR5YFd+Q0TsAj4v6c7ylFQeHgM3M0uU70IxM0uUA9zMLFEOcDvgSeov6eq85dMk/aqA7W6SdEYrj1Ur6dC21GnWWg5w6wr6A1e32KuRiPh2RPy6BPWYFYUD3DoNSVWSXpL0U0mrJN0n6QxJ/ylpjaQTs34DJD0saaWk/5J0XNY+XdLPJC2U9Gr2xB7ADOCvJK2Q9A9ZWx9JD2bHu0/SPvcPS5ol6aJsvlbSjZKelfR89ig3kiokPSlpeXYHhPK2/6ykxdlx75TUXdIJWd29JR0k6QVJo0p3Vu1A5gC3zmYEcCtwHPAx4NPAJ4Drga9nfW4ElkfEcVnb3Xnbf4zcPd4nAt+R1BOYBqyNiOqImJr1G0Pufv6jgY8C4wqobXNEjAVuz+oB+A7wdPb03zxyzwMg6SjgUmBcRFQDu4HPRMSSrN/NwPeBeyNiVYHnxux9fB+4dTZ/iIjnASS9ACyIiMgen67K+nwC+BRARDyVXQX3y9Y9GhE7gB2SNtH8k5WLI6IuO86KbN9Pt1DbL7LpMuDCbP7UhvmIeFRSw8NBE4DjgSXZxf0HgU3ZupvIvX/OdqDhrwSzVnOAW2ezI29+T97yHt7799rU49INDzTkb7+b5v+NF9qvqW0a92/qYQoBsyPihibWDQD6AD2B3sDbBRzbbB8eQrEU/Qb4DOTuKCE3tPHn/fTfBvTtgFrOBQ7J2hcAF0kamK0bkL1XB+Q+v/FbwH3ALSWqy7oAX4FbiqYDP8/elOodYPL+OkfEluyF0FXAY8CjRazlRnJvI/ws8P+A17Jjvijpm8CTkroBO4FrJP01sCsi/lVSd+AZSadHxFNFrMm6CD9Kb2aWKA+hmJklygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmifr/wiI+y1IFIWUAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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NBrik2yS9KqmmYN5MSb+XtCJ/fLR9yzQzs4aKOQOfA0xsZP53I6I6f/ystGWZmVlzmg3wiHgceK0DajEzsxZoSx/4JZJW5l0sB5SsIjMzK0prA/xG4INANfAy8C9NNZQ0TdJSSUs3bNjQyt2ZmVlDrQrwiFgfEdsjYgfw78CYPbS9JSJGR8ToysrK1tZpZmYNtCrAJR1SMDkZqGmqrZmZtY8ezTWQdDcwDhggqQ64ChgnqRoIoBb4QjvWaGZmjWg2wCPivEZm39oOtZiZWQt4JKaZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiWo2wCXdJulVSTUF8w6U9Iik1fnPA9q3TDMza6iYM/A5wMQG82YAj0bEMODRfNrMzDpQswEeEY8DrzWY/Qng9vz57cAZJa7LzMya0do+8IMj4mWA/OdBpSvJzMyK0e4fYkqaJmmppKUbNmxo792ZmXUZrQ3w9ZIOAch/vtpUw4i4JSJGR8ToysrKVu7OzMwaam2A3w9ckD+/APhJacoxM7NiFXMZ4d3Ar4DDJdVJ+jwwG/hLSauBv8ynzcysA/VorkFEnNfEogklrsXMzFrAIzHNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLV7LfSW9dSNWNBuUtoVu3sj5W7BLNOwWfgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZotp0MytJtcBmYDuwLSJGl6IoMzNrXinuRjg+IjaWYDtmZtYC7kIxM0tUWwM8gIclLZM0rRQFmZlZcdrahTI2ItZJOgh4RNJzEfF4YYM82KcBHHrooW3cnZmZ1WvTGXhErMt/vgrcC4xppM0tETE6IkZXVla2ZXdmZlag1QEuaT9JfeufA6cCNaUqzMzM9qwtXSgHA/dKqt/ODyPiwZJUZWZmzWp1gEfEi8CIEtZiZmYt4MsIzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0tUj3IXYGZWjKoZC8pdQlFqZ3+sw/blM3Azs0Q5wM3MEuUANzNLlAPczCxRbQpwSRMl/UbSC5JmlKooMzNrXqsDXFJ34N+A04EjgfMkHVmqwszMbM/acgY+BnghIl6MiD8C84BPlKYsMzNrjiKidStKZwITI2JqPv0Z4PiIuKRBu2nAtHzycOA3rS+3wwwANpa7iL2Ij2fp+FiWVirH87CIqGw4sy0DedTIvN3eDSLiFuCWNuynw0laGhGjy13H3sLHs3R8LEsr9ePZli6UOuADBdODgXVtK8fMzIrVlgBfAgyTNERSL+Bc4P7SlGVmZs1pdRdKRGyTdAnwENAduC0inilZZeWVVJdPAnw8S8fHsrSSPp6t/hDTzMzKyyMxzcwS5QA3M0uUA9zMLFFd/gsdJH2YbATpILLr2NcB90fEqrIWZsbO389BwKKIeKtg/sSIeLB8laVJ0hggImJJfuuPicBzEfGzMpfWKl36DFzSl8luASBgMdmlkQLu9s25Sk/SZ8tdQ0ok/T3wE+DvgBpJhbeq+KfyVJUuSVcB1wM3SvoWcAPQB5gh6cqyFtdKXfoqFEnPA0dFxNYG83sBz0TEsPJUtneS9FJEHFruOlIh6WngzyPiLUlVwD3AnRFxnaTlETGyrAUmJj+e1cA+wCvA4Ih4U9L7yP7COaasBbZCV+9C2QEMBH7XYP4h+TJrIUkrm1oEHNyRtewFutd3m0REraRxwD2SDqPxW1nYnm2LiO3AO5LWRMSbABHxrqQk/7939QC/DHhU0mpgbT7vUGAocEmTa9meHAycBrzeYL6AJzu+nKS9Iqk6IlYA5GfifwXcBhxd3tKS9EdJ+0bEO8Cx9TMl9SPRE7Yu3YUCIKkb2a1xB5GFTB2wJH+nthaSdCvwg4h4opFlP4yI88tQVpIkDSY7a3ylkWVjI+KXZSgrWZL2iYj3Gpk/ADgkIp4uQ1lt0uUD3MwsVV36KhQzs5Q5wM3MEuUAt72epP6SLiqYHifpv4tYb5akU1q4r9q8T9Ws3TnArSvoD1zUbKsGIuLrEfHzdqjHrCQc4NZpSKqS9Jyk/5BUI2mupFMk/VLS6nwYNJIOlHSfpJWS/k/SMfn8mZJuk7RQ0ov5SEaA2cAHJa2Q9M/5vD6S7sn3N1fSbtdVS5qTf/dr/Zn1NyQ9JenpfIg7kiokPSxpuaSbKbg+W9KnJS3O93uzpO6Sjsvr7i1pP0nPSBrefkfV9mYOcOtshgLXAccAHwbOBz4CXA5ckbf5BrA8Hzl3BXBHwfofJrsOfQxwlaSewAxgTURUR8T0vN1IsnEARwJ/BowtoraNETEKuDGvB+Aq4Il8VOT9ZOMIkHQEcA4wNiKqge3ApyJiSd7uauDbwF0RUVPksTHbRVcfyGOdz2/rr8eV9AzwaEREPgy6Km/zEeCTABHxWH4W3C9ftiC/1vc9Sa/S9OjPxRFRl+9nRb7t3a5db+DH+c9lwJT8+Un1zyNigaT6AUwTyAaLLMlP7t8HvJovm0V2350tQP1fCWYt5gC3zqZwoMWOgukd/On3tbFh5PUDGgrX307Tv+PFtmtsnYbtGxtMIeD2iPhKI8sOJLuJUk+gN/B2Efs22427UCxFjwOfguyKErKujTf30H4z0LcDajkdOCCf/yhwpqSD8mUH5vcwgex7GL8GzAWuaae6rAvwGbilaCbwg/zGWe8AF+ypcURsyj8IrQEeABaUsJZvkN1++Cngf4GX8n0+K+mrwMP57Rq2AhdL+guy4fE/lNQdeFLSyRHxWAlrsi7CQ+nNzBLlLhQzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxR/w/zWsWUKibJHQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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czCxRDnAzs0Q5wM3MEuUANzNLVLMBLqm3pKWSXpD0kqTb8vYBkp6RtDZ/Prr9yzUzs3rFnIHvAs6LiFFAJXCRpLOBGcCiiBgBLMrnzcysgzQb4JHZkc/2zB8BXArMy9vnAZPbpUIzM2tUUWPgkrpLWgVsBp6JiCXAsRGxESB/Pqb9yjQzs4aKCvCI2BsRlcBQ4ExJI4vdgaRpkpZLWl5bW9vaOs3MrIEWXYUSEduAxcBFwCZJgwDy581NrDMnIsZGxNjy8vI2lmtmZvWKuQqlXFL/fPoI4HzgFWAhMDXvNhV4rL2KNDOzA/Uoos8gYJ6k7mSB/1BE/EzSr4CHJF0HvAFc0Y51mplZA80GeESsBkY30r4VmNgeRZmZWfN8J6aZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaKaDXBJ75f075LWSHpJ0k15+wBJz0hamz8f3f7lmplZvWLOwPcA/zciTgLOBm6UdDIwA1gUESOARfm8mZl1kGYDPCI2RsSv8+ntwBpgCHApMC/vNg+Y3F5FmpnZgVo0Bi6pAhgNLAGOjYiNkIU8cEypizMzs6YVHeCS+gD/BtwcEX9owXrTJC2XtLy2trY1NZqZWSOKCnBJPcnC+8GI+EnevEnSoHz5IGBzY+tGxJyIGBsRY8vLy0tRs5mZUdxVKALuBdZExLcLFi0EpubTU4HHSl+emZk1pUcRfcYBnwBelLQqb/sSMBt4SNJ1wBvAFe1TopmZNabZAI+I5wA1sXhiacsxM7Ni+U5MM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUc0GuKQfSdosqaqgbYCkZyStzZ+Pbt8yzcysoWLOwOcCFzVomwEsiogRwKJ83szMOlCzAR4RvwDeatB8KTAvn54HTC5xXWZm1ozWjoEfGxEbAfLnY0pXkpmZFaPd38SUNE3ScknLa2tr23t3ZmZdRmsDfJOkQQD58+amOkbEnIgYGxFjy8vLW7k7MzNrqLUBvhCYmk9PBR4rTTlmZlasYi4j/DHwK+BESTWSrgNmAx+WtBb4cD5vZmYdqEdzHSLimiYWTSxxLWZm1gK+E9PMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFFtCnBJF0n6jaTXJM0oVVFmZta8Vge4pO7A94CLgZOBaySdXKrCzMzs4NpyBn4m8FpEvB4RfwLmA5eWpiwzM2tOjzasOwRYXzBfA5zVsJOkacC0fHaHpN+0YZ+HuoHAlo7ame7oqD11Cf7Zpe1w//kd31hjWwJcjbTFAQ0Rc4A5bdhPMiQtj4ixnV2HtZx/dmnrqj+/tgyh1ADvL5gfCmxoWzlmZlastgT4MmCEpGGSegFXAwtLU5aZmTWn1UMoEbFH0meBp4DuwI8i4qWSVZamLjFUdJjyzy5tXfLnp4gDhq3NzCwBvhPTzCxRDnAzs0Q5wM3MEtWW68C7NEkfJLvzdAjZ9e8bgIURsaZTCzPrAvL/f0OAJRGxo6D9ooh4svMq61g+A28FSV8g++gAAUvJLqkU8GN/qFfaJH2qs2uwg5P0t8BjwP8BqiQVfoTH33VOVZ3DV6G0gqRXgVMiYneD9l7ASxExonMqs7aS9EZEHNfZdVjTJL0I/GVE7JBUATwM3B8Rd0paGRGjO7XADuQhlNapAwYDv2vQPihfZocwSaubWgQc25G1WKt0rx82iYhqSeOBhyUdT+Mf8XHYcoC3zs3AIklr+fMHeh0HDAc+22lVWbGOBS4E3m7QLuD5ji/HWuhNSZURsQogPxP/G+BHwKmdW1rHcoC3QkQ8KekEso/UHUL2H78GWBYRezu1OCvGz4A+9QFQSNLiji/HWuiTwJ7ChojYA3xS0j2dU1Ln8Bi4mVmifBWKmVmiHOBmZolygNthT1J/STcUzI+X9LMi1psl6fwW7qta0sDW1GnWUg5w6wr6Azc026uBiPhqRPy8HeoxKwkHuB0yJFVIekXSDyVVSXpQ0vmS/lPSWkln5v0GSHpU0mpJ/yXptLx9pqQfSVos6fX8jj2A2cAHJK2S9Pd5Wx9JD+f7e1DSAdcPS5or6fJ8ulrSbZJ+LenF/FZuJJVJelrSyvwKCBWs/3FJS/P93iOpu6Qz8rp7SzpS0kuSRrbfUbXDmQPcDjXDgTuB04APAh8FPgTcAnwp73MbsDIiTsvb7itY/4Nk13ifCXxNUk9gBrAuIiojYnrebzTZ9fwnA38BjCuiti0RMQb4QV4PwNeA5/K7/xaS3Q+ApJOAq4BxEVEJ7AU+FhHL8n63A98EHoiIqiKPjdl+fB24HWp+GxEvAkh6CVgUEZHfPl2R9/kQ8BGAiHg2Pwvuly97PCJ2AbskbabpOyuXRkRNvp9V+bafa6a2n+TPK4Ap+fS59dMR8bik+puDJgKnA8vyk/sjgM35sllkn5+zE6j/K8GsxRzgdqjZVTBdVzBfx5//vTZ2u3T9DQ2F6++l6X/jxfZrbJ2G/Ru7mULAvIj4YiPLBgB9gJ5Ab+CdIvZtdgAPoViKfgF8DLIrSsiGNv5wkP7bgb4dUMvFwNF5+yLgcknH5MsG5J/VAdn3N34FeBC4o53qsi7AZ+CWopnAP+cfSvVHYOrBOkfE1vyN0CrgCeDxEtZyG9nHCP8a+A/gjXyfL0v6MvC0pG7AbuBGSX8F7ImIf5HUHXhe0nkR8WwJa7IuwrfSm5klykMoZmaJcoCbmSXKAW5mligHuJlZohzgZmaJcoCbmSXKAW5mligHuJlZov4/H5fCMrkux94AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# put all query times in a month for all valid users in a data frame\n",
+    "start = 0\n",
+    "\n",
     "for i in range (len(query_month)):\n",
     "    if query_month[i] is not NaN:\n",
-    "        query_month_df = pd.DataFrame(data = {'query times':query_month[i]})\n",
-    "        query_month_df.plot(xlabel='month index',kind = 'bar',title='query times in a month')"
+    "        query_month_df = pd.DataFrame({'user'+str(i+1):query_month[i]}).rename_axis('month indicies').reset_index()\n",
+    "        query_month_df.set_index(['month indicies'], inplace=True)\n",
+    "        start = i\n",
+    "        break\n",
+    "query_month_df\n",
+    "\n",
+    "        \n",
+    "for t in range(start+1,len(query_month)):\n",
+    "    if query_month[t] is not NaN:\n",
+    "        new_month_df = pd.DataFrame({'user'+str(t+1):query_month[t]}).rename_axis('month indicies').reset_index()\n",
+    "        new_month_df.set_index(['month indicies'], inplace=True)\n",
+    "        query_month_df = query_month_df.join(new_month_df,how='outer')\n",
+    "\n",
+    "query_month_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "naked-february",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot a graph for all valid users\n",
+    "graph_month = query_month_df.plot(kind='bar',figsize=(12,8),title='query times in a month',fontsize=18)\n",
+    "graph_month.title.set_size(20)\n",
+    "plt.xlabel('month indicies',fontsize=16)\n",
+    "plt.ylabel('query times', fontsize=16)\n",
+    "graph_day.yaxis.set_major_locator(MaxNLocator(integer=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "satisfied-essay",
+   "metadata": {},
+   "source": [
+    "### Get query times for all valid users in January"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "korean-vintage",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "oriented-possibility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot data graph\n",
+    "graph_date = date_df.plot(kind='bar',figsize=(20,10),title='query times in January',fontsize=18)\n",
+    "graph_date.title.set_size(20)\n",
+    "plt.xlabel('date',fontsize=16)\n",
+    "plt.ylabel('query times', fontsize=16)\n",
+    "graph_date.yaxis.set_major_locator(MaxNLocator(integer=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "alpha-vermont",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# subplots on January query times\n",
+    "date_ax_arr = date_df.plot(subplots=True,layout=(2,4),kind='bar',figsize=(16,8),fontsize=15,sharey =True)\n",
+    "\n",
+    "for ax in date_ax_arr[-1]:\n",
+    "    ax.set_xlabel(\"date\", fontsize=16)\n",
+    "    \n",
+    "for ax_arr in date_ax_arr:\n",
+    "    ax_arr[0].yaxis.set_major_locator(MaxNLocator(integer=True))\n",
+    "    ax_arr[0].set_ylabel(\"query times\", fontsize=16)\n",
+    "    \n",
+    "date_ax_arr[-1][0].set_xticks(list(range(0,len(date_df.index),5)))\n",
+    "date_ax_arr[-1][0].set_xticklabels(list(range(date_df.index[0],date_df.index[-1]+1,5)))"
    ]
   }
  ],
diff --git a/tour_model_eval/v-measurel_all_bins_single_user.ipynb b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
index cf2aa3a..0b0e3f0 100644
--- a/tour_model_eval/v-measurel_all_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "republican-pleasure",
+   "id": "level-offering",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "decent-passion",
+   "id": "significant-leone",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "preliminary-example",
+   "id": "graduate-oliver",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "executive-heather",
+   "id": "quality-aerospace",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,17 +66,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "genuine-shipping",
+   "id": "awful-career",
    "metadata": {},
    "outputs": [],
    "source": [
-    "user = all_users[6]"
+    "user = all_users[4]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "sorted-juvenile",
+   "id": "designing-fourth",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "primary-friendly",
+   "id": "interested-carroll",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,31 +99,33 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "medical-spider",
+   "id": "adverse-female",
    "metadata": {},
    "outputs": [],
    "source": [
     "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
-    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
-    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
+    "non_empty_trips_df = pd.DataFrame(t[\"data\"][\"user_input\"]for t in non_empty_trips)\n",
+    "valid_trips_df = non_empty_trips_df.dropna(axis=0,how='any',thresh=None,subset=None,inplace=False)\n",
+    "valid_trips_idx_ls = valid_trips_df.index.tolist()\n",
+    "valid_trips = [non_empty_trips[i]for i in valid_trips_idx_ls]\n",
     "len(valid_trips),valid_trips"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "latest-reconstruction",
+   "id": "proof-ridge",
    "metadata": {},
    "outputs": [],
    "source": [
     "sim = similarity.similarity(valid_trips, radius)\n",
-    "sim.data"
+    "len(sim.data)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "stone-omega",
+   "id": "fuzzy-mexico",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "individual-insert",
+   "id": "fourth-disposition",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -152,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "continued-borough",
+   "id": "ranging-composition",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +164,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "outside-fairy",
+   "id": "royal-elephant",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,7 +177,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "extraordinary-penalty",
+   "id": "unnecessary-significance",
    "metadata": {},
    "source": [
     "### Original output"
@@ -184,7 +186,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "advised-wiring",
+   "id": "played-passage",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "careful-sally",
+   "id": "collected-consensus",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "elect-hardwood",
+   "id": "reverse-hollow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "dynamic-grace",
+   "id": "sealed-georgia",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -231,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "romance-chrome",
+   "id": "occupational-attention",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "further-moscow",
+   "id": "automatic-rebate",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +258,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "welsh-trustee",
+   "id": "raised-hundred",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "minimal-fever",
+   "id": "noticed-default",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +286,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "corporate-missile",
+   "id": "municipal-rally",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "radio-meter",
+   "id": "flying-hazard",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fancy-mount",
+   "id": "outstanding-differential",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "brown-traveler",
+   "id": "danish-glasgow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,7 +330,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "chemical-rider",
+   "id": "spoken-bangkok",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +339,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "excessive-spell",
+   "id": "moved-inflation",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -346,7 +348,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "christian-legislature",
+   "id": "cellular-bruce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +361,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ready-delay",
+   "id": "genetic-stuff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +376,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "blind-router",
+   "id": "attempted-confidentiality",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,7 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "suited-institution",
+   "id": "north-bobby",
    "metadata": {
     "scrolled": true
    },
@@ -401,7 +403,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "incomplete-confusion",
+   "id": "supported-judge",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,7 +415,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ahead-hawaiian",
+   "id": "corresponding-officer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "collectible-australia",
+   "id": "accepting-waterproof",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -443,7 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "changed-pollution",
+   "id": "german-sugar",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +455,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "judicial-printer",
+   "id": "impossible-rescue",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,7 +465,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "female-confidentiality",
+   "id": "comfortable-playback",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "foster-reading",
+   "id": "israeli-alexandria",
    "metadata": {},
    "source": [
     "### After converting purposes and mode"
@@ -481,7 +483,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "opposite-aurora",
+   "id": "removed-gallery",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -492,7 +494,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "western-commissioner",
+   "id": "temporal-campaign",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +504,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "vocal-saudi",
+   "id": "joint-november",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -522,7 +524,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "recreational-label",
+   "id": "specialized-airport",
    "metadata": {
     "scrolled": true
    },
@@ -536,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "adequate-notice",
+   "id": "meaning-planet",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -548,7 +550,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "stuck-survivor",
+   "id": "patient-monitoring",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,7 +565,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "floppy-flight",
+   "id": "computational-lightning",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,7 +580,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "broken-demand",
+   "id": "boring-cream",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -588,7 +590,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "nasty-potato",
+   "id": "animated-presentation",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +600,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "regular-glance",
+   "id": "collaborative-essence",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
index c74fe3c..a5fdab5 100644
--- a/tour_model_eval/v-measurel_bins_all_user.ipynb
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "spoken-acrobat",
+   "id": "superior-snowboard",
    "metadata": {
     "scrolled": true
    },
@@ -28,7 +28,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "solid-decimal",
+   "id": "brave-finnish",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "acute-departure",
+   "id": "maritime-arcade",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "practical-flour",
+   "id": "final-stability",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_ls = evaluation.get_user_ls(all_users)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "colored-arctic",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "indirect-retrieval",
+   "id": "complete-excess",
    "metadata": {},
    "source": [
     "## Bins above cutoff"
@@ -67,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deadly-microwave",
+   "id": "higher-charge",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -76,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "strange-dining",
+   "id": "joined-mauritius",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "black-mobility",
+   "id": "cooked-louis",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "composite-possibility",
+   "id": "personalized-stable",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -104,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "wanted-mustang",
+   "id": "august-breed",
    "metadata": {
     "scrolled": true
    },
@@ -116,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "productive-palestine",
+   "id": "frequent-niagara",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "heard-florist",
+   "id": "connected-haven",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -134,7 +144,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "smaller-creek",
+   "id": "animated-tumor",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "square-importance",
+   "id": "primary-tampa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "approximate-groove",
+   "id": "agricultural-syndicate",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -162,7 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "studied-saint",
+   "id": "sorted-honey",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,7 +186,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "wrapped-rebate",
+   "id": "conservative-pregnancy",
    "metadata": {},
    "source": [
     "#### homogeneity_score"
@@ -185,20 +195,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "suburban-insertion",
+   "id": "western-safety",
    "metadata": {},
    "outputs": [],
    "source": [
     "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
-    "                           index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                                  'user9','user10','user11','user12','user13'])"
+    "                           index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "everyday-conditioning",
+   "id": "spectacular-haven",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "engaged-injury",
+   "id": "incorporated-google",
    "metadata": {},
    "source": [
     "#### completeness_score"
@@ -216,20 +225,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "mighty-consolidation",
+   "id": "adapted-insight",
    "metadata": {},
    "outputs": [],
    "source": [
     "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
-    "                      index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                                  'user9','user10','user11','user12','user13'])"
+    "                      index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "christian-revelation",
+   "id": "chemical-california",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +246,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lovely-particle",
+   "id": "living-archives",
    "metadata": {},
    "source": [
     "#### v_measure_score"
@@ -247,20 +255,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "documented-perfume",
+   "id": "indonesian-magnet",
    "metadata": {},
    "outputs": [],
    "source": [
     "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
     "                          'after converting purposes and replaced mode':v_score_cvt},\n",
-    "                   index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                                  'user9','user10','user11','user12','user13'])"
+    "                   index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "published-kruger",
+   "id": "demonstrated-resident",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "large-cement",
+   "id": "lined-manner",
    "metadata": {},
    "source": [
     "## All bins"
@@ -277,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "wrapped-channels",
+   "id": "insured-kingdom",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -286,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "assigned-pearl",
+   "id": "latest-finding",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +303,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "rotary-macro",
+   "id": "extended-processing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +312,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "communist-harvard",
+   "id": "european-philadelphia",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -314,7 +321,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "median-speed",
+   "id": "developing-password",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,7 +331,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "present-installation",
+   "id": "collective-calculator",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -333,7 +340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "armed-former",
+   "id": "capable-bathroom",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -342,7 +349,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "august-maintenance",
+   "id": "obvious-faculty",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "shaped-strip",
+   "id": "transparent-theology",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gentle-plenty",
+   "id": "charged-greeting",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -370,7 +377,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "architectural-perspective",
+   "id": "extraordinary-friday",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -384,7 +391,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "periodic-password",
+   "id": "trying-flesh",
    "metadata": {},
    "source": [
     "#### homogeneity_score"
@@ -393,20 +400,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "theoretical-oliver",
+   "id": "distinguished-morris",
    "metadata": {},
    "outputs": [],
    "source": [
     "ab_homo_df=pd.DataFrame(data={'original user input':ab_homo_score_ori,'after translation':ab_homo_score_sp2en,\n",
     "                              'after converting purposes and replaced mode':ab_homo_score_cvt},\n",
-    "                        index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                               'user9','user10','user11','user12','user13'])"
+    "                        index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "seasonal-coalition",
+   "id": "blocked-judge",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -415,7 +421,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "outdoor-pavilion",
+   "id": "english-quick",
    "metadata": {},
    "source": [
     "#### completeness_score"
@@ -424,20 +430,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "activated-schedule",
+   "id": "speaking-watson",
    "metadata": {},
    "outputs": [],
    "source": [
     "ab_comp_df = pd.DataFrame(data={'original user input':ab_comp_score_ori,'after translation':ab_comp_score_sp2en,\n",
     "                                'after converting purposes and replaced mode':ab_comp_score_cvt},\n",
-    "                          index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                                 'user9','user10','user11','user12','user13'])"
+    "                          index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "graphic-revision",
+   "id": "equivalent-fleece",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -446,7 +451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "golden-philadelphia",
+   "id": "white-reasoning",
    "metadata": {},
    "source": [
     "#### v_measure_score"
@@ -455,20 +460,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "relevant-nebraska",
+   "id": "sacred-celebrity",
    "metadata": {},
    "outputs": [],
    "source": [
     "ab_v_df = pd.DataFrame(data={'original user input':ab_v_score_ori,'after translation':ab_v_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':ab_v_score_cvt},\n",
-    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                              'user9','user10','user11','user12','user13'])"
+    "                       index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "assisted-franchise",
+   "id": "figured-stack",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
index f44bfdc..a35b9a6 100644
--- a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "secondary-armor",
+   "id": "warming-ghana",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "smooth-reviewer",
+   "id": "hindu-homeless",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "accessible-jaguar",
+   "id": "serious-guide",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "endangered-device",
+   "id": "split-burden",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_ls = evaluation.get_user_ls(all_users)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abstract-complaint",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prescription-disease",
+   "id": "colored-surprise",
    "metadata": {},
    "source": [
     "## Evaluate clusters above cutoff based on silhouette_score"
@@ -65,7 +75,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fatal-castle",
+   "id": "received-isolation",
    "metadata": {},
    "source": [
     "### Original user input"
@@ -74,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "federal-convertible",
+   "id": "original-parade",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +94,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "polyphonic-astronomy",
+   "id": "professional-evans",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "later-share",
+   "id": "latin-boulder",
    "metadata": {},
    "source": [
     "### After changing language"
@@ -102,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "lesbian-realtor",
+   "id": "prescription-supplement",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +122,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "defensive-numbers",
+   "id": "lesbian-testing",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +131,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-girlfriend",
+   "id": "opened-norway",
    "metadata": {},
    "source": [
     "### After converting purposes and replaced mode"
@@ -130,7 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "synthetic-debate",
+   "id": "wicked-timeline",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "finite-ireland",
+   "id": "severe-plant",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "inclusive-champagne",
+   "id": "matched-custody",
    "metadata": {},
    "source": [
     "### DataFrame"
@@ -158,7 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "protective-appraisal",
+   "id": "bizarre-retailer",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "intended-campaign",
+   "id": "sacred-applicant",
    "metadata": {},
    "source": [
     "#### homogeneity_score"
@@ -181,20 +191,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "robust-matthew",
+   "id": "alpha-differential",
    "metadata": {},
    "outputs": [],
    "source": [
     "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
-    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                              'user9','user10','user11','user12','user13'])"
+    "                       index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "surrounded-karma",
+   "id": "mental-abraham",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "furnished-valve",
+   "id": "premium-force",
    "metadata": {},
    "source": [
     "#### completeness_score"
@@ -212,20 +221,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "regulation-storage",
+   "id": "former-store",
    "metadata": {},
    "outputs": [],
    "source": [
     "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
-    "                       index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                              'user9','user10','user11','user12','user13'])"
+    "                       index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "liked-palestine",
+   "id": "brave-kentucky",
    "metadata": {
     "scrolled": true
    },
@@ -236,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "gorgeous-denver",
+   "id": "affecting-daughter",
    "metadata": {},
    "source": [
     "#### v_measure_score"
@@ -245,20 +253,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "quiet-capital",
+   "id": "cheap-psychiatry",
    "metadata": {},
    "outputs": [],
    "source": [
     "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
     "                          'after converting purposes and replaced mode':v_score_cvt},\n",
-    "                    index=['user1','user2','user3','user4','user5','user6','user7','user8',\n",
-    "                           'user9','user10','user11','user12','user13'])"
+    "                    index=user_ls)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "behavioral-embassy",
+   "id": "violent-consideration",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
index 271ef9f..a88733a 100644
--- a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -113,8 +113,10 @@
    "outputs": [],
    "source": [
     "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
-    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
-    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
+    "non_empty_trips_df = pd.DataFrame(t[\"data\"][\"user_input\"]for t in non_empty_trips)\n",
+    "valid_trips_df = non_empty_trips_df.dropna(axis=0,how='any',thresh=None,subset=None,inplace=False)\n",
+    "valid_trips_idx_ls = valid_trips_df.index.tolist()\n",
+    "valid_trips = [non_empty_trips[i]for i in valid_trips_idx_ls]\n",
     "len(valid_trips),valid_trips"
    ]
   },
diff --git a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
index c1c1ea9..e16031c 100644
--- a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
@@ -70,7 +70,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "user = all_users[6]"
+    "user = all_users[0]"
    ]
   },
   {
@@ -104,9 +104,11 @@
    "outputs": [],
    "source": [
     "# filter out trips that are not fully labeled(contain NaN in user_input)\n",
-    "valid_trips = [t for t in non_empty_trips if 'mode_confirm' in t[\"data\"][\"user_input\"] and \n",
-    "               'purpose_confirm'in t[\"data\"][\"user_input\"] and 'replaced_mode' in t[\"data\"][\"user_input\"]]\n",
-    "len(valid_trips)"
+    "non_empty_trips_df = pd.DataFrame(t[\"data\"][\"user_input\"]for t in non_empty_trips)\n",
+    "valid_trips_df = non_empty_trips_df.dropna(axis=0,how='any',thresh=None,subset=None,inplace=False)\n",
+    "valid_trips_idx_ls = valid_trips_df.index.tolist()\n",
+    "valid_trips = [non_empty_trips[i]for i in valid_trips_idx_ls]\n",
+    "len(valid_trips),valid_trips"
    ]
   },
   {

From 1be3341b32752acaa35124fbde7afb322545a05c Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Sat, 13 Mar 2021 14:18:39 -0800
Subject: [PATCH 11/16] change'query times' to 'user input request times', add
 scatter(v-score,user input request proportion median)

---
 ... user_input_request_times_all_users.ipynb} | 311 ++++++++++++------
 1 file changed, 209 insertions(+), 102 deletions(-)
 rename tour_model_eval/{query_times_all_users.ipynb => user_input_request_times_all_users.ipynb} (55%)

diff --git a/tour_model_eval/query_times_all_users.ipynb b/tour_model_eval/user_input_request_times_all_users.ipynb
similarity index 55%
rename from tour_model_eval/query_times_all_users.ipynb
rename to tour_model_eval/user_input_request_times_all_users.ipynb
index a15e626..8d4528d 100644
--- a/tour_model_eval/query_times_all_users.ipynb
+++ b/tour_model_eval/user_input_request_times_all_users.ipynb
@@ -26,7 +26,7 @@
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.ticker import MaxNLocator\n",
     "import numpy as np\n",
-    "# import matplotlib.ticker as ticker"
+    "from matplotlib import cm"
    ]
   },
   {
@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_day = []"
+    "req_day = []"
    ]
   },
   {
@@ -67,7 +67,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_month = []"
+    "req_month = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "incident-hampton",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "req_propor_median = []"
    ]
   },
   {
@@ -103,14 +113,50 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "formed-drive",
+   "id": "frozen-stanford",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# build a base dataframe for query times in January \n",
+    "# bin trips according to ['start_local_dt']\n",
+    "def bin_date(trip_ls,day=None,month=None):\n",
+    "    bin_date = []\n",
+    "    for trip_index in trip_ls:\n",
+    "        added = False\n",
+    "        trip = filter_trips[trip_index]\n",
+    "\n",
+    "        for bin in bin_date:\n",
+    "            if day:\n",
+    "                if match_day(trip,bin):\n",
+    "                    bin.append(trip_index)\n",
+    "                    added = True\n",
+    "                    break\n",
+    "            if month:\n",
+    "                if match_month(trip,bin):\n",
+    "                    bin.append(trip_index)\n",
+    "                    added = True\n",
+    "                    break\n",
+    "\n",
+    "        if not added:\n",
+    "            bin_date.append([trip_index])\n",
+    "\n",
+    "    return bin_date       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "formed-drive",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# build a base dataframe for request times in January \n",
     "date_df = pd.DataFrame(data = {'date':np.arange(1,32),'drop_col':np.arange(1,32)})\n",
     "date_df.set_index(['date'], inplace=True)\n",
     "\n",
+    "# get valid user list\n",
+    "valid_users = []\n",
     "\n",
     "for a in range(len(all_users)):\n",
     "    user = all_users[a]\n",
@@ -119,36 +165,39 @@
     "\n",
     "    # filter out users that don't have enough valid labeled trips\n",
     "    if not evaluation.valid_user(filter_trips,trips):\n",
-    "        query_day.append(NaN)\n",
-    "        query_month.append(NaN)      \n",
+    "        req_day.append(NaN)\n",
+    "        req_month.append(NaN) \n",
     "        continue\n",
-    "        \n",
+    "    val_user = 'user' + str(a + 1)\n",
+    "    valid_users.append(val_user)\n",
     "    sim.bin_data()\n",
     "    sim.delete_bins()\n",
     "    bins = sim.bins\n",
     "    \n",
-    "    # collect query trips and common trips(no need to query) indices above cutoff\n",
+    "    # collect requested trips and common trips(no need to request) indices above cutoff\n",
     "    ab_trip_ls = []\n",
-    "    no_query_trip_ls = []\n",
+    "    no_req_trip_ls = []\n",
     "    for bin in bins:\n",
     "        early_trip = filter_trips[bin[0]]\n",
-    "        trip_index = 0\n",
+    "        index = 0\n",
     "        for i in range(1,len(bin)):\n",
     "            compare_trip = filter_trips[bin[i]]\n",
     "            if early_trip['data']['start_local_dt']['year']>compare_trip['data']['start_local_dt']['year']:\n",
     "                early_trip = compare_trip\n",
-    "                trip_index = i\n",
+    "                index = i\n",
     "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']>compare_trip['data']['start_local_dt']['month']:\n",
     "                early_trip = compare_trip\n",
-    "                trip_index = i\n",
+    "                index = i\n",
     "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']==compare_trip['data']['start_local_dt']['month'] and early_trip['data']['start_local_dt']['day']>compare_trip['data']['start_local_dt']['day']:\n",
     "                early_trip = compare_trip\n",
-    "                trip_index = i\n",
-    "        ab_trip_ls.append(bin[trip_index])\n",
+    "                index = i\n",
+    "        early_trip_index = bin[index]\n",
+    "        ab_trip_ls.append(early_trip_index)\n",
     "        \n",
     "        for k in range(len(bin)):\n",
-    "            if k != trip_index:\n",
-    "                no_query_trip_ls.append(bin[k])\n",
+    "            if k != index:\n",
+    "                no_req_trip_idx = bin[k]\n",
+    "                no_req_trip_ls.append(no_req_trip_idx)\n",
     "\n",
     "\n",
     "    \n",
@@ -158,30 +207,21 @@
     "    # collect query trips indices below cutoff\n",
     "    bl_trip_ls = []\n",
     "    for bin in bl_bins:\n",
-    "        for index in bin:\n",
-    "            bl_trip_ls.append(index)\n",
+    "        for trip_index in bin:\n",
+    "            bl_trip_ls.append(trip_index)\n",
     "            \n",
-    "    # whole list of query trips indices\n",
-    "    query_trips_ls=ab_trip_ls+bl_trip_ls\n",
+    "    # whole list of requested trips indices\n",
+    "    req_trips_ls=ab_trip_ls+bl_trip_ls\n",
     "    \n",
     "    \n",
-    "    # collect query times in a day\n",
-    "    bin_day = []\n",
-    "    for trip_index in query_trips_ls:\n",
-    "        added = False\n",
-    "        trip = filter_trips[trip_index]\n",
-    "        for bin in bin_day:\n",
-    "            if match_day(trip,bin):\n",
-    "                bin.append(trip_index)\n",
-    "                added = True\n",
-    "        if not added:\n",
-    "            bin_day.append([trip_index])\n",
-    "    query_day_ls = []\n",
+    "    # collect request times in a day\n",
+    "    bin_day = bin_date(req_trips_ls,day=True)\n",
+    "    req_day_ls = []\n",
     "    for bin in bin_day:\n",
-    "        query_day_ls.append(len(bin))\n",
-    "        \n",
-    "    # collect 0 query days \n",
-    "    for trip_index in no_query_trip_ls:\n",
+    "        req_day_ls.append(len(bin))\n",
+    "               \n",
+    "    # collect 0 request days \n",
+    "    for trip_index in no_req_trip_ls:\n",
     "        trip = filter_trips[trip_index]\n",
     "        match = False\n",
     "        for bin in bin_day:\n",
@@ -189,43 +229,56 @@
     "                match = True\n",
     "                break\n",
     "        if not match:\n",
-    "            query_day_ls.append(0)\n",
+    "            req_day_ls.append(0)\n",
     "            \n",
-    "\n",
-    "    # collect query times in a day for every user\n",
-    "    query_day.append(query_day_ls)\n",
+    "    # collect request times in a day for every user\n",
+    "    req_day.append(req_day_ls)\n",
+    "    \n",
+    "    # collect user input request proportion in a day\n",
+    "    filter_trips_df = pd.DataFrame(filter_trips)\n",
+    "    filter_trips_idx_ls = filter_trips_df.index.values.tolist()\n",
+    "    bin_filter_trips_day = bin_date(filter_trips_idx_ls,day=True)\n",
+    "    propor_single_user = []\n",
+    "    for valid_trips_bin in bin_filter_trips_day:\n",
+    "        match = False\n",
+    "        for req_trips_bin in bin_day:\n",
+    "            req_trip = filter_trips[req_trips_bin[0]]\n",
+    "            if match_day(req_trip,valid_trips_bin):\n",
+    "                proportion = round(len(req_trips_bin)/len(valid_trips_bin), 2)\n",
+    "                propor_single_user.append(proportion)\n",
+    "                match = True\n",
+    "                break\n",
+    "        if not match:\n",
+    "            propor_single_user.append(0) \n",
+    "            \n",
+    "    # get user input request proportion median in a day\n",
+    "    median = np.median(propor_single_user)\n",
     "    \n",
+    "    # collect medians for every user\n",
+    "    req_propor_median.append(median)\n",
     "   \n",
-    "    # collect query times in a month\n",
-    "    bin_month = []\n",
-    "    for trip_index in query_trips_ls:\n",
-    "        added = False\n",
-    "        trip = filter_trips[trip_index]\n",
-    "        for bin in bin_month:\n",
-    "            if match_month(trip,bin):\n",
-    "                bin.append(trip_index)\n",
-    "                added = True\n",
-    "        if not added:\n",
-    "            bin_month.append([trip_index])\n",
-    "        query_month_ls = []\n",
+    "    \n",
+    "    # collect request times in a month\n",
+    "    bin_month = bin_date(req_trips_ls,month=True)\n",
+    "    req_month_ls = []\n",
     "    for bin in bin_month:\n",
-    "        query_month_ls.append(len(bin))\n",
+    "        req_month_ls.append(len(bin))\n",
     "\n",
-    "    # collect query times in a month for every user\n",
-    "    query_month.append(query_month_ls)\n",
+    "    # collect request times in a month for every user\n",
+    "    req_month.append(req_month_ls)\n",
     "    \n",
     "    # select the trips that are in Jan 2021\n",
     "    jan_trips = []\n",
-    "    for trip_index in query_trips_ls:\n",
+    "    for trip_index in req_trips_ls:\n",
     "        if filter_trips[trip_index]['data']['start_local_dt']['year']==2021 and filter_trips[trip_index]['data']['start_local_dt']['month']==1:\n",
     "            jan_trips.append(trip_index)\n",
     "    \n",
-    "    # create the data frame for query times in Jan 2021\n",
-    "    date = []\n",
+    "    # create the data frame for request times in Jan 2021\n",
+    "    jan_date = []\n",
     "    for trip_index in jan_trips:\n",
     "        trip_date = filter_trips[trip_index]['data']['start_local_dt']['day']\n",
-    "        date.append(trip_date)\n",
-    "        new_date_df = pd.DataFrame(data = date)\n",
+    "        jan_date.append(trip_date)\n",
+    "        new_date_df = pd.DataFrame(data = jan_date)\n",
     "        new_date_df=new_date_df.value_counts(sort = False).rename_axis('date').to_frame('user'+str(a+1)).reset_index()\n",
     "        new_date_df.set_index(['date'], inplace=True)\n",
     "    date_df = date_df.join(new_date_df,how='outer')\n",
@@ -239,7 +292,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_day"
+    "req_day"
    ]
   },
   {
@@ -249,7 +302,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_month"
+    "req_month"
    ]
   },
   {
@@ -257,7 +310,7 @@
    "id": "ruled-dictionary",
    "metadata": {},
    "source": [
-    "### Plot query times in a day"
+    "### Plot request times in a day"
    ]
   },
   {
@@ -267,24 +320,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# show query times in a data frame\n",
+    "# show request times in a data frame\n",
     "base = 0\n",
-    "for i in range (len(query_day)):\n",
-    "    if query_day[i] is not NaN:\n",
-    "            query_day_ls_df = pd.DataFrame(data = query_day[i])\n",
-    "            query_day_df=query_day_ls_df.value_counts(sort = False).rename_axis('query times').to_frame('user'+str(i+1)).reset_index()\n",
-    "            query_day_df.set_index(['query times'], inplace=True)\n",
+    "for i in range (len(req_day)):\n",
+    "    if req_day[i] is not NaN:\n",
+    "            req_day_ls_df = pd.DataFrame(data = req_day[i])\n",
+    "            req_day_df=req_day_ls_df.value_counts(sort = False).rename_axis('request times').to_frame('user'+str(i+1)).reset_index()\n",
+    "            req_day_df.set_index(['request times'], inplace=True)\n",
     "            base = i\n",
     "            break\n",
     "            \n",
-    "for i in range (base+1,len(query_day)):\n",
-    "    if query_day[i] is not NaN:\n",
-    "        new_day_df = pd.DataFrame(data = query_day[i])\n",
-    "        new_day_df = new_day_df.value_counts(sort = False).rename_axis('query times').to_frame('user'+str(i+1)).reset_index()\n",
-    "        new_day_df.set_index(['query times'], inplace=True)\n",
-    "        query_day_df=query_day_df.join(new_day_df,how='outer',sort='query times')\n",
+    "for i in range (len(req_day)):\n",
+    "    if req_day[i] is not NaN and i is not base:\n",
+    "        new_day_df = pd.DataFrame(data = req_day[i])\n",
+    "        new_day_df = new_day_df.value_counts(sort = False).rename_axis('request times').to_frame('user'+str(i+1)).reset_index()\n",
+    "        new_day_df.set_index(['request times'], inplace=True)\n",
+    "        req_day_df=req_day_df.join(new_day_df,how='outer',sort='request times')\n",
     "        \n",
-    "query_day_df"
+    "req_day_df"
    ]
   },
   {
@@ -295,10 +348,10 @@
    "outputs": [],
    "source": [
     "# plot a graph for all valid users\n",
-    "yticks_max = query_day_df.max().max()\n",
-    "graph_day = query_day_df.plot(kind='bar',figsize=(14,16),title='query times in a day',fontsize=18,yticks=np.arange(0, yticks_max+4, step=4))\n",
+    "yticks_max = req_day_df.max().max()\n",
+    "graph_day = req_day_df.plot(kind='bar',figsize=(14,16),title='request times in a day',fontsize=18,yticks=np.arange(0, yticks_max+4, step=4))\n",
     "graph_day.title.set_size(20)\n",
-    "plt.xlabel('query times',fontsize=16)\n",
+    "plt.xlabel('request times',fontsize=16)\n",
     "plt.ylabel('days', fontsize=16)"
    ]
   },
@@ -312,7 +365,7 @@
    "outputs": [],
    "source": [
     "# subplots\n",
-    "day_ax_arr = query_day_df.plot(subplots=True,layout=(2,4),kind='bar',figsize=(16,8),fontsize=15,sharey=True)\n",
+    "day_ax_arr = req_day_df.plot(subplots=True,layout=(2,4),kind='bar',figsize=(16,8),fontsize=15,sharey=True)\n",
     "\n",
     "for ax in day_ax_arr[-1]:\n",
     "    ax.set_xlabel('query times', fontsize=16)\n",
@@ -328,7 +381,7 @@
    "id": "confident-capacity",
    "metadata": {},
    "source": [
-    "### Plot query times in a month"
+    "### Plot request times in a month"
    ]
   },
   {
@@ -346,25 +399,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# put all query times in a month for all valid users in a data frame\n",
+    "# put all request times in a month for all valid users in a data frame\n",
     "start = 0\n",
     "\n",
-    "for i in range (len(query_month)):\n",
-    "    if query_month[i] is not NaN:\n",
-    "        query_month_df = pd.DataFrame({'user'+str(i+1):query_month[i]}).rename_axis('month indicies').reset_index()\n",
-    "        query_month_df.set_index(['month indicies'], inplace=True)\n",
+    "for i in range (len(req_month)):\n",
+    "    if req_month[i] is not NaN:\n",
+    "        req_month_df = pd.DataFrame({'user'+str(i+1):req_month[i]}).rename_axis('month indicies').reset_index()\n",
+    "        req_month_df.set_index(['month indicies'], inplace=True)\n",
     "        start = i\n",
     "        break\n",
-    "query_month_df\n",
+    "req_month_df\n",
     "\n",
     "        \n",
-    "for t in range(start+1,len(query_month)):\n",
-    "    if query_month[t] is not NaN:\n",
-    "        new_month_df = pd.DataFrame({'user'+str(t+1):query_month[t]}).rename_axis('month indicies').reset_index()\n",
+    "for t in range(len(req_month)):\n",
+    "    if req_month[t] is not NaN and t is not start:\n",
+    "        new_month_df = pd.DataFrame({'user'+str(t+1):req_month[t]}).rename_axis('month indicies').reset_index()\n",
     "        new_month_df.set_index(['month indicies'], inplace=True)\n",
-    "        query_month_df = query_month_df.join(new_month_df,how='outer')\n",
+    "        req_month_df = req_month_df.join(new_month_df,how='outer')\n",
     "\n",
-    "query_month_df"
+    "req_month_df"
    ]
   },
   {
@@ -375,11 +428,11 @@
    "outputs": [],
    "source": [
     "# plot a graph for all valid users\n",
-    "graph_month = query_month_df.plot(kind='bar',figsize=(12,8),title='query times in a month',fontsize=18)\n",
+    "graph_month = req_month_df.plot(kind='bar',figsize=(12,8),title='request times in a month',fontsize=18)\n",
     "graph_month.title.set_size(20)\n",
     "plt.xlabel('month indicies',fontsize=16)\n",
-    "plt.ylabel('query times', fontsize=16)\n",
-    "graph_day.yaxis.set_major_locator(MaxNLocator(integer=True))"
+    "plt.ylabel('request times', fontsize=16)\n",
+    "graph_month.yaxis.set_major_locator(MaxNLocator(integer=True))"
    ]
   },
   {
@@ -387,7 +440,7 @@
    "id": "satisfied-essay",
    "metadata": {},
    "source": [
-    "### Get query times for all valid users in January"
+    "### Get request times for all valid users in January"
    ]
   },
   {
@@ -408,10 +461,10 @@
    "outputs": [],
    "source": [
     "# plot data graph\n",
-    "graph_date = date_df.plot(kind='bar',figsize=(20,10),title='query times in January',fontsize=18)\n",
+    "graph_date = date_df.plot(kind='bar',figsize=(20,10),title='request times in January',fontsize=18)\n",
     "graph_date.title.set_size(20)\n",
     "plt.xlabel('date',fontsize=16)\n",
-    "plt.ylabel('query times', fontsize=16)\n",
+    "plt.ylabel('request times', fontsize=16)\n",
     "graph_date.yaxis.set_major_locator(MaxNLocator(integer=True))"
    ]
   },
@@ -419,10 +472,12 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "alpha-vermont",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "# subplots on January query times\n",
+    "# subplots on January request times\n",
     "date_ax_arr = date_df.plot(subplots=True,layout=(2,4),kind='bar',figsize=(16,8),fontsize=15,sharey =True)\n",
     "\n",
     "for ax in date_ax_arr[-1]:\n",
@@ -430,11 +485,63 @@
     "    \n",
     "for ax_arr in date_ax_arr:\n",
     "    ax_arr[0].yaxis.set_major_locator(MaxNLocator(integer=True))\n",
-    "    ax_arr[0].set_ylabel(\"query times\", fontsize=16)\n",
+    "    ax_arr[0].set_ylabel(\"request times\", fontsize=16)\n",
     "    \n",
     "date_ax_arr[-1][0].set_xticks(list(range(0,len(date_df.index),5)))\n",
     "date_ax_arr[-1][0].set_xticklabels(list(range(date_df.index[0],date_df.index[-1]+1,5)))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "applied-residence",
+   "metadata": {},
+   "source": [
+    "## Scatter(v-score on valid trips above cutoff, user input request proportion median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "olympic-guarantee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we use v-score on bins above cutoff after changing language and converting purposes and modes\n",
+    "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True,cutoff=True)\n",
+    "v_score_df = pd.DataFrame(data = {'v score':v_score_cvt}).dropna().reset_index(drop=True)\n",
+    "valid_v_score = v_score_df['v score'].values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "current-crime",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x=req_propor_median\n",
+    "y=valid_v_score\n",
+    "v=valid_users\n",
+    "cmp = cm.get_cmap('Dark2', len(valid_users))\n",
+    "\n",
+    "for i in range(len(valid_users)):\n",
+    "    plt.scatter(x[i], y[i], color=cmp.colors[i], label=v[i], s=70, alpha=0.7)\n",
+    "plt.legend(markerscale=0.7, scatterpoints=1)\n",
+    "plt.xlabel('user input request proportion median',fontsize=16)\n",
+    "plt.ylabel('v measure score',fontsize=16)\n",
+    "plt.xticks(fontsize=14)\n",
+    "plt.yticks(fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "velvet-sweden",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 302c6811d2a454d6a8f931c97336d2158ac44682 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Sat, 27 Mar 2021 17:38:52 -0700
Subject: [PATCH 12/16] modified evaluation code, changed to use homogeneity
 score for evaluation

---
 .../confirmed_trips_eval_bins_clusters.py     | 116 +++++++++--------
 .../user_input_request_times_all_users.ipynb  |  32 ++---
 .../v-measurel_bins_all_user.ipynb            | 121 +++++-------------
 ...urel_clusters_above_cutoff_all_users.ipynb |  61 +++------
 4 files changed, 126 insertions(+), 204 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index a538910..9131e61 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -22,12 +22,20 @@
 map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',
                'insurance_payment':'insurance'}
 
-def get_user_ls(all_users):
+def get_user_ls(all_users,radius):
     user_ls = []
+    valid_user_ls = []
     for i in range(len(all_users)):
         curr_user = 'user' + str(i + 1)
-        user_ls.append(curr_user)
-    return user_ls
+        user = all_users[i]
+        filter_trips,sim,trips = filter_data(user,radius)
+        if valid_user(filter_trips,trips):
+            valid_user_ls.append(curr_user)
+            user_ls.append(curr_user)
+        else:
+            user_ls.append(curr_user)
+            continue
+    return user_ls,valid_user_ls
 
 
 def filter_data(user,radius):
@@ -48,6 +56,44 @@ def valid_user(filter_trips,trips):
         valid = True
     return valid
 
+def map_labels(user_input_df,span_eng_dict,map_pur_dict,sp2en,cvt_pur_mo):
+    if sp2en:
+        # change language
+        user_input_df = user_input_df.replace(span_eng_dict)
+    elif cvt_pur_mo:
+        # change language first
+        user_input_df = user_input_df.replace(span_eng_dict)
+        # convert purpose
+        user_input_df = user_input_df.replace(map_pur_dict)
+        # convert mode
+        for a in range(len(user_input_df)):
+            if user_input_df.iloc[a]["replaced_mode"] == "same_mode":
+                # to see which row will be converted
+                logging.debug("The following rows will be changed: %s", user_input_df.iloc[a])
+                user_input_df.iloc[a]["replaced_mode"] = user_input_df.iloc[a]['mode_confirm']
+    return user_input_df
+
+
+def valid_user_check(filter_trips,trips,homo_score,comp_score,v_score):
+    if not valid_user(filter_trips, trips):
+        homo_score.append(NaN)
+        comp_score.append(NaN)
+        v_score.append(NaN)
+        skip = True
+    else:
+        skip = False
+    return homo_score,comp_score,v_score,skip
+
+
+def compute_score(labels_true,labels_pred,homo_score,comp_score,v_score):
+    homo = metrics.homogeneity_score(labels_true, labels_pred)
+    homo_score.append(float('%.3f' % homo))
+    comp = metrics.completeness_score(labels_true, labels_pred)
+    comp_score.append(float('%.3f' % comp))
+    v = metrics.v_measure_score(labels_true, labels_pred)
+    v_score.append(float('%.3f' % v))
+    return homo_score,comp_score,v_score
+
 
 # v_measure_bins takes 5 parameters
 # - sp2en=True: change Spanish to English
@@ -63,13 +109,10 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
         user = all_users[i]
         filter_trips,sim,trips = filter_data(user,radius)
 
-        # filter out users that haven't enough trips (at least 10 valid trips
-        # and 50% of total trips are valid) to analyze
-        if not valid_user(filter_trips,trips):
-            homo_score.append(NaN)
-            comp_score.append(NaN)
-            v_score.append(NaN)
+        homo_score,comp_score,v_score,skip = valid_user_check(filter_trips,trips,homo_score,comp_score,v_score)
+        if skip:
             continue
+
         sim.bin_data()
         if cutoff is None:
             trip_index_ls = []
@@ -84,23 +127,13 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
             bin_trips = sim.newdata
             bins = sim.bins
 
-        bin_trips_df = pd.DataFrame(data=[trip["data"]["user_input"] for trip in bin_trips])
-
-        if sp2en:
-            bin_trips_df = bin_trips_df.replace(span_eng_dict)
-        elif cvt_pur_mo:
-            bin_trips_df = bin_trips_df.replace(span_eng_dict)
-            bin_trips_df = bin_trips_df.replace(map_pur_dict)
-            for a in range(len(bin_trips_df)):
-                if bin_trips_df.iloc[a]["replaced_mode"] == "same_mode":
-                    # to see which row will be converted
-                    logging.debug("The following rows will be changed: %s", bin_trips_df.iloc[a])
-                    bin_trips_df.iloc[a]["replaced_mode"] = bin_trips_df.iloc[a]['mode_confirm']
+        bin_trips_user_input_df = pd.DataFrame(data=[trip["data"]["user_input"] for trip in bin_trips])
+        bin_trips_user_input_df = map_labels(bin_trips_user_input_df, span_eng_dict, map_pur_dict, sp2en, cvt_pur_mo)
 
         # turn all user_input into list without binning
-        bin_trips_user_input_ls = bin_trips_df.values.tolist()
+        bin_trips_user_input_ls = bin_trips_user_input_df.values.tolist()
         # drop duplicate user_input
-        no_dup_df = bin_trips_df.drop_duplicates()
+        no_dup_df = bin_trips_user_input_df.drop_duplicates()
         # turn non-duplicate user_input into list
         no_dup_list = no_dup_df.values.tolist()
 
@@ -125,12 +158,7 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
         bins_ts = pd.DataFrame(data=[filter_trips[i]["data"]["start_ts"] for i in bin_ls])
         # compare two data frames, the program will continue to score calculation if two data frames are the same
         assert_frame_equal(bins_ts, bin_trips_ts)
-        homo = metrics.homogeneity_score(labels_true, labels_pred)
-        homo_score.append(float('%.3f' % homo))
-        comp = metrics.completeness_score(labels_true, labels_pred)
-        comp_score.append(float('%.3f' % comp))
-        v = metrics.v_measure_score(labels_true, labels_pred)
-        v_score.append(float('%.3f' % v))
+        homo_score, comp_score, v_score = compute_score(labels_true, labels_pred, homo_score, comp_score, v_score)
 
     return homo_score, comp_score, v_score
 
@@ -148,12 +176,10 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
         user = all_users[i]
         filter_trips,sim,trips = filter_data(user,radius)
 
-        # filter out users that haven't enough trips to analyze
-        if not valid_user(filter_trips,trips):
-            homo_score.append(NaN)
-            comp_score.append(NaN)
-            v_score.append(NaN)
+        homo_score,comp_score,v_score,skip = valid_user_check(filter_trips,trips,homo_score,comp_score,v_score)
+        if skip:
             continue
+
         sim.bin_data()
         sim.delete_bins()
         bin_trips = sim.newdata
@@ -166,19 +192,7 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
         feat.cluster(min_clusters=min, max_clusters=max)
         cluster_trips = feat.data
         cluster_user_input_df = pd.DataFrame(data=[i["data"]["user_input"] for i in cluster_trips])
-        if sp2en:
-            # change language
-            cluster_user_input_df = cluster_user_input_df.replace(span_eng_dict)
-            cluster_user_input_ls = cluster_user_input_df.values.tolist()
-        elif cvt_pur_mo:
-            # change language first
-            cluster_user_input_df = cluster_user_input_df.replace(span_eng_dict)
-            # convert purpose
-            cluster_user_input_df = cluster_user_input_df.replace(map_pur_dict)
-            # convert mode
-            for a in range(len(cluster_user_input_df)):
-                if cluster_user_input_df.iloc[a]["replaced_mode"] == "same_mode":
-                    cluster_user_input_df.iloc[a]["replaced_mode"] = cluster_user_input_df.iloc[a]['mode_confirm']
+        cluster_user_input_df = map_labels(cluster_user_input_df, span_eng_dict, map_pur_dict, sp2en, cvt_pur_mo)
         # turn cluster_trips to list without any changes
         cluster_user_input_ls = cluster_user_input_df.values.tolist()
         # drop duplicate user_input
@@ -203,13 +217,7 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
         cluster_ps_df = pd.DataFrame(data=cluster_ps)
         label_ps_df = pd.DataFrame(data=feat.points)
         assert_frame_equal(cluster_ps_df, label_ps_df)
-
-        homo = metrics.homogeneity_score(labels_true, labels_pred)
-        homo_score.append(float('%.3f' % homo))
-        comp = metrics.completeness_score(labels_true, labels_pred)
-        comp_score.append(float('%.3f' % comp))
-        v = metrics.v_measure_score(labels_true, labels_pred)
-        v_score.append(float('%.3f' % v))
+        homo_score, comp_score, v_score = compute_score(labels_true, labels_pred, homo_score, comp_score, v_score)
 
     return homo_score, comp_score, v_score
 
diff --git a/tour_model_eval/user_input_request_times_all_users.ipynb b/tour_model_eval/user_input_request_times_all_users.ipynb
index 8d4528d..f78a808 100644
--- a/tour_model_eval/user_input_request_times_all_users.ipynb
+++ b/tour_model_eval/user_input_request_times_all_users.ipynb
@@ -73,7 +73,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "incident-hampton",
+   "id": "arabic-growth",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "frozen-stanford",
+   "id": "progressive-playback",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,7 +156,7 @@
     "date_df.set_index(['date'], inplace=True)\n",
     "\n",
     "# get valid user list\n",
-    "valid_users = []\n",
+    "user_ls,valid_users = evaluation.get_user_ls(all_users,radius)\n",
     "\n",
     "for a in range(len(all_users)):\n",
     "    user = all_users[a]\n",
@@ -168,8 +168,6 @@
     "        req_day.append(NaN)\n",
     "        req_month.append(NaN) \n",
     "        continue\n",
-    "    val_user = 'user' + str(a + 1)\n",
-    "    valid_users.append(val_user)\n",
     "    sim.bin_data()\n",
     "    sim.delete_bins()\n",
     "    bins = sim.bins\n",
@@ -424,7 +422,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "naked-february",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "# plot a graph for all valid users\n",
@@ -493,36 +493,36 @@
   },
   {
    "cell_type": "markdown",
-   "id": "applied-residence",
+   "id": "endless-copper",
    "metadata": {},
    "source": [
-    "## Scatter(v-score on valid trips above cutoff, user input request proportion median)"
+    "## Scatter(homogeneity score on valid trips above cutoff, user input request proportion median)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "olympic-guarantee",
+   "id": "creative-restaurant",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Here we use v-score on bins above cutoff after changing language and converting purposes and modes\n",
+    "# Here we use homogeneity score on bins above cutoff after changing language and converting purposes and modes\n",
     "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True,cutoff=True)\n",
-    "v_score_df = pd.DataFrame(data = {'v score':v_score_cvt}).dropna().reset_index(drop=True)\n",
-    "valid_v_score = v_score_df['v score'].values.tolist()"
+    "homo_score_df = pd.DataFrame(data = {'homo score':homo_score_cvt}).dropna().reset_index(drop=True)\n",
+    "valid_homo_score = homo_score_df['homo score'].values.tolist()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "current-crime",
+   "id": "serious-default",
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "x=req_propor_median\n",
-    "y=valid_v_score\n",
+    "y=valid_homo_score\n",
     "v=valid_users\n",
     "cmp = cm.get_cmap('Dark2', len(valid_users))\n",
     "\n",
@@ -530,7 +530,7 @@
     "    plt.scatter(x[i], y[i], color=cmp.colors[i], label=v[i], s=70, alpha=0.7)\n",
     "plt.legend(markerscale=0.7, scatterpoints=1)\n",
     "plt.xlabel('user input request proportion median',fontsize=16)\n",
-    "plt.ylabel('v measure score',fontsize=16)\n",
+    "plt.ylabel('homogeneity score',fontsize=16)\n",
     "plt.xticks(fontsize=14)\n",
     "plt.yticks(fontsize=14)"
    ]
@@ -538,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "velvet-sweden",
+   "id": "double-newman",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
index a5fdab5..bb7a27a 100644
--- a/tour_model_eval/v-measurel_bins_all_user.ipynb
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -22,7 +22,8 @@
     "from numpy import *\n",
     "import confirmed_trips_eval_bins_clusters as evaluation\n",
     "from sklearn import metrics\n",
-    "from pandas.testing import assert_frame_equal"
+    "from pandas.testing import assert_frame_equal\n",
+    "import numpy as np"
    ]
   },
   {
@@ -53,7 +54,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "user_ls = evaluation.get_user_ls(all_users)"
+    "user_ls,_ = evaluation.get_user_ls(all_users,radius)"
    ]
   },
   {
@@ -93,16 +94,6 @@
     "homo_score_ori, comp_score_ori, v_score_ori = evaluation.v_measure_bins(all_users,radius,cutoff=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cooked-louis",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_ori=round(mean([x for x in v_score_ori if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "personalized-stable",
@@ -123,16 +114,6 @@
     "homo_score_sp2en, comp_score_sp2en, v_score_sp2en = evaluation.v_measure_bins(all_users,radius,sp2en=True,cutoff=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "frequent-niagara",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_sp2en=round(mean([x for x in v_score_sp2en if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "connected-haven",
@@ -151,16 +132,6 @@
     "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True,cutoff=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "primary-tampa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_cvt=round(mean([x for x in v_score_cvt if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "agricultural-syndicate",
@@ -176,10 +147,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cutoff_df = pd.DataFrame(data={'homogeneity_score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
-    "                        'completeness_score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
-    "                       'v_measure_score':[v_score_ori,v_score_sp2en,v_score_cvt],\n",
-    "                        'mean v_measure_score':[mean_v_ori,mean_v_sp2en,mean_v_cvt]},\n",
+    "cutoff_df = pd.DataFrame(data={'homogeneity score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
+    "                        'completeness score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
+    "                       'v-measure score':[v_score_ori,v_score_sp2en,v_score_cvt]},\n",
     "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
     "cutoff_df"
    ]
@@ -189,7 +159,7 @@
    "id": "conservative-pregnancy",
    "metadata": {},
    "source": [
-    "#### homogeneity_score"
+    "#### homogeneity score"
    ]
   },
   {
@@ -201,7 +171,8 @@
    "source": [
     "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
-    "                           index=user_ls)"
+    "                           index=user_ls).dropna()\n",
+    "homo_df"
    ]
   },
   {
@@ -211,7 +182,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "homo_df.plot(kind = 'bar',title='homogeneity score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -219,7 +190,7 @@
    "id": "incorporated-google",
    "metadata": {},
    "source": [
-    "#### completeness_score"
+    "#### completeness score"
    ]
   },
   {
@@ -231,7 +202,7 @@
    "source": [
     "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
-    "                      index=user_ls)"
+    "                      index=user_ls).dropna()"
    ]
   },
   {
@@ -241,7 +212,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "comp_df.plot(kind = 'bar',title='completeness score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -249,7 +220,7 @@
    "id": "living-archives",
    "metadata": {},
    "source": [
-    "#### v_measure_score"
+    "#### v-measure score"
    ]
   },
   {
@@ -261,17 +232,20 @@
    "source": [
     "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
     "                          'after converting purposes and replaced mode':v_score_cvt},\n",
-    "                   index=user_ls)"
+    "                   index=user_ls).dropna()\n",
+    "v_df"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "demonstrated-resident",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "v_df.plot(kind = 'bar',title='v-measure score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -300,16 +274,6 @@
     "ab_homo_score_ori, ab_comp_score_ori, ab_v_score_ori = evaluation.v_measure_bins(all_users,radius)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "extended-processing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ab_mean_v_ori=round(mean([x for x in ab_v_score_ori if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "european-philadelphia",
@@ -328,16 +292,6 @@
     "ab_homo_score_sp2en, ab_comp_score_sp2en, ab_v_score_sp2en = evaluation.v_measure_bins(all_users,radius,sp2en=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "collective-calculator",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ab_mean_v_sp2en=round(mean([x for x in ab_v_score_sp2en if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "capable-bathroom",
@@ -356,16 +310,6 @@
     "ab_homo_score_cvt, ab_comp_score_cvt, ab_v_score_cvt = evaluation.v_measure_bins(all_users,radius,cvt_pur_mo=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "transparent-theology",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ab_mean_v_cvt=round(mean([x for x in ab_v_score_cvt if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "charged-greeting",
@@ -381,10 +325,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "all_df = pd.DataFrame(data={'homogeneity_score':[ab_homo_score_ori,ab_homo_score_sp2en,ab_homo_score_cvt],\n",
-    "                        'completeness_score':[ab_comp_score_ori,ab_comp_score_sp2en,ab_comp_score_cvt],\n",
-    "                       'v_measure_score':[ab_v_score_ori,ab_v_score_sp2en,ab_v_score_cvt],\n",
-    "                        'mean v_measure_score':[ab_mean_v_ori,ab_mean_v_sp2en,ab_mean_v_cvt]},\n",
+    "all_df = pd.DataFrame(data={'homogeneity score':[ab_homo_score_ori,ab_homo_score_sp2en,ab_homo_score_cvt],\n",
+    "                        'completeness score':[ab_comp_score_ori,ab_comp_score_sp2en,ab_comp_score_cvt],\n",
+    "                       'v-measure score':[ab_v_score_ori,ab_v_score_sp2en,ab_v_score_cvt]},\n",
     "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
     "all_df"
    ]
@@ -394,7 +337,7 @@
    "id": "trying-flesh",
    "metadata": {},
    "source": [
-    "#### homogeneity_score"
+    "#### homogeneity score"
    ]
   },
   {
@@ -406,7 +349,7 @@
    "source": [
     "ab_homo_df=pd.DataFrame(data={'original user input':ab_homo_score_ori,'after translation':ab_homo_score_sp2en,\n",
     "                              'after converting purposes and replaced mode':ab_homo_score_cvt},\n",
-    "                        index=user_ls)"
+    "                        index=user_ls).dropna()"
    ]
   },
   {
@@ -416,7 +359,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ab_homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "ab_homo_df.plot(kind = 'bar',title='homogeneity score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -424,7 +367,7 @@
    "id": "english-quick",
    "metadata": {},
    "source": [
-    "#### completeness_score"
+    "#### completeness score"
    ]
   },
   {
@@ -436,7 +379,7 @@
    "source": [
     "ab_comp_df = pd.DataFrame(data={'original user input':ab_comp_score_ori,'after translation':ab_comp_score_sp2en,\n",
     "                                'after converting purposes and replaced mode':ab_comp_score_cvt},\n",
-    "                          index=user_ls)"
+    "                          index=user_ls).dropna()"
    ]
   },
   {
@@ -446,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ab_comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "ab_comp_df.plot(kind = 'bar',title='completeness score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -454,7 +397,7 @@
    "id": "white-reasoning",
    "metadata": {},
    "source": [
-    "#### v_measure_score"
+    "#### v-measure score"
    ]
   },
   {
@@ -466,7 +409,7 @@
    "source": [
     "ab_v_df = pd.DataFrame(data={'original user input':ab_v_score_ori,'after translation':ab_v_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':ab_v_score_cvt},\n",
-    "                       index=user_ls)"
+    "                       index=user_ls).dropna()"
    ]
   },
   {
@@ -476,7 +419,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ab_v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "ab_v_df.plot(kind = 'bar',title='v-measure score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   }
  ],
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
index a35b9a6..af38b51 100644
--- a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -20,7 +20,8 @@
     "from numpy import *\n",
     "import confirmed_trips_eval_bins_clusters as evaluation\n",
     "from sklearn import metrics\n",
-    "from pandas.testing import assert_frame_equal"
+    "from pandas.testing import assert_frame_equal\n",
+    "import numpy as np"
    ]
   },
   {
@@ -51,7 +52,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "user_ls = evaluation.get_user_ls(all_users)"
+    "user_ls,_ = evaluation.get_user_ls(all_users,radius)"
    ]
   },
   {
@@ -91,16 +92,6 @@
     "homo_score_ori, comp_score_ori, v_score_ori = evaluation.v_measure_clusters(all_users,radius)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "professional-evans",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_ori=round(mean([x for x in v_score_ori if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "latin-boulder",
@@ -119,16 +110,6 @@
     "homo_score_sp2en, comp_score_sp2en, v_score_sp2en = evaluation.v_measure_clusters(all_users,radius,sp2en=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "lesbian-testing",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_sp2en=round(mean([x for x in v_score_sp2en if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "opened-norway",
@@ -147,16 +128,6 @@
     "homo_score_cvt, comp_score_cvt, v_score_cvt = evaluation.v_measure_clusters(all_users,radius,cvt_pur_mo=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "severe-plant",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_v_cvt=round(mean([x for x in v_score_cvt if str(x) != 'nan']),3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "matched-custody",
@@ -172,10 +143,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame(data={'homogeneity_score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
-    "                        'completeness_score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
-    "                       'v_measure_score':[v_score_ori,v_score_sp2en,v_score_cvt],\n",
-    "                        'mean v_measure_score':[mean_v_ori,mean_v_sp2en,mean_v_cvt]},\n",
+    "df = pd.DataFrame(data={'homogeneity score':[homo_score_ori,homo_score_sp2en,homo_score_cvt],\n",
+    "                        'completeness score':[comp_score_ori,comp_score_sp2en,comp_score_cvt],\n",
+    "                       'v-measure score':[v_score_ori,v_score_sp2en,v_score_cvt]},\n",
     "                  index = ['original user input','after translation','after converting purposes and replaced mode'])\n",
     "df"
    ]
@@ -185,7 +155,7 @@
    "id": "sacred-applicant",
    "metadata": {},
    "source": [
-    "#### homogeneity_score"
+    "#### homogeneity score"
    ]
   },
   {
@@ -197,7 +167,7 @@
    "source": [
     "homo_df = pd.DataFrame(data={'original user input':homo_score_ori,'after translation':homo_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':homo_score_cvt}, \n",
-    "                       index=user_ls)"
+    "                       index=user_ls).dropna()"
    ]
   },
   {
@@ -207,7 +177,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "homo_df.plot(kind = 'bar',title='homogeneity_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "homo_df.plot(kind = 'bar',title='homogeneity score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -215,7 +185,7 @@
    "id": "premium-force",
    "metadata": {},
    "source": [
-    "#### completeness_score"
+    "#### completeness score"
    ]
   },
   {
@@ -227,7 +197,7 @@
    "source": [
     "comp_df = pd.DataFrame(data={'original user input':comp_score_ori,'after translation':comp_score_sp2en,\n",
     "                             'after converting purposes and replaced mode':comp_score_cvt},\n",
-    "                       index=user_ls)"
+    "                       index=user_ls).dropna()"
    ]
   },
   {
@@ -239,7 +209,7 @@
    },
    "outputs": [],
    "source": [
-    "comp_df.plot(kind = 'bar',title='completeness_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "comp_df.plot(kind = 'bar',title='completeness score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   },
   {
@@ -247,7 +217,7 @@
    "id": "affecting-daughter",
    "metadata": {},
    "source": [
-    "#### v_measure_score"
+    "#### v-measure score"
    ]
   },
   {
@@ -259,7 +229,8 @@
    "source": [
     "v_df = pd.DataFrame(data={'original user input':v_score_ori,'after translation':v_score_sp2en,\n",
     "                          'after converting purposes and replaced mode':v_score_cvt},\n",
-    "                    index=user_ls)"
+    "                    index=user_ls).dropna()\n",
+    "v_df"
    ]
   },
   {
@@ -269,7 +240,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "v_df.plot(kind = 'bar',title='v_measure_score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
+    "v_df.plot(kind = 'bar',title='v-measure score',yticks=(0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1))"
    ]
   }
  ],

From 0e7f803645bfa8cb8926b2cf356c2848f5ad85e8 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Mon, 29 Mar 2021 19:51:36 -0700
Subject: [PATCH 13/16] modified evaluation code and added comments in the
 notebooks

---
 .../confirmed_trips_eval_bins_clusters.py     | 26 ++++++++++---------
 .../user_input_request_times_all_users.ipynb  | 18 ++++++-------
 .../v-measurel_all_bins_single_user.ipynb     |  8 ++++++
 .../v-measurel_bins_all_user.ipynb            |  8 ++++++
 ...urel_clusters_above_cutoff_all_users.ipynb |  8 ++++++
 .../v-measurel_cutoff_bins_single_user.ipynb  |  8 ++++++
 ...measurel_cutoff_clusters_single_user.ipynb |  8 ++++++
 7 files changed, 63 insertions(+), 21 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index 9131e61..fb4dffa 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -12,15 +12,6 @@
 from sklearn import metrics
 from pandas.testing import assert_frame_equal
 
-# Spanish words to English
-span_eng_dict = {'revisado_bike':'test ride with bike','placas_de carro':'car plates','aseguranza':'insurance',
- 'iglesia':'church','curso':'course','mi_hija recién aliviada':'my daughter just had a new baby',
- 'servicio_comunitario':'community service','pago_de aseguranza':'insurance payment',
- 'grupo_comunitario':'community group','caminata_comunitaria':'community walk'}
-
-# Convert purpose
-map_pur_dict = {'course':'school','work_- lunch break':'lunch_break','on_the way home':'home',
-               'insurance_payment':'insurance'}
 
 def get_user_ls(all_users,radius):
     user_ls = []
@@ -56,7 +47,18 @@ def valid_user(filter_trips,trips):
         valid = True
     return valid
 
-def map_labels(user_input_df,span_eng_dict,map_pur_dict,sp2en,cvt_pur_mo):
+def map_labels(user_input_df,sp2en,cvt_pur_mo):
+    # Spanish words to English
+    span_eng_dict = {'revisado_bike': 'test ride with bike', 'placas_de carro': 'car plates', 'aseguranza': 'insurance',
+                     'iglesia': 'church', 'curso': 'course',
+                     'mi_hija recién aliviada': 'my daughter just had a new baby',
+                     'servicio_comunitario': 'community service', 'pago_de aseguranza': 'insurance payment',
+                     'grupo_comunitario': 'community group', 'caminata_comunitaria': 'community walk'}
+
+    # Convert purpose
+    map_pur_dict = {'course': 'school', 'work_- lunch break': 'lunch_break', 'on_the way home': 'home',
+                    'insurance_payment': 'insurance'}
+
     if sp2en:
         # change language
         user_input_df = user_input_df.replace(span_eng_dict)
@@ -128,7 +130,7 @@ def v_measure_bins(all_users,radius,sp2en=None,cvt_pur_mo=None,cutoff=None):
             bins = sim.bins
 
         bin_trips_user_input_df = pd.DataFrame(data=[trip["data"]["user_input"] for trip in bin_trips])
-        bin_trips_user_input_df = map_labels(bin_trips_user_input_df, span_eng_dict, map_pur_dict, sp2en, cvt_pur_mo)
+        bin_trips_user_input_df = map_labels(bin_trips_user_input_df, sp2en, cvt_pur_mo)
 
         # turn all user_input into list without binning
         bin_trips_user_input_ls = bin_trips_user_input_df.values.tolist()
@@ -192,7 +194,7 @@ def v_measure_clusters(all_users,radius,sp2en=None,cvt_pur_mo=None):
         feat.cluster(min_clusters=min, max_clusters=max)
         cluster_trips = feat.data
         cluster_user_input_df = pd.DataFrame(data=[i["data"]["user_input"] for i in cluster_trips])
-        cluster_user_input_df = map_labels(cluster_user_input_df, span_eng_dict, map_pur_dict, sp2en, cvt_pur_mo)
+        cluster_user_input_df = map_labels(cluster_user_input_df, sp2en, cvt_pur_mo)
         # turn cluster_trips to list without any changes
         cluster_user_input_ls = cluster_user_input_df.values.tolist()
         # drop duplicate user_input
diff --git a/tour_model_eval/user_input_request_times_all_users.ipynb b/tour_model_eval/user_input_request_times_all_users.ipynb
index f78a808..c6c3973 100644
--- a/tour_model_eval/user_input_request_times_all_users.ipynb
+++ b/tour_model_eval/user_input_request_times_all_users.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "competitive-involvement",
+   "metadata": {},
+   "source": [
+    "This notebook integrates user inputs request times in a day, in a month, and in January 2021 for all users. The scatter at the bottom shows the relationship between homogeneity score and the median of user input request proportion in a day on valid common trips after the first round clustering for all users."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -202,7 +210,7 @@
     "    # bins below cutoff\n",
     "    bl_bins = sim.below_cutoff\n",
     "    \n",
-    "    # collect query trips indices below cutoff\n",
+    "    # collect requested trips indices below cutoff\n",
     "    bl_trip_ls = []\n",
     "    for bin in bl_bins:\n",
     "        for trip_index in bin:\n",
@@ -534,14 +542,6 @@
     "plt.xticks(fontsize=14)\n",
     "plt.yticks(fontsize=14)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "double-newman",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/tour_model_eval/v-measurel_all_bins_single_user.ipynb b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
index 0b0e3f0..d2fb745 100644
--- a/tour_model_eval/v-measurel_all_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_all_bins_single_user.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "operating-disabled",
+   "metadata": {},
+   "source": [
+    "This notebook is for evaluating all valid bins and exploring data for a single user after first round clustering."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tour_model_eval/v-measurel_bins_all_user.ipynb b/tour_model_eval/v-measurel_bins_all_user.ipynb
index bb7a27a..6486ff6 100644
--- a/tour_model_eval/v-measurel_bins_all_user.ipynb
+++ b/tour_model_eval/v-measurel_bins_all_user.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "visible-annual",
+   "metadata": {},
+   "source": [
+    "This notebook shows the homogeneity scores, completeness scores, and v-measure scores based on original user inputs, user inputs after converting language, and user inputs after converting purposes and replaced mode on valid bins above cutoff and on all valid bins for all users after the first round clustering."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
index af38b51..4c40c4a 100644
--- a/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
+++ b/tour_model_eval/v-measurel_clusters_above_cutoff_all_users.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "proper-research",
+   "metadata": {},
+   "source": [
+    "This notebook shows the homogeneity scores, completeness scores, and v-measure scores based on original user inputs, user inputs after converting language, and user inputs after converting purposes and replaced mode on valid clusters above cutoff after the first round clustering for all users. The clustering process is based on silhouette score, setting the min cluster to 0."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
index a88733a..2906745 100644
--- a/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_bins_single_user.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aging-evaluation",
+   "metadata": {},
+   "source": [
+    "This notebook is for evaluating bins above cutoff and exploring the data for a single user after first round clustering."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
index e16031c..7bab80b 100644
--- a/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
+++ b/tour_model_eval/v-measurel_cutoff_clusters_single_user.ipynb
@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "developing-commerce",
+   "metadata": {},
+   "source": [
+    "This notebook is for evaluating clusters above cutoff and exploring data for a single user after first round clustering."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 8db9a7235783b5f9e6ed4ecfc16b83b8e8fb3ec1 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Mon, 5 Apr 2021 23:06:48 -0700
Subject: [PATCH 14/16] add more comments in evaluation code

---
 .../confirmed_trips_eval_bins_clusters.py     | 16 ++++
 .../user_input_request_times_all_users.ipynb  | 76 +++++++++----------
 2 files changed, 54 insertions(+), 38 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index fb4dffa..af11212 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -13,6 +13,8 @@
 from pandas.testing import assert_frame_equal
 
 
+# - user_ls: a list of all users
+# - valid_user_ls: a list of valid users
 def get_user_ls(all_users,radius):
     user_ls = []
     valid_user_ls = []
@@ -29,6 +31,8 @@ def get_user_ls(all_users,radius):
     return user_ls,valid_user_ls
 
 
+# - trips: all trips read from database
+# - filter_trips: valid trips that have user labels and are not points
 def filter_data(user,radius):
     trips = pipeline.read_data(uuid=user, key=esda.CONFIRMED_TRIP_KEY)
     non_empty_trips = [t for t in trips if t["data"]["user_input"] != {}]
@@ -37,16 +41,25 @@ def filter_data(user,radius):
     valid_trips_idx_ls = valid_trips_df.index.tolist()
     valid_trips = [non_empty_trips[i]for i in valid_trips_idx_ls]
 
+    # similarity codes can filter out trips that are points in valid_trips
     sim = similarity.similarity(valid_trips, radius)
     filter_trips = sim.data
     return filter_trips,sim,trips
 
+
+# to determine if the user is valid:
+# valid user should have >= 10 trips for further analysis and the proportion of filter_trips is >=50%
 def valid_user(filter_trips,trips):
     valid = False
     if len(filter_trips) >= 10 and len(filter_trips) / len(trips) >= 0.5:
         valid = True
     return valid
 
+
+# to map the user labels
+# - user_input_df: pass in original user input dataframe, return changed user input dataframe
+# - sp2en: change Spanish to English
+# - cvt_pur_mo: convert purposes and replaced mode
 def map_labels(user_input_df,sp2en,cvt_pur_mo):
     # Spanish words to English
     span_eng_dict = {'revisado_bike': 'test ride with bike', 'placas_de carro': 'car plates', 'aseguranza': 'insurance',
@@ -76,6 +89,8 @@ def map_labels(user_input_df,sp2en,cvt_pur_mo):
     return user_input_df
 
 
+# check if the user is valid
+# append NaN to the score lists when the user invalid
 def valid_user_check(filter_trips,trips,homo_score,comp_score,v_score):
     if not valid_user(filter_trips, trips):
         homo_score.append(NaN)
@@ -87,6 +102,7 @@ def valid_user_check(filter_trips,trips,homo_score,comp_score,v_score):
     return homo_score,comp_score,v_score,skip
 
 
+#  This function is to get homogeneity score, complete score, and v-score
 def compute_score(labels_true,labels_pred,homo_score,comp_score,v_score):
     homo = metrics.homogeneity_score(labels_true, labels_pred)
     homo_score.append(float('%.3f' % homo))
diff --git a/tour_model_eval/user_input_request_times_all_users.ipynb b/tour_model_eval/user_input_request_times_all_users.ipynb
index c6c3973..a3b0167 100644
--- a/tour_model_eval/user_input_request_times_all_users.ipynb
+++ b/tour_model_eval/user_input_request_times_all_users.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "competitive-involvement",
+   "id": "compact-toolbox",
    "metadata": {},
    "source": [
     "This notebook integrates user inputs request times in a day, in a month, and in January 2021 for all users. The scatter at the bottom shows the relationship between homogeneity score and the median of user input request proportion in a day on valid common trips after the first round clustering for all users."
@@ -95,12 +95,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def match_day(trip,bin):\n",
-    "    if bin:\n",
-    "        t = filter_trips[bin[0]]\n",
-    "        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']and trip['data']['start_local_dt']['day']==t['data']['start_local_dt']['day']:\n",
-    "            return True\n",
-    "    return False"
+    "# def match_day(trip,bin):\n",
+    "#     if bin:\n",
+    "#         t = filter_trips[bin[0]]\n",
+    "#         if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']and trip['data']['start_local_dt']['day']==t['data']['start_local_dt']['day']:\n",
+    "#             return True\n",
+    "#     return False"
    ]
   },
   {
@@ -110,12 +110,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def match_month(trip,bin):\n",
-    "    if bin:\n",
-    "        t = filter_trips[bin[0]]\n",
-    "        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']:\n",
-    "            return True\n",
-    "    return False"
+    "# def match_month(trip,bin):\n",
+    "#     if bin:\n",
+    "#         t = filter_trips[bin[0]]\n",
+    "#         if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']:\n",
+    "#             return True\n",
+    "#     return False"
    ]
   },
   {
@@ -125,29 +125,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# bin trips according to ['start_local_dt']\n",
-    "def bin_date(trip_ls,day=None,month=None):\n",
-    "    bin_date = []\n",
-    "    for trip_index in trip_ls:\n",
-    "        added = False\n",
-    "        trip = filter_trips[trip_index]\n",
+    "# # bin trips according to ['start_local_dt']\n",
+    "# def ,filter_trips,filter_trips,filter_trips,filter_trips(trip_ls,day=None,month=None):\n",
+    "#     bin_date = []\n",
+    "#     for trip_index in trip_ls:\n",
+    "#         added = False\n",
+    "#         trip = filter_trips[trip_index]\n",
     "\n",
-    "        for bin in bin_date:\n",
-    "            if day:\n",
-    "                if match_day(trip,bin):\n",
-    "                    bin.append(trip_index)\n",
-    "                    added = True\n",
-    "                    break\n",
-    "            if month:\n",
-    "                if match_month(trip,bin):\n",
-    "                    bin.append(trip_index)\n",
-    "                    added = True\n",
-    "                    break\n",
+    "#         for bin in bin_date:\n",
+    "#             if day:\n",
+    "#                 if evaluation.match_day(trip,bin,filter_trips):\n",
+    "#                     bin.append(trip_index)\n",
+    "#                     added = True\n",
+    "#                     break\n",
+    "#             if month:\n",
+    "#                 if evaluation.match_month(trip,bin,filter_trips):\n",
+    "#                     bin.append(trip_index)\n",
+    "#                     added = True\n",
+    "#                     break\n",
     "\n",
-    "        if not added:\n",
-    "            bin_date.append([trip_index])\n",
+    "#         if not added:\n",
+    "#             bin_date.append([trip_index])\n",
     "\n",
-    "    return bin_date       "
+    "#     return bin_date       "
    ]
   },
   {
@@ -221,7 +221,7 @@
     "    \n",
     "    \n",
     "    # collect request times in a day\n",
-    "    bin_day = bin_date(req_trips_ls,day=True)\n",
+    "    bin_day = evaluation.bin_date(req_trips_ls,filter_trips,day=True)\n",
     "    req_day_ls = []\n",
     "    for bin in bin_day:\n",
     "        req_day_ls.append(len(bin))\n",
@@ -231,7 +231,7 @@
     "        trip = filter_trips[trip_index]\n",
     "        match = False\n",
     "        for bin in bin_day:\n",
-    "            if match_day(trip,bin):\n",
+    "            if evaluation.match_day(trip,bin,filter_trips):\n",
     "                match = True\n",
     "                break\n",
     "        if not match:\n",
@@ -243,13 +243,13 @@
     "    # collect user input request proportion in a day\n",
     "    filter_trips_df = pd.DataFrame(filter_trips)\n",
     "    filter_trips_idx_ls = filter_trips_df.index.values.tolist()\n",
-    "    bin_filter_trips_day = bin_date(filter_trips_idx_ls,day=True)\n",
+    "    bin_filter_trips_day = evaluation.bin_date(filter_trips_idx_ls,filter_trips,day=True)\n",
     "    propor_single_user = []\n",
     "    for valid_trips_bin in bin_filter_trips_day:\n",
     "        match = False\n",
     "        for req_trips_bin in bin_day:\n",
     "            req_trip = filter_trips[req_trips_bin[0]]\n",
-    "            if match_day(req_trip,valid_trips_bin):\n",
+    "            if evaluation.match_day(req_trip,valid_trips_bin,filter_trips):\n",
     "                proportion = round(len(req_trips_bin)/len(valid_trips_bin), 2)\n",
     "                propor_single_user.append(proportion)\n",
     "                match = True\n",
@@ -265,7 +265,7 @@
     "   \n",
     "    \n",
     "    # collect request times in a month\n",
-    "    bin_month = bin_date(req_trips_ls,month=True)\n",
+    "    bin_month = evaluation.bin_date(req_trips_ls,filter_trips,month=True)\n",
     "    req_month_ls = []\n",
     "    for bin in bin_month:\n",
     "        req_month_ls.append(len(bin))\n",

From 56049d23b31b50fac59041d754399ce0ba1e32cb Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Wed, 7 Apr 2021 10:32:34 -0700
Subject: [PATCH 15/16] add more functions in the evaluation code

---
 .../confirmed_trips_eval_bins_clusters.py     | 60 +++++++++++++++++++
 1 file changed, 60 insertions(+)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index af11212..01aaef4 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -113,6 +113,66 @@ def compute_score(labels_true,labels_pred,homo_score,comp_score,v_score):
     return homo_score,comp_score,v_score
 
 
+# This function is to compare a trip with a group of trips to see if they happened in a same day
+def match_day(trip,bin,filter_trips):
+    if bin:
+        t = filter_trips[bin[0]]
+        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']\
+                and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']\
+                and trip['data']['start_local_dt']['day']==t['data']['start_local_dt']['day']:
+            return True
+    return False
+
+
+# This function is to compare a trip with a group of trips to see if they happened in a same month
+def match_month(trip,bin,filter_trips):
+    if bin:
+        t = filter_trips[bin[0]]
+        if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']\
+                and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']:
+            return True
+    return False
+
+
+# This function bins trips according to ['start_local_dt']
+def bin_date(trip_ls,filter_trips,day=None,month=None):
+    bin_date = []
+    for trip_index in trip_ls:
+        added = False
+        trip = filter_trips[trip_index]
+
+        for bin in bin_date:
+            if day:
+                if match_day(trip,bin,filter_trips):
+                    bin.append(trip_index)
+                    added = True
+                    break
+            if month:
+                if match_month(trip,bin,filter_trips):
+                    bin.append(trip_index)
+                    added = True
+                    break
+
+        if not added:
+            bin_date.append([trip_index])
+
+    return bin_date
+
+
+# compare the trip orders in bin_trips with those in filter_trips above cutoff
+def compare_trip_orders(bins,bin_trips,filter_trips):
+    # compare the trips order in bins and those in valid_trips using timestamp
+    bin_trips_ts = pd.DataFrame(data=[trip["data"]["start_ts"] for trip in bin_trips])
+    bin_ls = []
+    for bin in bins:
+        for index in bin:
+            bin_ls.append(index)
+    bins_ts = pd.DataFrame(data=[filter_trips[i]["data"]["start_ts"] for i in bin_ls])
+    # compare two data frames, the program will continue to score calculation if two data frames are the same
+    assert_frame_equal(bins_ts, bin_trips_ts)
+
+
+
 # v_measure_bins takes 5 parameters
 # - sp2en=True: change Spanish to English
 # - cvt_pur_mo=True: convert purposes and replaced mode

From 644c2dd1e64734d194270ccba0920f3bfe64d209 Mon Sep 17 00:00:00 2001
From: Chunrui Huang <corinnehcr@gmail.com>
Date: Thu, 8 Apr 2021 01:47:24 -0700
Subject: [PATCH 16/16] add find_first_trip function using start_ts

---
 .../confirmed_trips_eval_bins_clusters.py     | 15 ++++
 .../user_input_request_times_all_users.ipynb  | 79 +------------------
 2 files changed, 17 insertions(+), 77 deletions(-)

diff --git a/tour_model_eval/confirmed_trips_eval_bins_clusters.py b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
index 01aaef4..f28f271 100644
--- a/tour_model_eval/confirmed_trips_eval_bins_clusters.py
+++ b/tour_model_eval/confirmed_trips_eval_bins_clusters.py
@@ -172,6 +172,21 @@ def compare_trip_orders(bins,bin_trips,filter_trips):
     assert_frame_equal(bins_ts, bin_trips_ts)
 
 
+def find_first_trip(filter_trips,bin):
+    early_trip = filter_trips[bin[0]]
+    index = 0
+    for i in range(1,len(bin)):
+        compare_trip = filter_trips[bin[i]]
+        if early_trip['data']["start_ts"] > compare_trip['data']["start_ts"]:
+            early_trip = compare_trip
+            index = i
+    early_trip_index = bin[index]
+    return early_trip_index, index
+
+
+
+
+
 
 # v_measure_bins takes 5 parameters
 # - sp2en=True: change Spanish to English
diff --git a/tour_model_eval/user_input_request_times_all_users.ipynb b/tour_model_eval/user_input_request_times_all_users.ipynb
index a3b0167..95623b8 100644
--- a/tour_model_eval/user_input_request_times_all_users.ipynb
+++ b/tour_model_eval/user_input_request_times_all_users.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "compact-toolbox",
+   "id": "cellular-panel",
    "metadata": {},
    "source": [
     "This notebook integrates user inputs request times in a day, in a month, and in January 2021 for all users. The scatter at the bottom shows the relationship between homogeneity score and the median of user input request proportion in a day on valid common trips after the first round clustering for all users."
@@ -88,68 +88,6 @@
     "req_propor_median = []"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "destroyed-attention",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def match_day(trip,bin):\n",
-    "#     if bin:\n",
-    "#         t = filter_trips[bin[0]]\n",
-    "#         if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']and trip['data']['start_local_dt']['day']==t['data']['start_local_dt']['day']:\n",
-    "#             return True\n",
-    "#     return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "breeding-cream",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def match_month(trip,bin):\n",
-    "#     if bin:\n",
-    "#         t = filter_trips[bin[0]]\n",
-    "#         if trip['data']['start_local_dt']['year']==t['data']['start_local_dt']['year']and trip['data']['start_local_dt']['month']==t['data']['start_local_dt']['month']:\n",
-    "#             return True\n",
-    "#     return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "progressive-playback",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # bin trips according to ['start_local_dt']\n",
-    "# def ,filter_trips,filter_trips,filter_trips,filter_trips(trip_ls,day=None,month=None):\n",
-    "#     bin_date = []\n",
-    "#     for trip_index in trip_ls:\n",
-    "#         added = False\n",
-    "#         trip = filter_trips[trip_index]\n",
-    "\n",
-    "#         for bin in bin_date:\n",
-    "#             if day:\n",
-    "#                 if evaluation.match_day(trip,bin,filter_trips):\n",
-    "#                     bin.append(trip_index)\n",
-    "#                     added = True\n",
-    "#                     break\n",
-    "#             if month:\n",
-    "#                 if evaluation.match_month(trip,bin,filter_trips):\n",
-    "#                     bin.append(trip_index)\n",
-    "#                     added = True\n",
-    "#                     break\n",
-    "\n",
-    "#         if not added:\n",
-    "#             bin_date.append([trip_index])\n",
-    "\n",
-    "#     return bin_date       "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -184,20 +122,7 @@
     "    ab_trip_ls = []\n",
     "    no_req_trip_ls = []\n",
     "    for bin in bins:\n",
-    "        early_trip = filter_trips[bin[0]]\n",
-    "        index = 0\n",
-    "        for i in range(1,len(bin)):\n",
-    "            compare_trip = filter_trips[bin[i]]\n",
-    "            if early_trip['data']['start_local_dt']['year']>compare_trip['data']['start_local_dt']['year']:\n",
-    "                early_trip = compare_trip\n",
-    "                index = i\n",
-    "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']>compare_trip['data']['start_local_dt']['month']:\n",
-    "                early_trip = compare_trip\n",
-    "                index = i\n",
-    "            elif early_trip['data']['start_local_dt']['year']==compare_trip['data']['start_local_dt']['year'] and early_trip['data']['start_local_dt']['month']==compare_trip['data']['start_local_dt']['month'] and early_trip['data']['start_local_dt']['day']>compare_trip['data']['start_local_dt']['day']:\n",
-    "                early_trip = compare_trip\n",
-    "                index = i\n",
-    "        early_trip_index = bin[index]\n",
+    "        early_trip_index, index = evaluation.find_first_trip(filter_trips,bin)\n",
     "        ab_trip_ls.append(early_trip_index)\n",
     "        \n",
     "        for k in range(len(bin)):\n",