624_A4_BoruSun.ipynb

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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "from ydata_profiling import ProfileReport\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<bound method NDFrame.head of              0         1         2         3         4         5         6  \\\n",
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       "\n",
       "             7         8         9       0.1       1.1       2.1  3.1  4.1  \n",
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       "\n",
       "[6170 rows x 15 columns]>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "a4_data = pd.read_csv(\"./dataset/2023-02-08-DATA624-Assignment4-Data.csv\")\n",
    "a4_data.describe()\n",
    "a4_data.head\n",
    "# a4_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use Pandas Profiling to skim the dataset for data cleaning and column selections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile = ProfileReport(\n",
    "    a4_data, \n",
    "    title=\"Nifty Profiling Report\",\n",
    "    # dark_mode=True,\n",
    "    orange_mode=True,\n",
    "    minimal=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
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    {
     "data": {
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      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
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     "metadata": {},
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac0eb0a5a942468795efb10afe703345",
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      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile.to_file(\"profile_a4.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6170, 14)\n"
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>labels</th>\n",
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       "      <td>1</td>\n",
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       "      <td>-0.015960</td>\n",
       "      <td>0.015448</td>\n",
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       "      <td>0.828618</td>\n",
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       "          0         1         2         3         4         5         6  \\\n",
       "0 -1.606951  0.873530  0.354386 -0.376583 -0.123913 -5.574885  0.115763   \n",
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       "\n",
       "          7         8         9        10        11        12 labels  \n",
       "0  6.322857 -0.133692 -0.235926  0.849269  0.972069 -0.785170      0  \n",
       "1  5.940719  0.056806 -0.048602 -0.441113  0.368587 -1.788952      0  \n",
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     "execution_count": 62,
     "metadata": {},
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   ],
   "source": [
    "labels = a4_data.iloc[:,14].to_numpy()\n",
    "a4_data_df = a4_data.iloc[:,:13].to_numpy()\n",
    "# a4_data_df = a4_data_df.to_numpy()\n",
    "# print(a4_data_df.to_numpy)\n",
    "\n",
    "a4_data_df = pd.concat([\n",
    "    pd.DataFrame(a4_data_df),\n",
    "    pd.DataFrame(labels,columns=['labels']).astype('category')\n",
    "    # pd.DataFrame(a4_data.iloc[:,14]),\n",
    "],axis=1)\n",
    "a4_data_df.columns = [str(x) for x in a4_data_df.columns]\n",
    "# last_col_name = a4_data_df.columns[-1]\n",
    "# create a dictionary of column names to rename\n",
    "# new_col_names = {last_col_name: 'label'}\n",
    "# rename the columns using the rename() method\n",
    "# a4_data_df = a4_data_df.rename(columns=new_col_names)\n",
    "print(a4_data_df.shape)\n",
    "a4_data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Based on the result of profiling above, 3.1 column will be removed due to all constant zero value (will see how different it will be) 4.1 column will be keeped in the table, it might represent the target value.\n",
    "\n",
    "#####  Use KMeans to get general idea of estiamted number of clusters, then apply other algorithms based on the range of K. Create blobs, circle and moons shape for each one test with three following algorithms and see which one has the best performances.\n",
    "\n",
    "#### To discover how many clusters in the dataset (check all pairs), will use Agglomerative clustering (for detecting Large n_samples and n_clusters) and OPTICS (for detecting Large n_clusters and n_samples also for Large dataset, outlier removal, data reduction). The last one will use t-Distributed Stochastic Neighbor Embedding (t-SNE) to see how data separated into clusters. "
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    "sscore = []\n",
    "# starting with 2 clusters now becuase the silhouette isn't defined for 1\n",
    "for i in range(2,10):\n",
    "    kmeans = KMeans(\n",
    "        n_clusters = i, # Number of clusters to find\n",
    "        init = \"k-means++\", # How to place the initial cluster centroids,\n",
    "        max_iter= 100, # Maximum number of iterations for the algorithm to run\n",
    "        tol=0.0001, # Roughly how much the centroids need to change between iterations to keep going\n",
    "    ).fit(\n",
    "        a4_data_df\n",
    "    )\n",
    "    \n",
    "    silhouette_avg = silhouette_score(a4_data_df, kmeans.labels_)\n",
    "    sscore.append(silhouette_avg)\n",
    "    \n",
    "px.line(y = sscore,x = range(2,10), markers=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 64,
     "metadata": {},
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   "source": [
    "kmeans = KMeans(\n",
    "        n_clusters = 4, # Number of clusters to find\n",
    "        init = \"k-means++\", # How to place the initial cluster centroids,\n",
    "        max_iter= 100, # Maximum number of iterations for the algorithm to run\n",
    "        tol=0.0001, # Roughly how much the centroids need to change between iterations to keep going\n",
    "    ).fit(\n",
    "        a4_data_df\n",
    "    )\n",
    "\n",
    "data_kmeans =pd.concat([\n",
    "    a4_data_df,\n",
    "    pd.DataFrame(\n",
    "        kmeans.labels_,\n",
    "        columns=['cluster']\n",
    "    ).astype('category')\n",
    "], axis=1)\n",
    "\n",
    "# create a color map based on unique values in 'cluster' column\n",
    "color_map = plt.cm.get_cmap('Set1')\n",
    "# convert 'cluster' column to a sequence of numbers\n",
    "colors = data_kmeans['cluster'].astype('category').cat.codes\n",
    "\n",
    "# plot scatter plot with colors based on 'cluster' column\n",
    "data_kmeans.plot.scatter(x='0', y='1', c=colors, cmap=color_map, marker='o',s=10, linewidths=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(\n",
    "        n_clusters = 4, # Number of clusters to find\n",
    "        init = \"k-means++\", # How to place the initial cluster centroids,\n",
    "        max_iter= 100, # Maximum number of iterations for the algorithm to run\n",
    "        tol=0.0001, # Roughly how much the centroids need to change between iterations to keep going\n",
    "    ).fit(\n",
    "        a4_data_df\n",
    "    )\n",
    "\n",
    "data_kmeans =pd.concat([\n",
    "        a4_data_df,\n",
    "        pd.DataFrame(\n",
    "            kmeans.labels_,\n",
    "            columns=['cluster']\n",
    "        ).astype('category')\n",
    "    ], axis=1)\n",
    "\n",
    "fig = px.scatter(\n",
    "    data_kmeans, \n",
    "    x='0', \n",
    "    y='1', \n",
    "    color='cluster',\n",
    "    color_continuous_scale='Set1',\n",
    "    width=800,\n",
    "    height=600,\n",
    "    title='Clustering with KMeans'\n",
    ")\n",
    "data_kmeans.to_csv('./dataset/a4_data_with_cluster.csv')"
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     "metadata": {},
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   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "sscore = []\n",
    "\n",
    "for i in range(2,10):\n",
    "    clusters = AgglomerativeClustering(\n",
    "        n_clusters = i, # Number of clusters to find\n",
    "        # linkage=\"ward\", #default\n",
    "        linkage=\"average\",\n",
    "    ).fit(\n",
    "        a4_data_df\n",
    "    )\n",
    "    \n",
    "    silhouette_avg = silhouette_score(a4_data_df, clusters.labels_)\n",
    "    sscore.append(silhouette_avg)\n",
    "    \n",
    "px.line(y = sscore,x = range(2,10), markers=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='1'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# In this regard, single linkage is the worst strategy, and Ward gives the most regular sizes. \n",
    "# However, the affinity (or distance used in clustering) cannot be varied with Ward, thus for \n",
    "# non Euclidean metrics, average linkage is a good alternative. Single linkage, while not robust \n",
    "# to noisy data, can be computed very efficiently and can therefore be useful to provide hierarchical \n",
    "# clustering of larger datasets. Single linkage can also perform well on non-globular data.\n",
    "\n",
    "clusters_agg = AgglomerativeClustering(\n",
    "        n_clusters = 4, # Number of clusters to find\n",
    "        # linkage=\"ward\", #default\n",
    "        linkage=\"average\",\n",
    "    ).fit(\n",
    "        a4_data_df\n",
    "    )\n",
    "\n",
    "data_agg = pd.concat([\n",
    "    a4_data_df,\n",
    "    pd.DataFrame(\n",
    "        clusters_agg.labels_,\n",
    "        columns=['cluster']\n",
    "    ).astype('category')\n",
    "], axis=1)\n",
    "\n",
    "# create a color map based on unique values in 'cluster' column\n",
    "color_map = plt.cm.get_cmap('Set1')\n",
    "# convert 'cluster' column to a sequence of numbers\n",
    "colors = data_agg['cluster'].astype('category').cat.codes\n",
    "\n",
    "# plot scatter plot with colors based on 'cluster' column\n",
    "data_agg.plot.scatter(x='0', y='1', c=colors, cmap=color_map, marker='o',s=10, linewidths=0.5)"
   ]
  },
  {
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   "metadata": {},
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     "metadata": {},
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   ],
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    "eps_values = np.arange(0.1, 5.1, 0.1)\n",
    "\n",
    "scores = []\n",
    "\n",
    "for eps in eps_values:\n",
    "    dbscan = DBSCAN(eps=eps,min_samples=2)\n",
    "    labels = dbscan.fit_predict(a4_data_df)\n",
    "    has_negative = False\n",
    "    for x in labels:\n",
    "        if x < 0:\n",
    "            has_negative = True\n",
    "            break\n",
    "    if has_negative == False:\n",
    "        score = silhouette_score(a4_data_df, labels)\n",
    "        scores.append([eps,score])\n",
    "\n",
    "fig = px.line(x=scores[0], y=scores[1], labels={'x': 'eps', 'y': 'silhouette score'})\n",
    "fig.show()\n",
    "# plt.plot(scores[0], scores[1])\n",
    "# plt.xlabel('eps')\n",
    "# plt.ylabel('silhouette score')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0', ylabel='1'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "clusters = DBSCAN(\n",
    "# eps= 0.5, # Max distance between two points to assign to same cluster \n",
    "eps= 2.5, # Max distance between two points to assign to same cluster \n",
    "# eps= 2, # Max distance between two points to assign to same cluster \n",
    ").fit(\n",
    "    a4_data_df\n",
    ")\n",
    "\n",
    "data_dbscan = pd.concat([\n",
    "    a4_data_df,\n",
    "    pd.DataFrame(\n",
    "        clusters.labels_,\n",
    "        columns=['cluster']\n",
    "    ).astype('category')\n",
    "], axis=1)\n",
    "\n",
    "# create a color map based on unique values in 'cluster' column\n",
    "color_map = plt.cm.get_cmap('Set1')\n",
    "# convert 'cluster' column to a sequence of numbers\n",
    "colors = data_dbscan['cluster'].astype('category').cat.codes\n",
    "\n",
    "# plot scatter plot with colors based on 'cluster' column\n",
    "data_dbscan.plot.scatter(x='0', y='1', c=colors, cmap=color_map,marker='o',s=10, linewidths=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
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   "execution_count": 71,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sun/opt/anaconda3/envs/dataenv/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:996: FutureWarning:\n",
      "\n",
      "The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "\n"
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    "import plotly.express as px\n",
    "\n",
    "# Perform t-SNE on the data\n",
    "tsne = TSNE(n_components=2, perplexity=100, learning_rate=\"auto\", n_iter=500, init='pca')\n",
    "tsne_data = tsne.fit_transform(a4_data_df)\n",
    "\n",
    "# Compute the silhouette score for different numbers of clusters\n",
    "silhouette_scores = []\n",
    "for n_clusters in range(2, 10):\n",
    "    kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n",
    "    cluster_labels = kmeans.fit_predict(tsne_data)\n",
    "    silhouette_avg = silhouette_score(tsne_data, cluster_labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "\n",
    "# Create a line plot of the silhouette scores using Plotly\n",
    "fig = px.line(x=range(2, 10), y=silhouette_scores, labels={'x': 'Number of clusters', 'y': 'Silhouette score'})\n",
    "fig.show()\n"
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   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "/Users/sun/opt/anaconda3/envs/dataenv/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:996: FutureWarning:\n",
      "\n",
      "The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "\n"
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   "source": [
    "ts = TSNE(\n",
    "    perplexity=100, # Roughly the \"size\" of the clusters to look for (original paper\n",
    "                   # recommends in the 5-50 range, but in general should be less than\n",
    "                   # then number of points in your dataset\n",
    "    learning_rate=\"auto\",\n",
    "    n_iter=500,\n",
    "    init='pca',\n",
    ").fit_transform(a4_data_df)\n",
    "\n",
    "# Perform clustering on the t-SNE results\n",
    "kmeans = KMeans(n_clusters=4, random_state=0)\n",
    "labels = kmeans.fit_predict(ts)\n",
    "\n",
    "fig = px.scatter(x=ts[:, 0], y=ts[:, 1], color=labels)\n",
    "fig.show()"
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