log_speed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this file i used logistic regression for training speed col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   video_id element  motion power  speed          video_summary\n",
      "0         1    Erde  linear   rot    9.6     (3.8147, 31.94809)\n",
      "1         2   Feuer     shm  grin    9.6   (26.70288, -4.29153)\n",
      "2         3   Feuer  random  geld    9.6      (3.8147, 8.58307)\n",
      "3         4    Erde  zigzag  lila    6.9  (-24.79553, -0.95367)\n",
      "4         5    Erde  linear  lila    9.6    (7.62939, 22.88818)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Load the training data (You might have to change the file path based on how you're working)\n",
    "train_df = pd.read_csv('BH25/Training_Data/train.csv')\n",
    "\n",
    "print(train_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: element\n",
      "Attribute -> Encoded Value\n",
      "Boden -> 0\n",
      "Erde -> 1\n",
      "Feuer -> 2\n",
      "Luft -> 3\n",
      "Wasser -> 4\n",
      "\n",
      "Column: motion\n",
      "Attribute -> Encoded Value\n",
      "circular -> 0\n",
      "linear -> 1\n",
      "random -> 2\n",
      "shm -> 3\n",
      "zigzag -> 4\n",
      "\n",
      "Column: power\n",
      "Attribute -> Encoded Value\n",
      "baau -> 0\n",
      "blau -> 1\n",
      "blru -> 2\n",
      "brun -> 3\n",
      "btau -> 4\n",
      "gaun -> 5\n",
      "gela -> 6\n",
      "gelb -> 7\n",
      "geld -> 8\n",
      "gele -> 9\n",
      "gelt -> 10\n",
      "got -> 11\n",
      "grin -> 12\n",
      "grun -> 13\n",
      "grus -> 14\n",
      "helb -> 15\n",
      "lela -> 16\n",
      "liga -> 17\n",
      "lila -> 18\n",
      "lili -> 19\n",
      "lisa -> 20\n",
      "lula -> 21\n",
      "not -> 22\n",
      "plau -> 23\n",
      "rat -> 24\n",
      "rod -> 25\n",
      "rot -> 26\n",
      "rut -> 27\n",
      "slau -> 28\n",
      "trun -> 29\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encode categorical attributes using the Label Encoder\n",
    "def encode_categorical_columns(df, columns):\n",
    "    label_encoders = {}\n",
    "    for col in columns:\n",
    "        le = LabelEncoder()\n",
    "        df[col] = le.fit_transform(df[col])\n",
    "        label_encoders[col] = le\n",
    "    return df, label_encoders\n",
    "\n",
    "# List of categorical columns to encode\n",
    "categorical_columns = ['element', 'motion', 'power']\n",
    "\n",
    "# Encoding the dataframe\n",
    "train_df, label_encoders = encode_categorical_columns(train_df, categorical_columns)\n",
    "\n",
    "# Print the mapping for each column\n",
    "for col in categorical_columns:\n",
    "    print(f\"Column: {col}\")\n",
    "    print(\"Attribute -> Encoded Value\")\n",
    "    for attribute, value in zip(label_encoders[col].classes_, range(len(label_encoders[col].classes_))):\n",
    "        print(f\"{attribute} -> {value}\")\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 \n",
    "\n",
    "def process_video(video_path):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frames = []\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        frame = cv2.resize(frame, (64, 64))\n",
    "        frames.append(frame)\n",
    "    cap.release()\n",
    "    frames = np.array(frames)  # Shape: (20, 64, 64, 3)\n",
    "    return frames.flatten()  # Flatten to 1D array\n",
    "    \n",
    "video_features = []\n",
    "for idx, row in train_df.iterrows():\n",
    "    video_path = f\"BH25/Training_Data/Train_Videos/{row['video_id']}.mp4\"\n",
    "    video_features.append(process_video(video_path))\n",
    "\n",
    "video_features = np.array(video_features)  # Shape: (num_samples, 20*64*64*3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "y_speed = train_df[['speed']]\n",
    "y_summary = train_df['video_summary'].apply(lambda x: eval(x))  # Convert string tuples to actual tuples\n",
    "\n",
    "y_categorical = train_df[categorical_columns]\n",
    "\n",
    "# Split data for training and validation\n",
    "X_train_speed, X_val_speed, y_train_speed, y_val_speed = train_test_split(video_features, y_speed, test_size=0.2, random_state=42)\n",
    "X_train_cat, X_val_cat, y_train_cat, y_val_cat = train_test_split(video_features, y_categorical, test_size=0.2, random_state=42)\n",
    "X_train_summary, X_val_summary, y_train_summary, y_val_summary = train_test_split(video_features, y_summary, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_batch_speed=X_train_speed[:3000]\n",
    "y_batch_speed=y_train_speed[:3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hp\\anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1339: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
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       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
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       "  background-color: var(--sklearn-color-background);\n",
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       "  width: 0;\n",
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       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
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       "/* Toggleable label */\n",
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       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
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       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
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       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
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       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
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       "\n",
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       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
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       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "\n",
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       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
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       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
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       "  color: var(--sklearn-color-background);\n",
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       "}\n",
       "\n",
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       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
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       "\n",
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       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
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       "  margin: .5ex;\n",
       "  width: min-content;\n",
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       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train logistic speed\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Map 6.9 to 0 and 9.6 to 1\n",
    "y_batch_speed_binary = np.where(y_batch_speed == 6.9, 0, 1)\n",
    "# Initialize and train the model\n",
    "model = LogisticRegression(max_iter=1000)  # Increase max_iter if needed for convergence\n",
    "model.fit(X_batch_speed, y_batch_speed_binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.2659499999999999\n"
     ]
    }
   ],
   "source": [
    "#for mae of speed for logistic\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# Example: Convert binary predictions back to continuous values\n",
    "y_pred_speed_binary = model.predict(X_val_speed)  # y_pred_binary is the binary output from LogisticRegression\n",
    "y_pred_speed = np.where(y_pred_speed_binary == 0, 6.9, 9.6)\n",
    "\n",
    "# Calculate MAE\n",
    "mae_continuous = mean_absolute_error(y_val_speed, y_pred_speed)\n",
    "\n",
    "# Output the result\n",
    "print(\"Mean Absolute Error:\", mae_continuous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "test_folder = 'BH25/Testing_Data'\n",
    "\n",
    "test_features = []\n",
    "video_ids = []\n",
    "\n",
    "# The below function finds all the video ids in the test folder and sorts them, \n",
    "# and then stores their features in test_features\n",
    "\n",
    "for video_id in sorted(\n",
    "    [f for f in os.listdir(test_folder) if f.endswith(\".mp4\")], key=lambda x: int(x.split('.')[0])):\n",
    "    video_path = os.path.join(test_folder, video_id)\n",
    "    video_ids.append(video_id.split('.')[0]) \n",
    "    test_features.append(process_video(video_path))\n",
    "\n",
    "test_features = np.array(test_features)  # Shape: (num_test_samples, 20*64*64*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the binary output back to 6.9 and 9.6\n",
    "y_test_speed_binary = model.predict(test_features)\n",
    "y_test_speed = np.where(y_test_speed_binary == 0, 6.9, 9.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "speed_predictions = y_test_speed.flatten()  # Ensure the shape is (num_samples,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_df = pd.DataFrame({\n",
    "    'video_id': video_ids,                      \n",
    "    'speed': speed_predictions,               \n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_df.to_csv(\"speed.csv\", index=False)"
   ]
  }
 ],
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