PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import data and packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#PCA\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueID</th>\n",
       "      <th>FrequencyofVisits</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Payer</th>\n",
       "      <th>Facility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57c316839e1dbcd90f5d1bd6eb42d12f</td>\n",
       "      <td>3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>FUTURE HEALTHCARE</td>\n",
       "      <td>HCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4d29a19dfe6806f18bce01bb0fe85675</td>\n",
       "      <td>13</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>FUTURE HEALTHCARE</td>\n",
       "      <td>ICDT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a26297e9956e6e46e1b5e4df332f1cce</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>ADVANCECARE</td>\n",
       "      <td>CCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a3ddef9f9f7736591f42cdf9039e2288</td>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>IASFA</td>\n",
       "      <td>CCMF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b500cfc9376a616d4e89ea600ebd1871</td>\n",
       "      <td>13</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>ADSE</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           UniqueID  FrequencyofVisits   Age       Sex  \\\n",
       "0  57c316839e1dbcd90f5d1bd6eb42d12f                  3  37.0  Feminino   \n",
       "1  4d29a19dfe6806f18bce01bb0fe85675                 13  37.0  Feminino   \n",
       "2  a26297e9956e6e46e1b5e4df332f1cce                  6  37.0  Feminino   \n",
       "3  a3ddef9f9f7736591f42cdf9039e2288                  6  37.0  Feminino   \n",
       "4  b500cfc9376a616d4e89ea600ebd1871                 13  37.0  Feminino   \n",
       "\n",
       "               Payer Facility  \n",
       "0  FUTURE HEALTHCARE      HCS  \n",
       "1  FUTURE HEALTHCARE     ICDT  \n",
       "2        ADVANCECARE      CCB  \n",
       "3              IASFA     CCMF  \n",
       "4               ADSE      CCC  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Data/customer_data.csv')\n",
    "del data[\"Unnamed: 0\"]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(810341, 6)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = data[0:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save patient names\n",
    "patients = data.UniqueID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FrequencyofVisits</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Payer</th>\n",
       "      <th>Facility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>FUTURE HEALTHCARE</td>\n",
       "      <td>HCS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>FUTURE HEALTHCARE</td>\n",
       "      <td>ICDT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>ADVANCECARE</td>\n",
       "      <td>CCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>IASFA</td>\n",
       "      <td>CCMF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>ADSE</td>\n",
       "      <td>CCC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   FrequencyofVisits   Age       Sex              Payer Facility\n",
       "0                  3  37.0  Feminino  FUTURE HEALTHCARE      HCS\n",
       "1                 13  37.0  Feminino  FUTURE HEALTHCARE     ICDT\n",
       "2                  6  37.0  Feminino        ADVANCECARE      CCB\n",
       "3                  6  37.0  Feminino              IASFA     CCMF\n",
       "4                 13  37.0  Feminino               ADSE      CCC"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(columns=\"UniqueID\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_data = data.select_dtypes(np.number)\n",
    "categorical_data = data.select_dtypes([object, \"category\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/csimmons1/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:334: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "numerical_data_normalized = MinMaxScaler().fit_transform(numerical_data)\n",
    "numerical_data_normalized = pd.DataFrame(\n",
    "                    numerical_data_normalized,\n",
    "                    columns=numerical_data.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_data_codified = pd.get_dummies(\n",
    "                                    categorical_data, \n",
    "                                    drop_first=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FrequencyofVisits</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex_Masculino</th>\n",
       "      <th>Sex_Missing</th>\n",
       "      <th>Payer_ADMG</th>\n",
       "      <th>Payer_ADSE</th>\n",
       "      <th>Payer_ADVANCECARE</th>\n",
       "      <th>Payer_ALLIANZ</th>\n",
       "      <th>Payer_ARS</th>\n",
       "      <th>Payer_ASSISTÊNCIA INTERNACIONAL</th>\n",
       "      <th>...</th>\n",
       "      <th>Facility_['HCIS' 'HCS']</th>\n",
       "      <th>Facility_['HCIS' 'HCV']</th>\n",
       "      <th>Facility_['HCIS' 'ICDT']</th>\n",
       "      <th>Facility_['HCP' 'HCS']</th>\n",
       "      <th>Facility_['HCP' 'HCV' 'ICDT']</th>\n",
       "      <th>Facility_['HCP' 'HCV']</th>\n",
       "      <th>Facility_['HCP' 'ICDT']</th>\n",
       "      <th>Facility_['HCS' 'HCV']</th>\n",
       "      <th>Facility_['HCS' 'ICDT']</th>\n",
       "      <th>Facility_['HCV' 'ICDT']</th>\n",
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       "      <td>0.349057</td>\n",
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       "    <tr>\n",
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       "<p>5 rows × 589 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FrequencyofVisits       Age  Sex_Masculino  Sex_Missing  Payer_ADMG  \\\n",
       "0           0.003130  0.349057              0            0           0   \n",
       "1           0.018779  0.349057              0            0           0   \n",
       "2           0.007825  0.349057              0            0           0   \n",
       "3           0.007825  0.349057              0            0           0   \n",
       "4           0.018779  0.349057              0            0           0   \n",
       "\n",
       "   Payer_ADSE  Payer_ADVANCECARE  Payer_ALLIANZ  Payer_ARS  \\\n",
       "0           0                  0              0          0   \n",
       "1           0                  0              0          0   \n",
       "2           0                  1              0          0   \n",
       "3           0                  0              0          0   \n",
       "4           1                  0              0          0   \n",
       "\n",
       "   Payer_ASSISTÊNCIA INTERNACIONAL  ...  Facility_['HCIS' 'HCS']  \\\n",
       "0                                0  ...                        0   \n",
       "1                                0  ...                        0   \n",
       "2                                0  ...                        0   \n",
       "3                                0  ...                        0   \n",
       "4                                0  ...                        0   \n",
       "\n",
       "   Facility_['HCIS' 'HCV']  Facility_['HCIS' 'ICDT']  Facility_['HCP' 'HCS']  \\\n",
       "0                        0                         0                       0   \n",
       "1                        0                         0                       0   \n",
       "2                        0                         0                       0   \n",
       "3                        0                         0                       0   \n",
       "4                        0                         0                       0   \n",
       "\n",
       "   Facility_['HCP' 'HCV' 'ICDT']  Facility_['HCP' 'HCV']  \\\n",
       "0                              0                       0   \n",
       "1                              0                       0   \n",
       "2                              0                       0   \n",
       "3                              0                       0   \n",
       "4                              0                       0   \n",
       "\n",
       "   Facility_['HCP' 'ICDT']  Facility_['HCS' 'HCV']  Facility_['HCS' 'ICDT']  \\\n",
       "0                        0                       0                        0   \n",
       "1                        0                       0                        0   \n",
       "2                        0                       0                        0   \n",
       "3                        0                       0                        0   \n",
       "4                        0                       0                        0   \n",
       "\n",
       "   Facility_['HCV' 'ICDT']  \n",
       "0                        0  \n",
       "1                        0  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        0  \n",
       "\n",
       "[5 rows x 589 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed = pd.concat([\n",
    "                            numerical_data_normalized,\n",
    "                            categorical_data_codified\n",
    "                        ], axis=1\n",
    ")\n",
    "data_processed.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/csimmons1/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/csimmons1/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "data2 = StandardScaler(with_std=False).fit_transform(data_processed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator_pca = PCA(n_components=14)\n",
    "data_pca = estimator_pca.fit_transform(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8032844731752403"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator_pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(810341, 14)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "      <th>PC12</th>\n",
       "      <th>PC13</th>\n",
       "      <th>PC14</th>\n",
       "      <th>UniqueID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.364037</td>\n",
       "      <td>-0.107169</td>\n",
       "      <td>-0.007892</td>\n",
       "      <td>-0.105537</td>\n",
       "      <td>0.09196</td>\n",
       "      <td>-0.068248</td>\n",
       "      <td>0.039098</td>\n",
       "      <td>0.171010</td>\n",
       "      <td>-0.179154</td>\n",
       "      <td>0.146908</td>\n",
       "      <td>0.804683</td>\n",
       "      <td>0.110341</td>\n",
       "      <td>0.694594</td>\n",
       "      <td>-0.162076</td>\n",
       "      <td>57c316839e1dbcd90f5d1bd6eb42d12f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.365604</td>\n",
       "      <td>-0.133887</td>\n",
       "      <td>0.015146</td>\n",
       "      <td>-0.162602</td>\n",
       "      <td>0.24113</td>\n",
       "      <td>-0.055893</td>\n",
       "      <td>-0.131696</td>\n",
       "      <td>0.186359</td>\n",
       "      <td>-0.586681</td>\n",
       "      <td>-0.594955</td>\n",
       "      <td>0.075158</td>\n",
       "      <td>0.546117</td>\n",
       "      <td>0.046840</td>\n",
       "      <td>-0.012824</td>\n",
       "      <td>4d29a19dfe6806f18bce01bb0fe85675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3       PC4      PC5       PC6       PC7  \\\n",
       "0 -0.364037 -0.107169 -0.007892 -0.105537  0.09196 -0.068248  0.039098   \n",
       "1 -0.365604 -0.133887  0.015146 -0.162602  0.24113 -0.055893 -0.131696   \n",
       "\n",
       "        PC8       PC9      PC10      PC11      PC12      PC13      PC14  \\\n",
       "0  0.171010 -0.179154  0.146908  0.804683  0.110341  0.694594 -0.162076   \n",
       "1  0.186359 -0.586681 -0.594955  0.075158  0.546117  0.046840 -0.012824   \n",
       "\n",
       "                           UniqueID  \n",
       "0  57c316839e1dbcd90f5d1bd6eb42d12f  \n",
       "1  4d29a19dfe6806f18bce01bb0fe85675  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = pd.DataFrame(data_pca, columns=[\"PC1\", \"PC2\", \"PC3\", \"PC4\", \"PC5\", \"PC6\", \"PC7\", \"PC8\", \"PC9\", \"PC10\", \"PC11\", \"PC12\", \"PC13\", \"PC14\"])\n",
    "data_df[\"UniqueID\"] = patients\n",
    "data_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(810341, 15)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the wide format, the customer table \n",
    "data_df.to_csv(\"Data/pca_customer_data.csv\")"
   ]
  }
 ],
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