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    "## Clustering\n",
    "\n",
    "We use a simple k-means algorithm to demonstrate how clustering can be done. Clustering can help discover valuable, hidden groupings within the data. The dataset is created in the [Obtain_dataset Notebook](Obtain_dataset.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from ast import literal_eval\n",
    "# load data\n",
    "datafile_path = \"../2-Data/dialogues_embededd.pkl\"\n",
    "df = pd.read_pickle(datafile_path)"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>Hi Doctor, I have been having severe hair fall...</td>\n",
       "      <td>hello,        hair4u is combination of minoxid...</td>\n",
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       "                                              Description  \\\n",
       "255916  What medicine is best for taking out pores and...   \n",
       "255917  What causes non conception despite unprotected...   \n",
       "255918  How long after getting chicken pox is it safe ...   \n",
       "255919  How to remove unwanted hair without any side e...   \n",
       "255920                I am 15. Can i shave my pubic hair?   \n",
       "...                                                   ...   \n",
       "256911  Why is hair fall increasing while using Bontre...   \n",
       "256912  Why was I asked to discontinue Androanagen whi...   \n",
       "256913  Can Mintop 5% Lotion be used by women for seve...   \n",
       "256914  Is Minoxin 5% lotion advisable instead of Foli...   \n",
       "256915  Are Biotin supplements need to reduce severe h...   \n",
       "\n",
       "                                                  Patient  \\\n",
       "255916  Hi, I am (Simran) 28 years (Female) hight 5/3 ...   \n",
       "255917  Hi, i am living with my partner from last six ...   \n",
       "255918  i am 25 yrs old. last year in June i had an IU...   \n",
       "255919  hi doctor! i m 22 years old and i want to remo...   \n",
       "255920  sorry,im just curious because im 15 and when i...   \n",
       "...                                                   ...   \n",
       "256911  I am suffering from excessive hairfall. My doc...   \n",
       "256912  Hi Doctor, I have been having severe hair fall...   \n",
       "256913  Hi..i hav sever hair loss problem so consulted...   \n",
       "256914  Hi, i am 25 year old girl, i am having massive...   \n",
       "256915  iam having hairfall for a decade.. but fews we...   \n",
       "\n",
       "                                                   Doctor  \\\n",
       "255916  Hi simran, your problem can be best sort out u...   \n",
       "255917  do serum tsh and serum prolactin den do hsg on...   \n",
       "255918  Hi, I think you should keep a gap of 3 months ...   \n",
       "255919  Hello, which part of the body do you have thes...   \n",
       "255920  haha aw. Yes your supposed to shave, trim it f...   \n",
       "...                                                   ...   \n",
       "256911  Hello Dear Thanks for writing to us, we are he...   \n",
       "256912  hello,        hair4u is combination of minoxid...   \n",
       "256913  HI  I have evaluated your query thoroughly you...   \n",
       "256914  Hello and Welcome to ‘Ask A Doctor’ service.I ...   \n",
       "256915  you did'nt mention about thyroid problem ...us...   \n",
       "\n",
       "                                                 combined  n_tokens  \\\n",
       "255916  Description: What medicine is best for taking ...       282   \n",
       "255917  Description: What causes non conception despit...       195   \n",
       "255918  Description: How long after getting chicken po...       254   \n",
       "255919  Description: How to remove unwanted hair witho...       110   \n",
       "255920  Description: I am 15. Can i shave my pubic hai...       223   \n",
       "...                                                   ...       ...   \n",
       "256911  Description: Why is hair fall increasing while...       211   \n",
       "256912  Description: Why was I asked to discontinue An...       154   \n",
       "256913  Description: Can Mintop 5% Lotion be used by w...       191   \n",
       "256914  Description: Is Minoxin 5% lotion advisable in...       232   \n",
       "256915  Description: Are Biotin supplements need to re...       213   \n",
       "\n",
       "                                                embedding  \n",
       "255916  [-0.2568769, 0.14872037, 0.17243277, 0.2426784...  \n",
       "255917  [-0.058888793, 0.084498726, -0.05540138, 0.021...  \n",
       "255918  [-0.12680525, -0.01062898, -0.25460148, 0.0511...  \n",
       "255919  [0.12641467, -0.1543769, 0.42004266, 0.1722529...  \n",
       "255920  [-0.06219541, 0.020706447, 0.38799724, -0.0219...  \n",
       "...                                                   ...  \n",
       "256911  [-0.17113408, 0.10835318, 0.33148944, -0.06146...  \n",
       "256912  [-0.24637492, 0.031407423, -0.05137701, -0.301...  \n",
       "256913  [-0.32340947, 0.3667281, 0.3651925, -0.0989788...  \n",
       "256914  [-0.18737659, 0.12219846, 0.2365137, 0.1126744...  \n",
       "256915  [-0.032349, -0.050617322, 0.30625877, 0.201994...  \n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
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       "(1000, 384)"
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   ],
   "source": [
    "df[\"embedding\"] = df.embedding.apply(np.array)  # convert string to numpy array\n",
    "matrix = np.vstack(df.embedding.values)\n",
    "matrix.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#df[\"embedding_doctor\"] = df.embedding_doctor.apply(np.array)  # convert string to numpy array\n",
    "#matrix = np.vstack(df.embedding_doctor.values)\n",
    "#matrix.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
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       "                                              Description  \\\n",
       "255916  What medicine is best for taking out pores and...   \n",
       "255917  What causes non conception despite unprotected...   \n",
       "255918  How long after getting chicken pox is it safe ...   \n",
       "255919  How to remove unwanted hair without any side e...   \n",
       "255920                I am 15. Can i shave my pubic hair?   \n",
       "...                                                   ...   \n",
       "256911  Why is hair fall increasing while using Bontre...   \n",
       "256912  Why was I asked to discontinue Androanagen whi...   \n",
       "256913  Can Mintop 5% Lotion be used by women for seve...   \n",
       "256914  Is Minoxin 5% lotion advisable instead of Foli...   \n",
       "256915  Are Biotin supplements need to reduce severe h...   \n",
       "\n",
       "                                                  Patient  \\\n",
       "255916  Hi, I am (Simran) 28 years (Female) hight 5/3 ...   \n",
       "255917  Hi, i am living with my partner from last six ...   \n",
       "255918  i am 25 yrs old. last year in June i had an IU...   \n",
       "255919  hi doctor! i m 22 years old and i want to remo...   \n",
       "255920  sorry,im just curious because im 15 and when i...   \n",
       "...                                                   ...   \n",
       "256911  I am suffering from excessive hairfall. My doc...   \n",
       "256912  Hi Doctor, I have been having severe hair fall...   \n",
       "256913  Hi..i hav sever hair loss problem so consulted...   \n",
       "256914  Hi, i am 25 year old girl, i am having massive...   \n",
       "256915  iam having hairfall for a decade.. but fews we...   \n",
       "\n",
       "                                                   Doctor  \\\n",
       "255916  Hi simran, your problem can be best sort out u...   \n",
       "255917  do serum tsh and serum prolactin den do hsg on...   \n",
       "255918  Hi, I think you should keep a gap of 3 months ...   \n",
       "255919  Hello, which part of the body do you have thes...   \n",
       "255920  haha aw. Yes your supposed to shave, trim it f...   \n",
       "...                                                   ...   \n",
       "256911  Hello Dear Thanks for writing to us, we are he...   \n",
       "256912  hello,        hair4u is combination of minoxid...   \n",
       "256913  HI  I have evaluated your query thoroughly you...   \n",
       "256914  Hello and Welcome to ‘Ask A Doctor’ service.I ...   \n",
       "256915  you did'nt mention about thyroid problem ...us...   \n",
       "\n",
       "                                                 combined  n_tokens  \\\n",
       "255916  Description: What medicine is best for taking ...       282   \n",
       "255917  Description: What causes non conception despit...       195   \n",
       "255918  Description: How long after getting chicken po...       254   \n",
       "255919  Description: How to remove unwanted hair witho...       110   \n",
       "255920  Description: I am 15. Can i shave my pubic hai...       223   \n",
       "...                                                   ...       ...   \n",
       "256911  Description: Why is hair fall increasing while...       211   \n",
       "256912  Description: Why was I asked to discontinue An...       154   \n",
       "256913  Description: Can Mintop 5% Lotion be used by w...       191   \n",
       "256914  Description: Is Minoxin 5% lotion advisable in...       232   \n",
       "256915  Description: Are Biotin supplements need to re...       213   \n",
       "\n",
       "                                                embedding  \n",
       "255916  [-0.2568769, 0.14872037, 0.17243277, 0.2426784...  \n",
       "255917  [-0.058888793, 0.084498726, -0.05540138, 0.021...  \n",
       "255918  [-0.12680525, -0.01062898, -0.25460148, 0.0511...  \n",
       "255919  [0.12641467, -0.1543769, 0.42004266, 0.1722529...  \n",
       "255920  [-0.06219541, 0.020706447, 0.38799724, -0.0219...  \n",
       "...                                                   ...  \n",
       "256911  [-0.17113408, 0.10835318, 0.33148944, -0.06146...  \n",
       "256912  [-0.24637492, 0.031407423, -0.05137701, -0.301...  \n",
       "256913  [-0.32340947, 0.3667281, 0.3651925, -0.0989788...  \n",
       "256914  [-0.18737659, 0.12219846, 0.2365137, 0.1126744...  \n",
       "256915  [-0.032349, -0.050617322, 0.30625877, 0.201994...  \n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 1. Find the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd \n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"embedding\"] = df.embedding.apply(np.array)  # convert string to numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:, 5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.iloc[:, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_input=[embed.tolist() for embed in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is your array with the values\n",
    "X = np.array(X_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_input[:2][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss = [] \n",
    "max=20\n",
    "for i in range(1, max): \n",
    "    kmeans = KMeans(n_init = i, init = 'k-means++', random_state = 42)\n",
    "    kmeans.fit(X) \n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, max), wcss)\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Find the clusters using K-means"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We show the simplest use of K-means. You can pick the number of clusters that fits your use case best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "n_clusters = 7\n",
    "kmeans = KMeans(n_init=n_clusters, init=\"k-means++\", random_state=42)\n",
    "kmeans.fit(matrix)\n",
    "labels = kmeans.labels_\n",
    "df[\"Cluster\"] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Centroid coordinates\n",
    "centroids = kmeans.cluster_centers_\n",
    "# get centroids\n",
    "#centroids = model.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 384)"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.spatial.distance as sdist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 384)"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 384)"
      ]
     },
     "execution_count": 463,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids[df['Cluster']].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(index):\n",
    "    dist=sdist.norm(matrix[index] - centroids[df['Cluster']][index])\n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist=[distance(index) for index in range(len(df['Cluster']))]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['distance'] = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Doctor</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
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       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255916</th>\n",
       "      <td>What medicine is best for taking out pores and...</td>\n",
       "      <td>Hi, I am (Simran) 28 years (Female) hight 5/3 ...</td>\n",
       "      <td>Hi simran, your problem can be best sort out u...</td>\n",
       "      <td>Description: What medicine is best for taking ...</td>\n",
       "      <td>282</td>\n",
       "      <td>[-0.2568769, 0.14872037, 0.17243277, 0.2426784...</td>\n",
       "      <td>7</td>\n",
       "      <td>3.389531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255917</th>\n",
       "      <td>What causes non conception despite unprotected...</td>\n",
       "      <td>Hi, i am living with my partner from last six ...</td>\n",
       "      <td>do serum tsh and serum prolactin den do hsg on...</td>\n",
       "      <td>Description: What causes non conception despit...</td>\n",
       "      <td>195</td>\n",
       "      <td>[-0.058888793, 0.084498726, -0.05540138, 0.021...</td>\n",
       "      <td>5</td>\n",
       "      <td>3.848544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255918</th>\n",
       "      <td>How long after getting chicken pox is it safe ...</td>\n",
       "      <td>i am 25 yrs old. last year in June i had an IU...</td>\n",
       "      <td>Hi, I think you should keep a gap of 3 months ...</td>\n",
       "      <td>Description: How long after getting chicken po...</td>\n",
       "      <td>254</td>\n",
       "      <td>[-0.12680525, -0.01062898, -0.25460148, 0.0511...</td>\n",
       "      <td>5</td>\n",
       "      <td>3.287058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255919</th>\n",
       "      <td>How to remove unwanted hair without any side e...</td>\n",
       "      <td>hi doctor! i m 22 years old and i want to remo...</td>\n",
       "      <td>Hello, which part of the body do you have thes...</td>\n",
       "      <td>Description: How to remove unwanted hair witho...</td>\n",
       "      <td>110</td>\n",
       "      <td>[0.12641467, -0.1543769, 0.42004266, 0.1722529...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.875464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255920</th>\n",
       "      <td>I am 15. Can i shave my pubic hair?</td>\n",
       "      <td>sorry,im just curious because im 15 and when i...</td>\n",
       "      <td>haha aw. Yes your supposed to shave, trim it f...</td>\n",
       "      <td>Description: I am 15. Can i shave my pubic hai...</td>\n",
       "      <td>223</td>\n",
       "      <td>[-0.06219541, 0.020706447, 0.38799724, -0.0219...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.841674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256911</th>\n",
       "      <td>Why is hair fall increasing while using Bontre...</td>\n",
       "      <td>I am suffering from excessive hairfall. My doc...</td>\n",
       "      <td>Hello Dear Thanks for writing to us, we are he...</td>\n",
       "      <td>Description: Why is hair fall increasing while...</td>\n",
       "      <td>211</td>\n",
       "      <td>[-0.17113408, 0.10835318, 0.33148944, -0.06146...</td>\n",
       "      <td>6</td>\n",
       "      <td>2.765943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256912</th>\n",
       "      <td>Why was I asked to discontinue Androanagen whi...</td>\n",
       "      <td>Hi Doctor, I have been having severe hair fall...</td>\n",
       "      <td>hello,        hair4u is combination of minoxid...</td>\n",
       "      <td>Description: Why was I asked to discontinue An...</td>\n",
       "      <td>154</td>\n",
       "      <td>[-0.24637492, 0.031407423, -0.05137701, -0.301...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.314942</td>\n",
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       "    <tr>\n",
       "      <th>256913</th>\n",
       "      <td>Can Mintop 5% Lotion be used by women for seve...</td>\n",
       "      <td>Hi..i hav sever hair loss problem so consulted...</td>\n",
       "      <td>HI  I have evaluated your query thoroughly you...</td>\n",
       "      <td>Description: Can Mintop 5% Lotion be used by w...</td>\n",
       "      <td>191</td>\n",
       "      <td>[-0.32340947, 0.3667281, 0.3651925, -0.0989788...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.229499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256914</th>\n",
       "      <td>Is Minoxin 5% lotion advisable instead of Foli...</td>\n",
       "      <td>Hi, i am 25 year old girl, i am having massive...</td>\n",
       "      <td>Hello and Welcome to ‘Ask A Doctor’ service.I ...</td>\n",
       "      <td>Description: Is Minoxin 5% lotion advisable in...</td>\n",
       "      <td>232</td>\n",
       "      <td>[-0.18737659, 0.12219846, 0.2365137, 0.1126744...</td>\n",
       "      <td>6</td>\n",
       "      <td>2.536337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256915</th>\n",
       "      <td>Are Biotin supplements need to reduce severe h...</td>\n",
       "      <td>iam having hairfall for a decade.. but fews we...</td>\n",
       "      <td>you did'nt mention about thyroid problem ...us...</td>\n",
       "      <td>Description: Are Biotin supplements need to re...</td>\n",
       "      <td>213</td>\n",
       "      <td>[-0.032349, -0.050617322, 0.30625877, 0.201994...</td>\n",
       "      <td>6</td>\n",
       "      <td>3.123004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Description  \\\n",
       "255916  What medicine is best for taking out pores and...   \n",
       "255917  What causes non conception despite unprotected...   \n",
       "255918  How long after getting chicken pox is it safe ...   \n",
       "255919  How to remove unwanted hair without any side e...   \n",
       "255920                I am 15. Can i shave my pubic hair?   \n",
       "...                                                   ...   \n",
       "256911  Why is hair fall increasing while using Bontre...   \n",
       "256912  Why was I asked to discontinue Androanagen whi...   \n",
       "256913  Can Mintop 5% Lotion be used by women for seve...   \n",
       "256914  Is Minoxin 5% lotion advisable instead of Foli...   \n",
       "256915  Are Biotin supplements need to reduce severe h...   \n",
       "\n",
       "                                                  Patient  \\\n",
       "255916  Hi, I am (Simran) 28 years (Female) hight 5/3 ...   \n",
       "255917  Hi, i am living with my partner from last six ...   \n",
       "255918  i am 25 yrs old. last year in June i had an IU...   \n",
       "255919  hi doctor! i m 22 years old and i want to remo...   \n",
       "255920  sorry,im just curious because im 15 and when i...   \n",
       "...                                                   ...   \n",
       "256911  I am suffering from excessive hairfall. My doc...   \n",
       "256912  Hi Doctor, I have been having severe hair fall...   \n",
       "256913  Hi..i hav sever hair loss problem so consulted...   \n",
       "256914  Hi, i am 25 year old girl, i am having massive...   \n",
       "256915  iam having hairfall for a decade.. but fews we...   \n",
       "\n",
       "                                                   Doctor  \\\n",
       "255916  Hi simran, your problem can be best sort out u...   \n",
       "255917  do serum tsh and serum prolactin den do hsg on...   \n",
       "255918  Hi, I think you should keep a gap of 3 months ...   \n",
       "255919  Hello, which part of the body do you have thes...   \n",
       "255920  haha aw. Yes your supposed to shave, trim it f...   \n",
       "...                                                   ...   \n",
       "256911  Hello Dear Thanks for writing to us, we are he...   \n",
       "256912  hello,        hair4u is combination of minoxid...   \n",
       "256913  HI  I have evaluated your query thoroughly you...   \n",
       "256914  Hello and Welcome to ‘Ask A Doctor’ service.I ...   \n",
       "256915  you did'nt mention about thyroid problem ...us...   \n",
       "\n",
       "                                                 combined  n_tokens  \\\n",
       "255916  Description: What medicine is best for taking ...       282   \n",
       "255917  Description: What causes non conception despit...       195   \n",
       "255918  Description: How long after getting chicken po...       254   \n",
       "255919  Description: How to remove unwanted hair witho...       110   \n",
       "255920  Description: I am 15. Can i shave my pubic hai...       223   \n",
       "...                                                   ...       ...   \n",
       "256911  Description: Why is hair fall increasing while...       211   \n",
       "256912  Description: Why was I asked to discontinue An...       154   \n",
       "256913  Description: Can Mintop 5% Lotion be used by w...       191   \n",
       "256914  Description: Is Minoxin 5% lotion advisable in...       232   \n",
       "256915  Description: Are Biotin supplements need to re...       213   \n",
       "\n",
       "                                                embedding  Cluster  distance  \n",
       "255916  [-0.2568769, 0.14872037, 0.17243277, 0.2426784...        7  3.389531  \n",
       "255917  [-0.058888793, 0.084498726, -0.05540138, 0.021...        5  3.848544  \n",
       "255918  [-0.12680525, -0.01062898, -0.25460148, 0.0511...        5  3.287058  \n",
       "255919  [0.12641467, -0.1543769, 0.42004266, 0.1722529...        6  3.875464  \n",
       "255920  [-0.06219541, 0.020706447, 0.38799724, -0.0219...        6  3.841674  \n",
       "...                                                   ...      ...       ...  \n",
       "256911  [-0.17113408, 0.10835318, 0.33148944, -0.06146...        6  2.765943  \n",
       "256912  [-0.24637492, 0.031407423, -0.05137701, -0.301...        6  3.314942  \n",
       "256913  [-0.32340947, 0.3667281, 0.3651925, -0.0989788...        6  3.229499  \n",
       "256914  [-0.18737659, 0.12219846, 0.2365137, 0.1126744...        6  2.536337  \n",
       "256915  [-0.032349, -0.050617322, 0.30625877, 0.201994...        6  3.123004  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.groupby(['Cluster'])['Score'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster\n",
       "0    2.015194\n",
       "1    2.055015\n",
       "4    2.461319\n",
       "6    2.740797\n",
       "7    3.067031\n",
       "5    3.095309\n",
       "3    3.164274\n",
       "2    3.284517\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Cluster\").distance.mean().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "nticks = n_clusters\n",
    "colormap = plt.get_cmap('turbo', nticks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.colors.ListedColormap"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(colormap.colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.colors.ListedColormap"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(colormap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rusla\\AppData\\Local\\Temp\\ipykernel_26408\\3162068520.py:16: RuntimeWarning: Mean of empty slice.\n",
      "  avg_x = xs.mean()\n",
      "C:\\gpt\\my_venv\\lib\\site-packages\\numpy\\core\\_methods.py:190: RuntimeWarning: invalid value encountered in divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "C:\\Users\\rusla\\AppData\\Local\\Temp\\ipykernel_26408\\3162068520.py:17: RuntimeWarning: Mean of empty slice.\n",
      "  avg_y = ys.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Clusters identified visualized in language 2d using t-SNE')"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=15, random_state=42, init=\"random\", learning_rate=200)\n",
    "vis_dims2 = tsne.fit_transform(matrix)\n",
    "\n",
    "x = [x for x, y in vis_dims2]\n",
    "y = [y for x, y in vis_dims2]\n",
    "\n",
    "for category, color in enumerate([\"purple\", \"green\", \"red\", \"blue\",\"yellow\",\"orange\",\"violet\",\"cyan\",\"magenta\"]):\n",
    "    xs = np.array(x)[df.Cluster == category]\n",
    "    ys = np.array(y)[df.Cluster == category]\n",
    "    plt.scatter(xs, ys, color=color, alpha=0.3)\n",
    "\n",
    "    avg_x = xs.mean()\n",
    "    avg_y = ys.mean()\n",
    "\n",
    "    plt.scatter(avg_x, avg_y, marker=\"x\", color=color, s=100)\n",
    "plt.title(\"Clusters identified visualized in language 2d using t-SNE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization of clusters in a 2d projection. In this run, the green cluster (#1) seems quite different from the others. Let's see a few samples from each cluster."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3 (GPT)",
   "language": "python",
   "name": "gpt"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "vscode": {
   "interpreter": {
    "hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}