Body Language Decoder Tutorial.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Install and Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyforest in c:\\users\\skarasala\\anaconda3\\lib\\site-packages (1.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install mediapipe opencv-python pandas scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mediapipe as mp # Import mediapipe\n",
    "import cv2 # Import opencv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "mp_drawing = mp.solutions.drawing_utils # Drawing helpers\n",
    "mp_holistic = mp.solutions.holistic # Mediapipe Solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Make Some Detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.process(image)\n",
    "        # print(results.face_landmarks)\n",
    "        \n",
    "        # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n",
    "        \n",
    "        # Recolor image back to BGR for rendering\n",
    "        image.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "                        \n",
    "        cv2.imshow('Raw Webcam Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.face_landmarks.landmark[0].visibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Capture Landmarks & Export to CSV\n",
    "<!--<img src=\"https://i.imgur.com/8bForKY.png\">-->\n",
    "<!--<img src=\"https://i.imgur.com/AzKNp7A.png\">-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_coords = len(results.pose_landmarks.landmark)+len(results.face_landmarks.landmark)\n",
    "num_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "landmarks = ['class']\n",
    "for val in range(1, num_coords+1):\n",
    "    landmarks += ['x{}'.format(val), 'y{}'.format(val), 'z{}'.format(val), 'v{}'.format(val)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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   "outputs": [
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landmarks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('coords.csv', mode='w', newline='') as f:\n",
    "    csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "    csv_writer.writerow(landmarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = \"Smile\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.process(image)\n",
    "        # print(results.face_landmarks)\n",
    "        \n",
    "        # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n",
    "        \n",
    "        # Recolor image back to BGR for rendering\n",
    "        image.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "        # Export coordinates\n",
    "        try:\n",
    "            # Extract Pose landmarks\n",
    "            pose = results.pose_landmarks.landmark\n",
    "            pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n",
    "            \n",
    "            # Extract Face landmarks\n",
    "            face = results.face_landmarks.landmark\n",
    "            face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n",
    "            \n",
    "            # Concate rows\n",
    "            row = pose_row+face_row\n",
    "            \n",
    "            # Append class name \n",
    "            row.insert(0, class_name)\n",
    "            \n",
    "            # Export to CSV\n",
    "            with open('coords.csv', mode='a', newline='') as f:\n",
    "                csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "                csv_writer.writerow(row) \n",
    "            \n",
    "        except:\n",
    "            pass\n",
    "                        \n",
    "        cv2.imshow('Raw Webcam Feed', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Train Custom Model Using Scikit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Read in Collected Data and Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('coords.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
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       "      <td>Angry</td>\n",
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       "   class        x1        y1        z1        v1        x2        y2  \\\n",
       "0  Angry  0.591761  0.604082 -1.109361  0.999336  0.620489  0.552917   \n",
       "1  Angry  0.591646  0.614272 -1.296188  0.998693  0.619998  0.561734   \n",
       "2  Angry  0.591560  0.619552 -1.387648  0.998468  0.619242  0.566419   \n",
       "3  Angry  0.590882  0.621521 -1.395772  0.998297  0.618031  0.568397   \n",
       "4  Angry  0.589783  0.622623 -1.405468  0.998094  0.616704  0.569357   \n",
       "\n",
       "         z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "0 -1.050994  0.998342  0.636986  ... -0.004208   0.0  0.651810  0.552300   \n",
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       "4 -1.342474  0.995929  0.631959  ... -0.005481   0.0  0.650345  0.549765   \n",
       "\n",
       "       z500  v500      x501      y501      z501  v501  \n",
       "0  0.020892   0.0  0.656684  0.547002  0.021837   0.0  \n",
       "1  0.019410   0.0  0.657182  0.546253  0.020338   0.0  \n",
       "2  0.020076   0.0  0.655838  0.545584  0.021008   0.0  \n",
       "3  0.019639   0.0  0.655509  0.544745  0.020526   0.0  \n",
       "4  0.019728   0.0  0.655132  0.544829  0.020624   0.0  \n",
       "\n",
       "[5 rows x 2005 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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       "\n",
       "         z500  v500      x501      y501      z501  v501  \n",
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       "829  0.005661   0.0  0.711192  0.518792  0.006096   0.0  \n",
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       "\n",
       "[5 rows x 2005 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
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       "      <td>0.999809</td>\n",
       "      <td>0.625563</td>\n",
       "      <td>0.686869</td>\n",
       "      <td>-2.170775</td>\n",
       "      <td>0.999809</td>\n",
       "      <td>0.642836</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.657865</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>-0.010720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.662996</td>\n",
       "      <td>0.654452</td>\n",
       "      <td>-0.011027</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.595007</td>\n",
       "      <td>0.748804</td>\n",
       "      <td>-2.180200</td>\n",
       "      <td>0.999816</td>\n",
       "      <td>0.625574</td>\n",
       "      <td>0.685586</td>\n",
       "      <td>-2.144049</td>\n",
       "      <td>0.999819</td>\n",
       "      <td>0.642851</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030808</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.657909</td>\n",
       "      <td>0.659333</td>\n",
       "      <td>-0.010318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663018</td>\n",
       "      <td>0.654053</td>\n",
       "      <td>-0.010635</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.594940</td>\n",
       "      <td>0.747216</td>\n",
       "      <td>-2.209533</td>\n",
       "      <td>0.999822</td>\n",
       "      <td>0.625565</td>\n",
       "      <td>0.684433</td>\n",
       "      <td>-2.170951</td>\n",
       "      <td>0.999827</td>\n",
       "      <td>0.642825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658194</td>\n",
       "      <td>0.659113</td>\n",
       "      <td>-0.010567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663409</td>\n",
       "      <td>0.653702</td>\n",
       "      <td>-0.010885</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>Sad</td>\n",
       "      <td>0.595101</td>\n",
       "      <td>0.745948</td>\n",
       "      <td>-2.237671</td>\n",
       "      <td>0.999828</td>\n",
       "      <td>0.625577</td>\n",
       "      <td>0.683308</td>\n",
       "      <td>-2.197548</td>\n",
       "      <td>0.999836</td>\n",
       "      <td>0.642834</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.030662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.659164</td>\n",
       "      <td>0.654291</td>\n",
       "      <td>-0.010033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.664253</td>\n",
       "      <td>0.649165</td>\n",
       "      <td>-0.010365</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 2005 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    class        x1        y1        z1        v1        x2        y2  \\\n",
       "138   Sad  0.582425  0.610281 -1.112963  0.999783  0.605942  0.551302   \n",
       "139   Sad  0.583670  0.609115 -1.109615  0.999383  0.608023  0.552323   \n",
       "140   Sad  0.584232  0.608269 -1.061073  0.998937  0.609137  0.553156   \n",
       "141   Sad  0.584912  0.607134 -1.055154  0.998670  0.610186  0.553423   \n",
       "142   Sad  0.585081  0.605977 -1.021464  0.998296  0.610458  0.553655   \n",
       "..    ...       ...       ...       ...       ...       ...       ...   \n",
       "324   Sad  0.594990  0.752791 -2.189175  0.999802  0.625511  0.688891   \n",
       "325   Sad  0.594998  0.750483 -2.207575  0.999809  0.625563  0.686869   \n",
       "326   Sad  0.595007  0.748804 -2.180200  0.999816  0.625574  0.685586   \n",
       "327   Sad  0.594940  0.747216 -2.209533  0.999822  0.625565  0.684433   \n",
       "328   Sad  0.595101  0.745948 -2.237671  0.999828  0.625577  0.683308   \n",
       "\n",
       "           z2        v2        x3  ...      z499  v499      x500      y500  \\\n",
       "138 -1.055583  0.999391  0.621226  ... -0.000283   0.0  0.642700  0.543093   \n",
       "139 -1.047511  0.998729  0.622821  ... -0.000761   0.0  0.643295  0.541288   \n",
       "140 -0.997856  0.998021  0.623560  ... -0.001825   0.0  0.643466  0.541122   \n",
       "141 -0.990583  0.997612  0.624386  ... -0.001789   0.0  0.643097  0.541407   \n",
       "142 -0.954134  0.997109  0.624552  ... -0.001598   0.0  0.643538  0.541837   \n",
       "..        ...       ...       ...  ...       ...   ...       ...       ...   \n",
       "324 -2.154902  0.999800  0.642780  ... -0.031334   0.0  0.657580  0.660652   \n",
       "325 -2.170775  0.999809  0.642836  ... -0.031071   0.0  0.657865  0.659817   \n",
       "326 -2.144049  0.999819  0.642851  ... -0.030808   0.0  0.657909  0.659333   \n",
       "327 -2.170951  0.999827  0.642825  ... -0.031006   0.0  0.658194  0.659113   \n",
       "328 -2.197548  0.999836  0.642834  ... -0.030662   0.0  0.659164  0.654291   \n",
       "\n",
       "         z500  v500      x501      y501      z501  v501  \n",
       "138  0.025886   0.0  0.647620  0.537701  0.027098   0.0  \n",
       "139  0.025314   0.0  0.648271  0.536031  0.026488   0.0  \n",
       "140  0.024142   0.0  0.648524  0.535580  0.025288   0.0  \n",
       "141  0.024160   0.0  0.648188  0.535935  0.025316   0.0  \n",
       "142  0.024797   0.0  0.648712  0.536296  0.025956   0.0  \n",
       "..        ...   ...       ...       ...       ...   ...  \n",
       "324 -0.010801   0.0  0.662705  0.655195 -0.011118   0.0  \n",
       "325 -0.010720   0.0  0.662996  0.654452 -0.011027   0.0  \n",
       "326 -0.010318   0.0  0.663018  0.654053 -0.010635   0.0  \n",
       "327 -0.010567   0.0  0.663409  0.653702 -0.010885   0.0  \n",
       "328 -0.010033   0.0  0.664253  0.649165 -0.010365   0.0  \n",
       "\n",
       "[191 rows x 2005 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['class']=='Sad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('class', axis=1) # features\n",
    "y = df['class'] # target value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326           Sad\n",
       "602    Victorious\n",
       "288           Sad\n",
       "230           Sad\n",
       "362         Happy\n",
       "          ...    \n",
       "9           Angry\n",
       "77          Angry\n",
       "475    Victorious\n",
       "523    Victorious\n",
       "589    Victorious\n",
       "Name: class, Length: 250, dtype: object"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Train Machine Learning Classification Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelines = {\n",
    "    #'lr':make_pipeline(StandardScaler(), LogisticRegression()),\n",
    "    'rc':make_pipeline(StandardScaler(), RidgeClassifier()),\n",
    "    'rf':make_pipeline(StandardScaler(), RandomForestClassifier()),\n",
    "    'gb':make_pipeline(StandardScaler(), GradientBoostingClassifier()),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit_models = {}\n",
    "for algo, pipeline in pipelines.items():\n",
    "    model = pipeline.fit(X_train, y_train)\n",
    "    fit_models[algo] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rc': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('ridgeclassifier', RidgeClassifier())]),\n",
       " 'rf': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('randomforestclassifier', RandomForestClassifier())]),\n",
       " 'gb': Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                 ('gradientboostingclassifier', GradientBoostingClassifier())])}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sad', 'Victorious', 'Sad', 'Sad', 'Happy', 'Angry', 'Happy',\n",
       "       'Sad', 'Thinking', 'Happy', 'Victorious', 'Angry', 'Smile',\n",
       "       'Thinking', 'Victorious', 'Angry', 'Sad', 'Victorious',\n",
       "       'Victorious', 'Victorious', 'Victorious', 'Victorious', 'Smile',\n",
       "       'Happy', 'Happy', 'Sad', 'Thinking', 'Sad', 'Sad', 'Angry',\n",
       "       'Smile', 'Angry', 'Thinking', 'Victorious', 'Happy', 'Victorious',\n",
       "       'Sad', 'Sad', 'Thinking', 'Sad', 'Angry', 'Sad', 'Sad', 'Sad',\n",
       "       'Victorious', 'Sad', 'Angry', 'Sad', 'Sad', 'Victorious', 'Happy',\n",
       "       'Thinking', 'Smile', 'Thinking', 'Smile', 'Happy', 'Thinking',\n",
       "       'Angry', 'Sad', 'Angry', 'Thinking', 'Thinking', 'Sad',\n",
       "       'Victorious', 'Sad', 'Victorious', 'Thinking', 'Victorious',\n",
       "       'Happy', 'Thinking', 'Sad', 'Angry', 'Thinking', 'Angry', 'Happy',\n",
       "       'Victorious', 'Happy', 'Sad', 'Angry', 'Victorious', 'Thinking',\n",
       "       'Victorious', 'Victorious', 'Sad', 'Angry', 'Sad', 'Angry',\n",
       "       'Angry', 'Thinking', 'Smile', 'Angry', 'Happy', 'Sad', 'Thinking',\n",
       "       'Smile', 'Victorious', 'Thinking', 'Sad', 'Sad', 'Sad',\n",
       "       'Victorious', 'Sad', 'Victorious', 'Angry', 'Happy', 'Sad',\n",
       "       'Victorious', 'Victorious', 'Victorious', 'Thinking', 'Victorious',\n",
       "       'Sad', 'Thinking', 'Victorious', 'Sad', 'Sad', 'Sad', 'Angry',\n",
       "       'Victorious', 'Happy', 'Victorious', 'Victorious', 'Angry',\n",
       "       'Thinking', 'Victorious', 'Thinking', 'Sad', 'Sad', 'Sad', 'Angry',\n",
       "       'Angry', 'Thinking', 'Thinking', 'Sad', 'Victorious', 'Angry',\n",
       "       'Happy', 'Sad', 'Angry', 'Victorious', 'Smile', 'Angry', 'Angry',\n",
       "       'Sad', 'Victorious', 'Victorious', 'Smile', 'Happy', 'Thinking',\n",
       "       'Smile', 'Happy', 'Sad', 'Angry', 'Victorious', 'Sad',\n",
       "       'Victorious', 'Sad', 'Thinking', 'Victorious', 'Sad', 'Smile',\n",
       "       'Thinking', 'Smile', 'Happy', 'Happy', 'Angry', 'Angry', 'Sad',\n",
       "       'Victorious', 'Victorious', 'Happy', 'Angry', 'Victorious', 'Sad',\n",
       "       'Angry', 'Thinking', 'Sad', 'Sad', 'Smile', 'Victorious',\n",
       "       'Thinking', 'Victorious', 'Victorious', 'Sad', 'Angry', 'Sad',\n",
       "       'Angry', 'Angry', 'Happy', 'Angry', 'Happy', 'Smile', 'Victorious',\n",
       "       'Angry', 'Smile', 'Victorious', 'Victorious', 'Thinking', 'Smile',\n",
       "       'Sad', 'Happy', 'Victorious', 'Happy', 'Sad', 'Thinking',\n",
       "       'Thinking', 'Victorious', 'Victorious', 'Sad', 'Victorious',\n",
       "       'Thinking', 'Thinking', 'Victorious', 'Victorious', 'Angry', 'Sad',\n",
       "       'Thinking', 'Sad', 'Sad', 'Angry', 'Thinking', 'Angry', 'Happy',\n",
       "       'Angry', 'Sad', 'Happy', 'Sad', 'Thinking', 'Thinking', 'Sad',\n",
       "       'Victorious', 'Thinking', 'Victorious', 'Happy', 'Thinking', 'Sad',\n",
       "       'Smile', 'Victorious', 'Victorious', 'Angry', 'Sad', 'Victorious',\n",
       "       'Sad', 'Smile', 'Angry', 'Angry', 'Angry', 'Victorious',\n",
       "       'Victorious', 'Victorious'], dtype='<U10')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rc'].predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Evaluate and Serialize Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score # Accuracy metrics \n",
    "import pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rc 1.0\n",
      "rf 1.0\n",
      "gb 1.0\n"
     ]
    }
   ],
   "source": [
    "for algo, model in fit_models.items():\n",
    "    yhat = model.predict(X_test)\n",
    "    print(algo, accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sad', 'Victorious', 'Sad', 'Sad', 'Happy', 'Angry', 'Happy',\n",
       "       'Sad', 'Thinking', 'Happy', 'Victorious', 'Angry', 'Smile',\n",
       "       'Thinking', 'Victorious', 'Angry', 'Sad', 'Victorious',\n",
       "       'Victorious', 'Victorious', 'Victorious', 'Victorious', 'Smile',\n",
       "       'Happy', 'Happy', 'Sad', 'Thinking', 'Sad', 'Sad', 'Angry',\n",
       "       'Smile', 'Angry', 'Thinking', 'Victorious', 'Happy', 'Victorious',\n",
       "       'Sad', 'Sad', 'Thinking', 'Sad', 'Angry', 'Sad', 'Sad', 'Sad',\n",
       "       'Victorious', 'Sad', 'Angry', 'Sad', 'Sad', 'Victorious', 'Happy',\n",
       "       'Thinking', 'Smile', 'Thinking', 'Smile', 'Happy', 'Thinking',\n",
       "       'Angry', 'Sad', 'Angry', 'Thinking', 'Thinking', 'Sad',\n",
       "       'Victorious', 'Sad', 'Victorious', 'Thinking', 'Victorious',\n",
       "       'Happy', 'Thinking', 'Sad', 'Angry', 'Thinking', 'Angry', 'Happy',\n",
       "       'Victorious', 'Happy', 'Sad', 'Angry', 'Victorious', 'Thinking',\n",
       "       'Victorious', 'Victorious', 'Sad', 'Angry', 'Sad', 'Angry',\n",
       "       'Angry', 'Thinking', 'Smile', 'Angry', 'Happy', 'Sad', 'Thinking',\n",
       "       'Smile', 'Victorious', 'Thinking', 'Sad', 'Sad', 'Sad',\n",
       "       'Victorious', 'Sad', 'Victorious', 'Angry', 'Happy', 'Sad',\n",
       "       'Victorious', 'Victorious', 'Victorious', 'Thinking', 'Victorious',\n",
       "       'Sad', 'Thinking', 'Victorious', 'Sad', 'Sad', 'Sad', 'Angry',\n",
       "       'Victorious', 'Happy', 'Victorious', 'Victorious', 'Angry',\n",
       "       'Thinking', 'Victorious', 'Thinking', 'Sad', 'Sad', 'Sad', 'Angry',\n",
       "       'Angry', 'Thinking', 'Thinking', 'Sad', 'Victorious', 'Angry',\n",
       "       'Happy', 'Sad', 'Angry', 'Victorious', 'Smile', 'Angry', 'Angry',\n",
       "       'Sad', 'Victorious', 'Victorious', 'Smile', 'Happy', 'Thinking',\n",
       "       'Smile', 'Happy', 'Sad', 'Angry', 'Victorious', 'Sad',\n",
       "       'Victorious', 'Sad', 'Thinking', 'Victorious', 'Sad', 'Smile',\n",
       "       'Thinking', 'Smile', 'Happy', 'Happy', 'Angry', 'Angry', 'Sad',\n",
       "       'Victorious', 'Victorious', 'Happy', 'Angry', 'Victorious', 'Sad',\n",
       "       'Angry', 'Thinking', 'Sad', 'Sad', 'Smile', 'Victorious',\n",
       "       'Thinking', 'Victorious', 'Victorious', 'Sad', 'Angry', 'Sad',\n",
       "       'Angry', 'Angry', 'Happy', 'Angry', 'Happy', 'Smile', 'Victorious',\n",
       "       'Angry', 'Smile', 'Victorious', 'Victorious', 'Thinking', 'Smile',\n",
       "       'Sad', 'Happy', 'Victorious', 'Happy', 'Sad', 'Thinking',\n",
       "       'Thinking', 'Victorious', 'Victorious', 'Sad', 'Victorious',\n",
       "       'Thinking', 'Thinking', 'Victorious', 'Victorious', 'Angry', 'Sad',\n",
       "       'Thinking', 'Sad', 'Sad', 'Angry', 'Thinking', 'Angry', 'Happy',\n",
       "       'Angry', 'Sad', 'Happy', 'Sad', 'Thinking', 'Thinking', 'Sad',\n",
       "       'Victorious', 'Thinking', 'Victorious', 'Happy', 'Thinking', 'Sad',\n",
       "       'Smile', 'Victorious', 'Victorious', 'Angry', 'Sad', 'Victorious',\n",
       "       'Sad', 'Smile', 'Angry', 'Angry', 'Angry', 'Victorious',\n",
       "       'Victorious', 'Victorious'], dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_models['rf'].predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "326           Sad\n",
       "602    Victorious\n",
       "288           Sad\n",
       "230           Sad\n",
       "362         Happy\n",
       "          ...    \n",
       "9           Angry\n",
       "77          Angry\n",
       "475    Victorious\n",
       "523    Victorious\n",
       "589    Victorious\n",
       "Name: class, Length: 250, dtype: object"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('body_language.pkl', 'wb') as f:\n",
    "    pickle.dump(fit_models['rf'], f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Make Detections with Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('body_language.pkl', 'rb') as f:\n",
    "    model = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('randomforestclassifier', RandomForestClassifier())])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.79 0.   0.12 0.08 0.   0.01]\n",
      "Angry [0.8  0.   0.12 0.07 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.79 0.   0.11 0.09 0.   0.01]\n",
      "Angry [0.79 0.   0.12 0.08 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.79 0.   0.11 0.09 0.   0.01]\n",
      "Angry [0.8  0.   0.11 0.08 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.8  0.   0.11 0.08 0.   0.01]\n",
      "Angry [0.79 0.   0.11 0.09 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.79 0.   0.11 0.09 0.   0.01]\n",
      "Angry [0.69 0.01 0.1  0.02 0.04 0.14]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.02 0.18 0.02 0.48 0.27]\n",
      "Thinking [0.02 0.02 0.17 0.04 0.43 0.32]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.12 0.   0.51 0.01 0.08 0.28]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.08 0.   0.57 0.01 0.08 0.26]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.04 0.   0.44 0.   0.18 0.34]\n",
      "Victorious [0.04 0.   0.32 0.02 0.19 0.43]\n",
      "Victorious [0.05 0.02 0.34 0.03 0.12 0.44]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.03 0.02 0.29 0.03 0.1  0.53]\n",
      "Victorious [0.05 0.   0.32 0.02 0.06 0.55]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.22 0.   0.26 0.   0.02 0.5 ]\n",
      "Victorious [0.11 0.   0.32 0.   0.02 0.55]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.08 0.   0.4  0.   0.07 0.45]\n",
      "Sad [0.04 0.   0.54 0.   0.08 0.34]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.05 0.   0.45 0.   0.1  0.4 ]\n",
      "Victorious [0.02 0.   0.4  0.   0.08 0.5 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.07 0.   0.41 0.   0.07 0.45]\n",
      "Sad [0.05 0.   0.46 0.   0.07 0.42]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.02 0.   0.54 0.   0.07 0.37]\n",
      "Sad [0.01 0.   0.55 0.   0.06 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.02 0.   0.58 0.   0.05 0.35]\n",
      "Sad [0.03 0.   0.59 0.   0.04 0.34]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.06 0.   0.56 0.   0.03 0.35]\n",
      "Sad [0.17 0.   0.55 0.   0.01 0.27]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.14 0.   0.55 0.   0.01 0.3 ]\n",
      "Sad [0.18 0.   0.59 0.   0.01 0.22]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.18 0.01 0.59 0.   0.01 0.21]\n",
      "Sad [0.22 0.01 0.6  0.   0.01 0.16]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.2  0.01 0.63 0.   0.01 0.15]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.19 0.01 0.67 0.   0.   0.13]\n",
      "Sad [0.17 0.01 0.68 0.   0.   0.14]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.17 0.01 0.68 0.   0.   0.14]\n",
      "Sad [0.2  0.01 0.65 0.   0.01 0.13]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.18 0.01 0.65 0.   0.01 0.15]\n",
      "Sad [0.18 0.01 0.69 0.   0.   0.12]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.19 0.01 0.69 0.   0.   0.11]\n",
      "Sad [0.19 0.01 0.68 0.   0.   0.12]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.17 0.01 0.68 0.   0.   0.14]\n",
      "Sad [0.15 0.   0.68 0.   0.   0.17]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.16 0.   0.67 0.   0.01 0.16]\n",
      "Sad [0.16 0.01 0.69 0.   0.   0.14]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.15 0.01 0.67 0.   0.   0.17]\n",
      "Sad [0.17 0.01 0.67 0.   0.   0.15]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.17 0.   0.64 0.   0.01 0.18]\n",
      "Sad [0.19 0.   0.64 0.   0.01 0.16]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.42 0.05 0.41 0.01 0.   0.11]\n",
      "Angry [0.39 0.05 0.35 0.01 0.   0.2 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.1  0.   0.53 0.   0.07 0.3 ]\n",
      "Sad [0.04 0.   0.43 0.   0.12 0.41]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.05 0.   0.26 0.   0.28 0.41]\n",
      "Victorious [0.06 0.   0.32 0.   0.17 0.45]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.11 0.   0.4  0.   0.06 0.43]\n",
      "Victorious [0.12 0.   0.32 0.   0.09 0.47]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.2  0.01 0.24 0.   0.16 0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.19 0.   0.39 0.   0.05 0.37]\n",
      "Sad [0.13 0.   0.46 0.   0.04 0.37]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.16 0.   0.48 0.   0.03 0.33]\n",
      "Sad [0.1  0.   0.5  0.   0.05 0.35]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.07 0.   0.55 0.   0.05 0.33]\n",
      "Sad [0.03 0.   0.51 0.   0.14 0.32]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.02 0.   0.23 0.   0.49 0.26]\n",
      "Thinking [0.02 0.01 0.19 0.01 0.46 0.31]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.01 0.01 0.26 0.   0.43 0.29]\n",
      "Thinking [0.   0.   0.22 0.   0.56 0.22]\n",
      "Thinking [0.02 0.   0.15 0.01 0.5  0.32]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.01 0.01 0.07 0.   0.57 0.34]\n",
      "Thinking [0.01 0.   0.11 0.   0.49 0.39]\n",
      "Thinking [0.01 0.   0.16 0.   0.56 0.27]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.   0.   0.14 0.   0.6  0.26]\n",
      "Thinking [0.02 0.   0.13 0.   0.46 0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.02 0.01 0.12 0.   0.59 0.26]\n",
      "Thinking [0.01 0.01 0.13 0.01 0.56 0.28]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.01 0.   0.13 0.01 0.59 0.26]\n",
      "Thinking [0.02 0.   0.21 0.   0.44 0.33]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.02 0.   0.46 0.   0.26 0.26]\n",
      "Victorious [0.03 0.   0.36 0.   0.24 0.37]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.04 0.   0.4  0.   0.24 0.32]\n",
      "Sad [0.04 0.   0.42 0.   0.25 0.29]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.03 0.   0.51 0.   0.15 0.31]\n",
      "Sad"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [0.04 0.   0.55 0.   0.09 0.32]\n",
      "Sad [0.07 0.   0.65 0.   0.02 0.26]\n",
      "Sad [0.24 0.   0.43 0.02 0.01 0.3 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.34 0.01 0.21 0.04 0.01 0.39]\n",
      "Victorious [0.39 0.05 0.11 0.03 0.   0.42]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.36 0.04 0.07 0.05 0.   0.48]\n",
      "Victorious [0.36 0.04 0.07 0.01 0.   0.52]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.4  0.02 0.03 0.03 0.   0.52]\n",
      "Victorious [0.39 0.01 0.03 0.02 0.   0.55]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.29 0.01 0.02 0.03 0.   0.65]\n",
      "Victorious [0.36 0.01 0.06 0.02 0.   0.55]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.45 0.03 0.07 0.01 0.   0.44]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.49 0.02 0.08 0.01 0.   0.4 ]\n",
      "Angry [0.46 0.03 0.12 0.02 0.   0.37]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.42 0.03 0.2  0.03 0.01 0.31]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.48 0.03 0.19 0.01 0.   0.29]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.49 0.01 0.16 0.01 0.01 0.32]\n",
      "Angry [0.5  0.01 0.11 0.01 0.03 0.34]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.51 0.01 0.07 0.01 0.05 0.35]\n",
      "Sad [0.31 0.01 0.36 0.   0.04 0.28]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.02 0.   0.53 0.   0.15 0.3 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.03 0.   0.48 0.   0.19 0.3 ]\n",
      "Victorious [0.02 0.   0.37 0.   0.2  0.41]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.01 0.   0.32 0.   0.17 0.5 ]\n",
      "Victorious [0.01 0.   0.41 0.   0.13 0.45]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sad [0.05 0.   0.43 0.   0.09 0.43]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.19 0.   0.37 0.   0.03 0.41]\n",
      "Angry [0.38 0.01 0.24 0.01 0.04 0.32]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.45 0.01 0.24 0.01 0.02 0.27]\n",
      "Angry [0.54 0.01 0.18 0.01 0.01 0.25]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.58 0.01 0.19 0.02 0.01 0.19]\n",
      "Angry [0.52 0.01 0.08 0.04 0.08 0.27]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.6  0.01 0.08 0.02 0.06 0.23]\n",
      "Angry [0.6  0.   0.09 0.03 0.05 0.23]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.14 0.05 0.09 0.09 0.5  0.13]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.11 0.07 0.1  0.04 0.46 0.22]\n",
      "Thinking [0.14 0.07 0.14 0.04 0.44 0.17]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.37 0.02 0.19 0.02 0.05 0.35]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.48 0.   0.15 0.01 0.03 0.33]\n",
      "Angry [0.55 0.   0.24 0.02 0.02 0.17]\n",
      "Angry [0.59 0.   0.19 0.02 0.01 0.19]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.63 0.   0.15 0.02 0.   0.2 ]\n",
      "Angry [0.62 0.   0.15 0.02 0.   0.21]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.67 0.   0.13 0.02 0.   0.18]\n",
      "Angry [0.71 0.01 0.13 0.03 0.   0.12]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.78 0.01 0.14 0.03 0.   0.04]\n",
      "Angry [0.75 0.01 0.17 0.04 0.   0.03]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.77 0.01 0.16 0.04 0.   0.02]\n",
      "Angry [0.76 0.01 0.18 0.03 0.   0.02]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.78 0.   0.18 0.03 0.   0.01]\n",
      "Angry [0.73 0.01 0.22 0.03 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.7  0.   0.26 0.03 0.   0.01]\n",
      "Sad [0.39 0.02 0.46 0.03 0.04 0.06]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.04 0.   0.28 0.   0.44 0.24]\n",
      "Thinking [0.03 0.   0.13 0.   0.47 0.37]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.   0.07 0.   0.52 0.38]\n",
      "Thinking [0.03 0.   0.08 0.   0.51 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.01 0.01 0.04 0.01 0.6  0.33]\n",
      "Thinking [0.01 0.01 0.04 0.02 0.58 0.34]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.02 0.01 0.09 0.03 0.47 0.38]\n",
      "Thinking [0.03 0.   0.1  0.   0.49 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.   0.14 0.   0.45 0.38]\n",
      "Thinking [0.03 0.01 0.15 0.   0.42 0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.01 0.18 0.   0.39 0.39]\n",
      "Thinking [0.03 0.01 0.17 0.   0.4  0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.03 0.01 0.18 0.   0.38 0.4 ]\n",
      "Thinking [0.03 0.01 0.16 0.   0.42 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.   0.15 0.   0.44 0.38]\n",
      "Thinking [0.03 0.01 0.15 0.   0.43 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.01 0.02 0.13 0.03 0.43 0.38]\n",
      "Thinking [0.01 0.04 0.14 0.03 0.44 0.34]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.02 0.04 0.17 0.01 0.39 0.37]\n",
      "Victorious [0.04 0.03 0.18 0.   0.32 0.43]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.06 0.   0.21 0.   0.34 0.39]\n",
      "Victorious [0.05 0.01 0.19 0.   0.36 0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.05 0.01 0.16 0.   0.39 0.39]\n",
      "Victorious [0.03 0.01 0.2  0.   0.37 0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.01 0.2  0.   0.38 0.38]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.03 0.01 0.21 0.   0.33 0.42]\n",
      "Victorious [0.03 0.02 0.19 0.   0.36 0.4 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.02 0.17 0.   0.41 0.37]\n",
      "Thinking [0.03 0.01 0.17 0.   0.4  0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thinking [0.03 0.01 0.17 0.   0.4  0.39]\n",
      "Thinking [0.03 0.01 0.17 0.   0.4  0.39]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victorious [0.06 0.   0.19 0.   0.37 0.38]\n",
      "Sad [0.14 0.   0.35 0.01 0.19 0.31]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.55 0.   0.12 0.03 0.1  0.2 ]\n",
      "Angry [0.63 0.   0.19 0.08 0.01 0.09]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.66 0.   0.22 0.08 0.   0.04]\n",
      "Angry [0.68 0.   0.24 0.08 0.   0.  ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.72 0.   0.22 0.06 0.   0.  ]\n",
      "Angry [0.8  0.   0.14 0.06 0.   0.  ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.82 0.   0.11 0.06 0.   0.01]\n",
      "Angry [0.82 0.   0.11 0.06 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.82 0.   0.11 0.06 0.   0.01]\n",
      "Angry [0.82 0.   0.11 0.06 0.   0.01]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\skarasala\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Angry [0.84 0.   0.09 0.06 0.   0.01]\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Initiate holistic model\n",
    "with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:\n",
    "    \n",
    "    while cap.isOpened():\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Recolor Feed\n",
    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        image.flags.writeable = False        \n",
    "        \n",
    "        # Make Detections\n",
    "        results = holistic.process(image)\n",
    "        # print(results.face_landmarks)\n",
    "        \n",
    "        # face_landmarks, pose_landmarks, left_hand_landmarks, right_hand_landmarks\n",
    "        \n",
    "        # Recolor image back to BGR for rendering\n",
    "        image.flags.writeable = True   \n",
    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
    "        \n",
    "        # 1. Draw face landmarks\n",
    "        mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_TESSELATION, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)\n",
    "                                 )\n",
    "        \n",
    "        # 2. Right hand\n",
    "        mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 3. Left Hand\n",
    "        mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "\n",
    "        # 4. Pose Detections\n",
    "        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS, \n",
    "                                 mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),\n",
    "                                 mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)\n",
    "                                 )\n",
    "        # Export coordinates\n",
    "        try:\n",
    "            # Extract Pose landmarks\n",
    "            pose = results.pose_landmarks.landmark\n",
    "            pose_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in pose]).flatten())\n",
    "            \n",
    "            # Extract Face landmarks\n",
    "            face = results.face_landmarks.landmark\n",
    "            face_row = list(np.array([[landmark.x, landmark.y, landmark.z, landmark.visibility] for landmark in face]).flatten())\n",
    "            \n",
    "            # Concate rows\n",
    "            row = pose_row+face_row\n",
    "            \n",
    "#             # Append class name \n",
    "#             row.insert(0, class_name)\n",
    "            \n",
    "#             # Export to CSV\n",
    "#             with open('coords.csv', mode='a', newline='') as f:\n",
    "#                 csv_writer = csv.writer(f, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "#                 csv_writer.writerow(row) \n",
    "\n",
    "            # Make Detections\n",
    "            X = pd.DataFrame([row])\n",
    "            body_language_class = model.predict(X)[0]\n",
    "            body_language_prob = model.predict_proba(X)[0]\n",
    "            print(body_language_class, body_language_prob)\n",
    "            \n",
    "            # Grab ear coords\n",
    "            coords = tuple(np.multiply(\n",
    "                            np.array(\n",
    "                                (results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "                                 results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y))\n",
    "                        , [640,480]).astype(int))\n",
    "            \n",
    "            cv2.rectangle(image, \n",
    "                          (coords[0], coords[1]+5), \n",
    "                          (coords[0]+len(body_language_class)*20, coords[1]-30), \n",
    "                          (245, 117, 16), -1)\n",
    "            cv2.putText(image, body_language_class, coords, \n",
    "                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Get status box\n",
    "            cv2.rectangle(image, (0,0), (250, 60), (245, 117, 16), -1)\n",
    "            \n",
    "            # Display Class\n",
    "            cv2.putText(image, 'CLASS'\n",
    "                        , (95,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, body_language_class.split(' ')[0]\n",
    "                        , (90,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "            # Display Probability\n",
    "            cv2.putText(image, 'PROB'\n",
    "                        , (15,12), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)\n",
    "            cv2.putText(image, str(round(body_language_prob[np.argmax(body_language_prob)],2))\n",
    "                        , (10,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "            \n",
    "        except:\n",
    "            pass\n",
    "                        \n",
    "        cv2.imshow('Webcam', image)\n",
    "\n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(401, 227)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuple(np.multiply(np.array((results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].x, \n",
    "results.pose_landmarks.landmark[mp_holistic.PoseLandmark.LEFT_EAR].y)), [640,480]).astype(int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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