sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to get metric\n",
    "def get_metrics_as_dataframe(model_name, metrics_dict):\n",
    "    model_name = model_name  # Replace with your model's name\n",
    "    accuracy = metrics_dict['Accuracy']\n",
    "    precision = metrics_dict['Precision']\n",
    "    recall = metrics_dict['Recall']\n",
    "    f1 = metrics_dict['F1 Score']\n",
    "    \n",
    "    table = pd.DataFrame({\n",
    "        'Model': [model_name],\n",
    "        'Accuracy': [accuracy],\n",
    "        'Precision': [precision],\n",
    "        'Recall': [recall],\n",
    "        'F1 Score': [f1],\n",
    "    })\n",
    "\n",
    "    return table\n",
    "\n",
    "\n",
    "## Function to fit loaded model to our dataset\n",
    "def sentiment_score(review):\n",
    "    with torch.no_grad():\n",
    "        tokens = tokenizer.encode(review, return_tensors='pt')\n",
    "        result = model(tokens)\n",
    "        return int(torch.argmax(result.logits))+1 # +1 because the model's output is [0,1,2,3,4] and our dataset labels are [1,2,3,4,5]\n",
    "\n",
    "## Similar function but without +1    \n",
    "def sentiment_score_2(review):\n",
    "    with torch.no_grad():\n",
    "        tokens = tokenizer.encode(review, return_tensors='pt')\n",
    "        result = model(tokens)\n",
    "        return int(torch.argmax(result.logits))\n",
    "    \n",
    "\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "def evaluate_classification(y_true, y_pred):\n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    recall = recall_score(y_true, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "    \n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    \n",
    "    return {\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1 Score': f1,\n",
    "        'Confusion Matrix': cm\n",
    "    }\n",
    "\n",
    "# Example usage:\n",
    "# y_true = true ratings (ground truth)\n",
    "# y_pred = predicted ratings (output of your sentiment analysis model)\n",
    "# results = evaluate_classification(y_true, y_pred)\n",
    "# print(results)\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Example usage:\n",
    "class_names_1 = ['1', '2', '3', '4', '5']\n",
    "class_names_2 = ['0', '1', '2']\n",
    "class_names_3 = ['0', '1']\n",
    "\n",
    "\n",
    "def plot_confusion_matrix(true_labels, predicted_labels, class_names):\n",
    "    # Calculate the confusion matrix\n",
    "    confusion = confusion_matrix(true_labels, predicted_labels)\n",
    "\n",
    "    # Calculate row-wise sums (total predictions for each class)\n",
    "    row_sums = confusion.sum(axis=1)\n",
    "\n",
    "    # Create a larger heatmap using Seaborn\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    # Create the heatmap\n",
    "    heatmap = sns.heatmap(confusion / row_sums[:, np.newaxis], annot=True, fmt=\".1%\", cmap='Blues', cbar=False)\n",
    "\n",
    "    # Customize the plot\n",
    "    plt.xlabel('Predicted', fontsize=14)  # You can adjust font size\n",
    "    plt.ylabel('True', fontsize=14)       # You can adjust font size\n",
    "\n",
    "    # Add class names as tick labels for both axes, centered between the boxes\n",
    "    tick_marks = np.arange(len(class_names))\n",
    "    plt.xticks(tick_marks + 0.5, class_names, rotation=0)  # Adjusted to center labels\n",
    "    plt.yticks(tick_marks + 0.5, class_names, rotation=0)   # Adjusted to center labels\n",
    "\n",
    "    plt.title('Confusion Matrix (in Percentage)', fontsize=16)  # You can adjust font size\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/womens_clothing_ecommerce_reviews.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Clothing ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Title</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Recommended IND</th>\n",
       "      <th>Positive Feedback Count</th>\n",
       "      <th>Division Name</th>\n",
       "      <th>Department Name</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>847</td>\n",
       "      <td>33</td>\n",
       "      <td>Cute, crisp shirt</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1077</td>\n",
       "      <td>60</td>\n",
       "      <td>Some major design flaws</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>General</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1049</td>\n",
       "      <td>50</td>\n",
       "      <td>My favorite buy!</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>General Petite</td>\n",
       "      <td>Bottoms</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>847</td>\n",
       "      <td>47</td>\n",
       "      <td>Flattering shirt</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>General</td>\n",
       "      <td>Tops</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Clothing ID  Age                    Title  \\\n",
       "0           0          847   33        Cute, crisp shirt   \n",
       "1           1         1080   34                      NaN   \n",
       "2           2         1077   60  Some major design flaws   \n",
       "3           3         1049   50         My favorite buy!   \n",
       "4           4          847   47         Flattering shirt   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "0  If this product was in petite  i would get the...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love  love  love this jumpsuit. it's fun  fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "\n",
       "   Positive Feedback Count   Division Name Department Name Class Name  \n",
       "0                        2         General            Tops    Blouses  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review Text</th>\n",
       "      <th>Class Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating                                        Review Text Class Name\n",
       "0       4  If this product was in petite  i would get the...    Blouses\n",
       "1       5  Love this dress!  it's sooo pretty.  i happene...    Dresses\n",
       "2       3  I had such high hopes for this dress and reall...    Dresses\n",
       "3       5  I love  love  love this jumpsuit. it's fun  fl...      Pants\n",
       "4       5  This shirt is very flattering to all due to th...    Blouses"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment = df[['Rating', 'Review Text', 'Class Name']]\n",
    "sentiment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed = sentiment.rename(columns={'Review Text': 'review_body',\n",
    "                                    'Rating': 'star_rating',\n",
    "                                    'Class Name': 'product_category'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                                        review_body  \\\n",
       "0            4  If this product was in petite  i would get the...   \n",
       "1            5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2            3  I had such high hopes for this dress and reall...   \n",
       "3            5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4            5  This shirt is very flattering to all due to th...   \n",
       "\n",
       "  product_category  \n",
       "0          Blouses  \n",
       "1          Dresses  \n",
       "2          Dresses  \n",
       "3            Pants  \n",
       "4          Blouses  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23486, 3)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating           0\n",
       "review_body         845\n",
       "product_category     14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check forNaN values\n",
    "df_transformed.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_transformed.isna(), cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove NaN\n",
    "df_transformed.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22628, 3)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Assuming df_transformed is your DataFrame\n",
    "df_transformed.drop_duplicates(inplace=True)\n",
    "\n",
    "# Now, if you check the number of duplicated rows, it should be 0\n",
    "print(df_transformed.duplicated().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   star_rating                                        review_body  \\\n",
       "0            4  If this product was in petite  i would get the...   \n",
       "1            5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2            3  I had such high hopes for this dress and reall...   \n",
       "3            5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4            5  This shirt is very flattering to all due to th...   \n",
       "\n",
       "  product_category  \n",
       "0          Blouses  \n",
       "1          Dresses  \n",
       "2          Dresses  \n",
       "3            Pants  \n",
       "4          Blouses  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22622 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  \n",
       "0              Blouses  \n",
       "1              Dresses  \n",
       "2              Dresses  \n",
       "3                Pants  \n",
       "4              Blouses  \n",
       "...                ...  \n",
       "22617          Dresses  \n",
       "22618            Knits  \n",
       "22619          Dresses  \n",
       "22620          Dresses  \n",
       "22621          Dresses  \n",
       "\n",
       "[22622 rows x 3 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.reset_index(drop=True, inplace=True )\n",
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22620, 3)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove reviews for product_categories with < 10 reviews\n",
    "df_transformed = df_transformed.groupby('product_category').filter(lambda reviews : len(reviews) > 10)[['star_rating', 'review_body', 'product_category']]\n",
    "\n",
    "df_transformed.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "star_rating\n",
       "1      821\n",
       "2     1548\n",
       "3     2823\n",
       "4     4906\n",
       "5    12522\n",
       "Name: review_body, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_sentiment = df_transformed.groupby('star_rating')['review_body'].count()\n",
    "count_sentiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out how many reviews per sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='star_rating'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_sentiment.plot(kind='bar', x='star_rating', y='count_sentiment', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find out number of reviews per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_category\n",
       "Dresses       6144\n",
       "Knits         4625\n",
       "Blouses       2983\n",
       "Sweaters      1380\n",
       "Pants         1350\n",
       "Jeans         1103\n",
       "Fine gauge    1059\n",
       "Skirts         903\n",
       "Jackets        683\n",
       "Lounge         668\n",
       "Swim           330\n",
       "Outerwear      319\n",
       "Shorts         304\n",
       "Sleep          214\n",
       "Legwear        158\n",
       "Intimates      147\n",
       "Layering       132\n",
       "Trend          118\n",
       "Name: star_rating, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_sentiment_per_category = df_transformed.groupby('product_category')['star_rating'].count()\n",
    "count_sentiment_per_category.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GuH37tlrekSNHCu3cV65cwfXr16XtxMREHD16FE2aNAHw6vLelStXUKlSJdStW1dKq1atwtKlS6GtrZ3vczVu3BgApBmvHL///juysrLg5ub2n/pSuXJlWFpaIj4+Xq2tZcuWxY8//qi2QrZz584QQmDcuHG4f/8+fHx83tru/IyBt7c3Tp48iR07dkhBWtOmTREfH4+FCxeiRYsWUqA3cuRIuLq6QggBAwMDfPHFF1Jw+K6HyG7evFlt+48//oCdnR0qVKgADw8PAK8CvNfb+vfff2PcuHG5BmLv8uLFC+zatUtte/v27WjWrFmex+QEr//+rG/cuIFjx45Jn3WzZs1w9OhR3LlzRyrz119/4caNG9J2zqzy638HGRkZOHnypLRduXJlWFhYICIiQu18K1euRJs2bfDy5ct8/a66u7vjxo0biI2NlfLS0tLQvn17LF26FEePHkV2djYCAwOlwC0rK0u6fJ6dnQ0Ab5yrcePGePr0KYQQap/LhQsXMHHiRGRmZqJRo0bQ1tbGpk2b1I7duHGj9HN+x/VtfH198fjxY4wYMQJVqlRRu7WCioGPecMdUXH1+oKF1z18+FBYWVmp3agthBBdu3YVenp6YuHChWLv3r3Cx8dHWFhYvLFg4fWbo4V4c1GDEG8uBOjZs6fQ19cXVatWFZs2bRJ//PGHqFKlirCxsRH3798XQghx584dYWlpKVxdXcW6devE3r17Rb9+/QQAMXPmTCFE3jeU56Znz57SatM9e/aIadOmCUNDQ9GkSRORlZWVaztzk1eZxYsXCy0tLTFkyBCxe/dusX79elGjRg1hamoqbty4oVb2q6++Etra2qJBgwZq+f/uT37GQIhXN7br6ekJAGLt2rVCCCEyMjKEkZGRACCWLl0qld2+fbuQyWSiR48eYvfu3WLr1q3Cw8NDmJmZiYcPH+ba55yFAbq6uiIwMFDs2rVL9O/fXwBQu+Hcx8dHKJVK8euvv4rIyEhpxa2Li4vIzMwUQuT9e/hvOb9H1tbWYunSpSIiIkJ4eHgIhUIhrl69qtau/fv3qx2b8xl16dJFbN++XaxcuVI4OjoKKysrkZSUJIQQ4sGDB8LGxkZUq1ZN/PHHH+L3338XFSpUEHp6emp/Bw0bNhTm5uYiLCxM7NixQ7Rq1UpYWFio/d7PmzdPABCDBw8Wu3fvFnPnzhVGRkbi+++/F0L8/wKh6dOnqy3ueV1qaqqoUKGCcHR0FKtXrxY7d+4UrVu3ln5/cvrq4+MjIiMjxR9//CGaNGkiZDKZACBSU1OFEEL4+/sLPT09sX37dnH37l2RlZUlPDw8hI2NjZg/f77Yv3+/+OWXX4RcLhft2rWTzt+vXz+hr68vfv31V7Fr1y4xcOBAoaWlJQBIK8DzM67v+hvKWQTxyy+/vPN3gDQLgzeiIvC2L82NGze+EbwlJyeLTp06CYVCIUxMTMTAgQPFkiVLCi14a9SokZg5c6awtrYW+vr64osvvnjjMQrXrl0TnTp1EqampkJfX1/UrFlTLRApSPCWmZkppkyZIhwdHUWpUqWEvb29GDNmjHjx4kWe7czN28qsW7dO1KlTR8jlcmFubi7atm0rYmJi3igXHh4uAIh58+ap5efWn3eNQY7WrVsLANKXqBBCtGnTRshkMpGcnKxWds2aNcLFxUUoFAphZGQkWrdunWs7c+QEDvPnzxf16tUTcrlc1KxZU/zxxx9q5TIyMsSkSZOkMS5Xrpzw8/NTCwoLGrxt2rRJ2NvbCwMDA9GiRQsRHR39Rrv+HbwJIcQff/wh6tSpI3R1dYWFhYXw8fERt27dUitz/fp18eWXXwqFQiGsra3FjBkzxOeff672dxAXFydatmwp9PX1hZWVlRgzZoyYMmXKG7/3oaGholq1akJXV1c4OjqKKVOmiIyMDCHEq8eANG/eXOjq6qo91uXf7ty5I7p27SpMTEyEsbGx8PLyEufPn5f2z5s3Tzg6Ogq5XC5sbW1Fz549xaZNm9RWTF+4cEFUrlxZlCpVSlpd/fTpUzF8+HBRrlw5oaurKxwcHERAQIDa735aWpoYPny4sLS0FHp6esLb21uMHz9eAFD7/N41ru/6G5ozZ47Q1tYWd+/ezXMcSDPJhOBbdYmIPhVRUVFo0qQJ9u/fn+fDiwtbYGAgJk6cyJesfwApKSnYsWMHWrVqJa0MB4AffvgBy5Yte69L3nlp3bo19PT03rhES5qPCxaIiIg+EAMDAwwdOhS1a9fG999/D4VCgWPHjmHu3LkICAgolHNMnjwZV65cwa5du966wII0F4M3IiKiD0RPTw/79u3D2LFj4evri2fPnqFChQoIDg7GoEGDCuUcW7ZswbVr1zBt2rR8r8AlzcLLpkREREQahI8KISIiItIgDN6IiIiINAiDNyIiIiINwgULGiY7Oxt3796FkZHRO19sTURERJpDCIEnT56gTJky0NLKe36NwZuGuXv3LsqXL/+xm0FERERFJDExEeXKlctzP4M3DZPzEurExETpXYBERESk+VJTU1G+fHnpuz4vDN40TM6lUmNjYwZvRERExdC7boti8KahlMqP3QIiIqKS51N4Oi5XmxIRERFpkGIRvNnb20Mmk0Emk0FLSwsKhQKNGjXCrl27PnbTiIiIiApVsQjeAGDWrFlISkrC7du3cfz4cTRq1Aje3t7Yu3fvx24aERERUaEpNve8KZVKWFtbAwDKlCmDX3/9FUlJSRg+fDguXLjwkVtHREREVDiKzcxbbvr374+LFy/i2rVrkMlkGD9+PCwsLNC2bVsAwKFDh1C3bl3o6+ujRo0aCA8Pl469desWvLy8oFAoYGlpiSFDhiAjIwMAcP78eTRs2BAGBgYoW7YsJk2aJB2Xnp6OYcOGwcLCAhYWFvDx8UFKSoq0f86cObCzs4Oenh7q1q2Lw4cPf6DRICIiouKgWAdvVatWBQD89ddfAICIiAgcOXIEP//8M5KTk/HFF1/A19cXFy5cwOjRo+Hr64tDhw4BAIYMGQKFQoHo6Ghs3rwZf/zxBxYvXgwA6NGjB2rXro3Y2FgsXboUv/zyC7Zv3w4AGDNmDE6dOoXt27dj//79UKlU6NSpEwDg3Llz+OGHHzB//nxcvnwZ7u7u6NSpE7Kzsz/00BAREZGGKjaXTXOj/Od5Gk+ePAEADBgwAM7OzgCAcePGoXnz5hg8eDAAwMnJCefOncOsWbPg7u6OhIQEuLi4wM7ODk5OTti+fTtMTU0BAAkJCWjXrh3s7Ozg4OCAvXv3wsHBAc+fP0dISAhOnz6NGjVqAABWrVoFc3NzXLhwAQkJCZDJZLCzs4O9vT2mTJmCL774AtnZ2Xm+BiM9PR3p6enSdmpqatEMFhEREWmEYj3zlhPo5DzM1t7eXtp36dIlREREQKFQSCkkJARxcXEAgFGjRiEsLAylS5dGly5dcPPmTen4MWPGYMqUKbCxsUGfPn2Qnp4Oa2tr3LhxAy9fvkSDBg2kOsuVK4fs7GzExcWhZcuWqFGjBmrUqAEXFxcEBwejSpUq0NHJO4YOCgqCUqmUEl+NRUREVLIV6+AtJiYGAFC9enUAgJ6enrQvMzMTPj4+iI6OllJsbCy2bt0KAOjWrRtu3bqFn3/+GU+ePEHHjh0xduxYAMDo0aNx/fp1jB49Gjdu3EDTpk2xZMkSZGZmAgAOHz6sVu/Vq1fh5eUFAwMDnDhxApGRkfD09MTy5cvh4uKCO3fu5NmHgIAAqFQqKSUmJhbJWBEREZFmKNbB27Jly1CnTh04ODi8sc/Z2RlXr16Fk5OTlP7880+EhYUBAH766Sfcu3cPAwcOxNatWzFlyhSEh4cjLS0Nw4YNg66uLvz9/bF//370798f4eHhqFChArS1tfHw4UOpTmNjYwwfPhz37t3DsWPHEBQUhCZNmmDGjBm4cuUK0tLS3rpoQS6XS6/C4iuxiIiIqNgEbyqVCsnJyUhKSsKFCxfw/fff4/fff8f06dNzLf/dd9/h9OnTGDt2LK5evYo1a9ZgzJgxsLOzAwBcvnwZgwcPRkxMDGJjY7F9+3bUrl0benp6OHz4MIYMGYIrV67g9OnTOHjwIGrXrg0jIyP069cPfn5+iIqKwl9//YUePXrg2rVrcHBwgL6+PiZOnIglS5YgISEBv//+O54+fYrPPvvsQw4VERERaTJRDNjZ2QkAAoCQyWTC0tJStGrVShw6dEgqA0Ds379f7bg9e/YIFxcXoaurKxwcHMTcuXOlfffu3RMdOnQQJiYmQqFQiM6dO4u///5bCCHE1atXhZeXlzAyMhKmpqZiwIAB4vnz50IIIZ49eyb8/PyEmZmZMDIyEt7e3uLGjRtSvatWrRKVKlUScrlcVKpUSaxdu7ZAfVWpVP/0VSVevWGNiYmJiYmJ6UOlopTzHa9Sqd5aTiaEEB81eqQCSU1N/WcVrQoAL6ESERF9SEUZNeV8x6tUqrfeJlVsLpsSERERlQQM3oiIiIg0SLF+SG9xplIBXHhKRERU8nDmjYiIiEiDMHgjIiIi0iAM3oiIiIg0CIM3IiIiIg3CBQsaSqn82C0gIqK88AmqVJQ480ZERESkQRi8EREREWkQBm+5kMlkiIqKUsvbuXMnSpUqhcDAwHceHxUVBZlMJm1HR0fj6NGjhdxKIiIiKokYvOXDiRMn0LFjRwwaNChfwVvDhg2RlJQkbX/99deIi4srwhYSERFRScHg7R2uXLkCb29vfPPNN5g5c2a+jtHV1YW1tbW0LXjnKhERERUSBm9vcffuXbRs2RKenp5YvHix2qVQmUyG1atXo3r16pDL5XB3d0d8fDwA9cumnp6euHnzJnr16gVfX18AwJgxY2BjYwN9fX14enoiNjb2g/eNiIiINBODtzw8fvwYLVu2REpKClasWAFtbe03ykyYMAFz5szBmTNn8ODBA4wdO/aNMhs3bkS5cuUwa9YszJ49G5s2bcKiRYuwYcMGXLx4EdbW1ujVq1ee7UhPT0dqaqpaIiIiopKLwVseBg4cCD09PWRlZWH69Om5lvH390fTpk1RvXp1+Pn54dSpU2+UMTMzg7a2NpRKJZRKJRISEqCrqwtbW1tUqFABc+fOxYwZM/JsR1BQkHSsUqlE+fLlC62PREREpHkYvOXB0tISu3fvxqRJkzBlyhTExMS8UaZixYrSz8bGxsjIyHhnvV26dIG+vj4cHBzg5uaGFStWoFq1anmWDwgIgEqlklJiYuL7dYiIiIiKBQZveZgxYwZMTU3x/fff47PPPoOvry8yMzPVyujq6qpt52dhgrW1NS5fvowtW7agRo0amDZtGurXr4/nz5/nWl4ul8PY2FgtERERUcnF4C0POjqv3hymra2NJUuW4MKFCwgKCnqvul5f6LBt2zYsWbIE3t7eWLBgAc6fP4+4uDhcuHChUNpNRERExRuDt3yoVasW/P39MXny5PcKsgwNDXH58mWkpKQgOzsbI0eOxKZNm5CQkIDly5fDwMAAlSpVKoKWExERUXHDF9PnU2BgIMLDw+Hr64sTJ04U6NjvvvsOo0aNQlxcHDZu3IhJkyZh+PDhSE5ORuXKlfHnn3/C1NS0iFpORERExYlM8AmyGiU1NRVKpRKACgDvfyMi+hTxm5XeR853vEqleus97rxsSkRERKRBGLwRERERaRDe86ahVCqATw0hIiIqeTjzRkRERKRBGLwRERERaRAGb0REREQahPe8aSil8mO3gIjywsdEEFFR4swbERERkQZh8EZERESkQYpV8CaTydRS6dKl0a9fPzx9+lQqY29vj9DQ0I/XSCIiIqL/oFgFbwAQHh6OpKQk3L59GxERETh58iR++OGHj90sIiIiokJR7II3MzMzWFtbo2zZsqhfvz4CAgKwbt26j90sIiIiokJR7IK3fzM0NMxzX3Z2NqZNmwZHR0fo6+ujSZMmuHDhgrRfJpMhKipK2g4NDYW9vb20PWbMGNjY2EBfXx+enp6IjY2V9h06dAh169aFvr4+atSogfDwcGnfrVu34OXlBYVCAUtLSwwZMgQZGRmF02EiIiIq1op18PbgwQPMmTMHPj4+ue6fNGkSgoODMWvWLJw9exZ2dnZo1aoVnj179s66N23ahEWLFmHDhg24ePEirK2t0atXLwBAcnIyvvjiC/j6+uLChQsYPXo0fH19cejQIQDAkCFDoFAoEB0djc2bN+OPP/7A4sWLcz1Peno6UlNT1RIRERGVXMXuOW+tW7eGtrY2hBB4/vw5zM3NsWDBgjfKCSEwd+5cBAUFoW3btgCAxYsXo0KFCli9ejUGDBjw1vMkJCRAV1cXtra2sLW1xdy5c3HlyhUAwLx589C8eXMMHjwYAODk5IRz585h1qxZcHd3R0JCAlxcXGBnZwcnJyds374dpqamuZ4nKCgIEydO/C9DQkRERMVIsZt5W7JkCaKjoxEdHY2jR4+iZcuWaNSoEe7fv69W7v79+0hJScHnn38u5ZUqVQp169bFpUuX3nmeLl26QF9fHw4ODnBzc8OKFStQrVo1AMClS5cQEREBhUIhpZCQEMTFxQEARo0ahbCwMJQuXRpdunTBzZs31S7Hvi4gIAAqlUpKiYmJ7zkyREREVBwUu+CtbNmycHJyQsWKFdGgQQMsX74cz58/x/r169XK6enp5Xp8VlYWsrKyct2XmZkp/WxtbY3Lly9jy5YtqFGjBqZNm4b69evj+fPnyMzMhI+PjxRERkdHIzY2Flu3bgUAdOvWDbdu3cLPP/+MJ0+eoGPHjhg7dmyu55TL5TA2NlZLREREVHIVu+Dt37S0tJCdnf1GQKZUKmFlZYXjx49LeRkZGThz5gycnZ0BALq6unjy5Im0/8aNG9LP27Ztw5IlS+Dt7Y0FCxbg/PnziIuLw4ULF+Ds7IyrV6/CyclJSn/++SfCwsIAAD/99BPu3buHgQMHYuvWrZgyZYraggYiIiKivBS74C0lJQXJyclITk7G1atXMWjQIGRlZUn3tb3O398f48ePR0REBC5duoR+/fohLS0NnTt3BgC4urpi7ty5uHr1KrZs2YLly5dLx2ZnZ2PkyJHYtGkTEhISsHz5chgYGKBSpUr47rvvcPr0aYwdOxZXr17FmjVrMGbMGNjZ2QEALl++jMGDByMmJgaxsbHYvn07ateu/WEGiIiIiDSbKEYAqCUDAwPh4eEhIiMjpTJ2dnZi+fLlQgghMjMzxU8//SSsrKyEvr6+aNasmbhw4YJU9uzZs8LFxUXo6uqKRo0aiUWLFgk7Oztpf3BwsLCzsxNyuVzUrFlT7NmzR9q3Z88e6VgHBwcxd+5cad+9e/dEhw4dhImJiVAoFKJz587i77//zlcfVSrVP/1TiVevv2ZiYvrUEhHR+8j5jlepVG8tJxNCiI8ZPFLBpKamQqlUAlAB4P1vRJ8i/leViN5Hzne8SqV66z3uxe6yKREREVFxxuCNiIiISIMUu4f0lhQqFcCnhhAREZU8nHkjIiIi0iAM3oiIiIg0CIM3IiIiIg3Ce940lFL5sVtA+cXHRhARUWHizBsRERGRBmHwRkRERKRBNCp4y8jIQGBgIBwdHSGXy2Frawt/f3+1l8d/CBs2bMD9+/c/6DmJiIiIAA0L3kaPHo3w8HAsXrwYV65cwfLly7F792507dr1g7Xh5s2b+Oabb/D8+fMPdk4iIiKiHBq1YCE0NBTLli1Ds2bNAAD29vZYuHAh3N3dkZSUBBsbmyJvA18FS0RERB+TRs28aWlpITIyEtnZ2VJegwYNEBsbiylTpqBjx45S/tSpUyGXy5GWlgYAiIuLg56eHp49e4b09HQMGzYMFhYWsLCwgI+PD1JSUqRjjxw5Ajc3NxgYGMDQ0BBt2rRBUlISAMDBwUH6NzQ0FACwadMmVK1aFQYGBqhXrx4OHDgg1eXp6YkhQ4bA0dERtra2ePLkCebMmQM7Ozvo6emhbt26OHz4cJGNGRERERUvGhW8DRs2DHPnzoW9vT38/PwQHh6OFy9eoGrVqvD29sbBgwelmbEDBw4gIyMDp0+fBgDs2bMHbm5uMDQ0xJgxY3Dq1Cls374d+/fvh0qlQqdOnQAAKpUK3t7e8PLyQmxsLHbv3o1r164hKCgIAHDy5Enp386dO+P8+fPo2bMnxo4di5iYGPj4+KB169a4du2a1O7ly5dj9erV2LRpE65du4YffvgB8+fPx+XLl+Hu7o5OnTqpBaSvS09PR2pqqloiIiKiEkxomNWrV4uGDRsKLS0tAUAYGRmJZcuWiWfPngldXV1x4cIFkZGRIYyMjESrVq3Ezz//LIQQ4quvvhLTpk2TysXExEh1Pnr0SGhpaYmYmBiRlJQkgoODRXZ2trT/xx9/FE2bNhVCCBEfHy8AiPj4eCGEED4+PsLf31+tje3bt5fyGjduLDp37izt27hxo5DL5eLChQtCCCGePn0q9u7dKzIyMnLt74QJEwSAXJJKvHqCGNOnnoiIiPJDpVIJAEKlUr21nEbNvAFAt27dcOTIEdy/fx9hYWGoVq0a+vTpg0uXLsHNzQ1RUVE4e/Ys7Ozs0KZNGxw+fBhZWVmIiopCq1atcOPGDbx8+RINGjSAQqGAQqFAuXLlkJ2djbi4OFhbW6Nnz56YOXMmevTogbp16yI4OBhZWVm5tufSpUsICQmR6lIoFIiIiEBcXJxUxt7eXvq5ZcuWqFGjBmrUqAEXFxcEBwejSpUq0NHJ/fbDgIAAqFQqKSUmJhbqeBIREZFm0ZgFCzExMVixYgWmT58OADA3N0fXrl3RsWNHODk5ITIyEl5eXoiKikJaWhrc3Nzg5uaGwMBAnDp1CgqFAtWrV0d0dDQA4PDhw1AoFGrnsLKywp07d1C3bl3UqVMHLVq0QL9+/bBt2zYcP34813ZlZmZi9OjR6NGjh1q+vr6+9LOenp70s4GBAU6cOIEDBw4gIiICy5cvx4IFC3DmzBmULVv2jfrlcjnkcvl7jRkREREVPxoz85aZmYkZM2bg3Llzavm6urowMDBA6dKl0bJlSxw8eBCHDx+Gu7s7atasiczMTMyePRstW7YEAFSoUAHa2tp4+PAhnJyc4OTkBGNjYwwfPhz37t3Dpk2bYGZmhq1bt2LYsGFwd3fHjRs3pHvpZDKZ2vmdnZ0RHx8v1eXk5IRFixZhx44dufbj2LFjCAoKQpMmTTBjxgxcuXIFaWlpXLRARERE+aIxM28uLi7w9vZGu3bt8PPPP6Nhw4ZITk7GihUrkJaWhg4dOkChUEBLSws7duzAnDlzoKWlhYYNG2LdunVYv349AMDIyAj9+vWDn58fFi1aBEtLS/j7++PmzZtwcHCAubk5bt26hX379sHBwQEbNmxAeHg4XF1dAQCGhoYAgPPnz8PCwgLDhw+Hu7s7XF1d4e3tjYiICMyYMQORkZG59kNfXx8TJ06ElZUVmjdvjgMHDuDp06f47LPPPsxAEhERkWb7MLfgFY5nz56JMWPGiIoVKwq5XC7MzMxEly5dxM2bN6UyPj4+onz58tL21KlThba2tnj06JFaPX5+fsLMzEwYGRkJb29vcePGDSGEEJmZmWLgwIHCxMREmJqaCi8vLzFz5kxhbGws0tLSpHPo6uqKmTNnCiGEWLt2rXB2dha6urqiSpUqYv369dK5GjduLCZMmKDWj1WrVolKlSoJuVwuKlWqJNauXZvvMci5mZELFjQnERER5Ud+FyzIhPjneiBphNTUVCiVSgAqAMYfuzmUD/wLIyKi/Mj5jlepVDA2zvs7XmPueSMiIiIiBm9EREREGkVjFiyQOpUKeMuMKhERERVTnHkjIiIi0iAM3oiIiIg0CIM3IiIiIg3Ce940lFL5sVtA78JHhBARUVHgzBsRERGRBmHwRkRERKRBGLwBsLe3h0wmk1KpUqVQuXJlzJo1q1Dqj4yMxKVLlwqlLiIiIirZGLz9Y9asWUhKSkJSUhJu3LiBgIAAjBw5EitXrvzPdTdr1gz37t0rhFYSERFRScfg7R9KpRLW1tawtrZG+fLl0bNnTzRv3hwbN2782E0jIiIikjB4ewsdHR3o6uoiNTUVvXv3hqWlJXR1dVG5cmVs3rxZKieTybB69WpUr14dcrkc7u7uiI+PB/DqkiwANGnSBIGBgcjIyEC/fv1gYWEBhUKBtm3b4s6dOx+hd0RERKSJGLzlIiMjAxs3bsTu3bvRrl07DBs2DFeuXMHu3bsRGxsLd3d39O3bFy9fvpSOmTBhAubMmYMzZ87gwYMHGDt2LADg1KlTAIDw8HCMHDkSISEhOHDgAHbv3o3Tp0/jyZMnGD58eJ5tSU9PR2pqqloiIiKikovPefvHwIEDMXjwYADAixcvYGBggOHDh6Nbt27IyMjAiBEjUL16dQDAyJEjsWTJEty7dw/ly5cHAPj7+6Np06YAAD8/P4SEhAAASpcuDQAwMzODQqFAQkIC9PX1YW9vDzMzM4SGhuLhw4d5tisoKAgTJ04ssn4TERGRZuHM2z8mTZqE6OhoREdH4+bNm3j8+DF++eUXAECPHj0QFxeHoUOHwsvLCw0bNgQAZGVlScdXrFhR+tnY2BgZGRm5nqd///5ISkqCtbU1vLy8sH37dlSpUiXPdgUEBEClUkkpMTGxMLpLREREGorB2z8sLS3h5OQEJycnlCtXDtra2tK+Hj16YOTIkTA1NYWfnx+2bdv2xvG6urpq2yKPx+tXq1YNCQkJCAsLg42NDQICAuDl5ZVneblcDmNjY7VEREREJRcvm75Damoq1qxZgxMnTsDV1RUAsH37dgB5B2hvs3LlSsjlcnTu3BmdOnXC8ePH0aBBA9y/fx9WVlaF2nYiIiIqfhi8vYOenh4MDQ0RHh6O0qVL48qVK9K9cenp6fmqw9DQEBcvXkTt2rWhUqkwdepUWFhYwNHREWFhYShXrhwsLCyKshtERERUTDB4ewddXV2sXr0aI0aMwJw5c+Dg4ICxY8di7NixOHfuHCpXrvzOOoYOHYoffvgB169fx/Tp03H79m10794dKSkpqFu3LrZs2aJ2mZaIiIgoLzLxPtf+6KNJTU2FUqkEoALA+98+ZfzLIiKigsj5jlepVG+9x50LFoiIiIg0CIM3IiIiIg3Ce940lEoF8KkhREREJQ9n3oiIiIg0CIM3IiIiIg3C4I2IiIhIg/CeNw2lVH7sFhQ/fLQHERFpAs68EREREWkQBm9EREREGoTB2z9CQ0Nhb2//sZtBRERE9FYM3oiIiIg0CIM3IiIiIg3C4C0XiYmJaNu2LQwMDGBvb4+JEyciKytL2r9kyRJUrlwZurq6sLCwwKBBg6T9vr6+8Pf3R+fOnWFgYIDy5ctj1apV0rGRkZGoVasW9PT04OjoiN9+++2D94+IiIg0F4O3fxFCoH379rC0tMS5c+cQGhqKNWvW4H//+x8A4MCBAxg6dCj+97//IS4uDgsXLsTSpUvx559/SnWEhISgTp06uHjxIjp06IABAwZApVIhKysLnTp1QqdOnXD58mVMnjwZ3333Hf7666+P1V0iIiLSMAze/iUyMhI3b97EokWL4OzsDE9PTwQHB2PWrFkAAIVCgaVLl6J9+/awt7dHx44dUbt2bcTGxkp11KxZE6NGjYKjoyMmTZqEFy9eIDY2FiqVCikpKbCysoK9vT26deuGvXv3wsbGJs/2pKenIzU1VS0RERFRycXg7V8uXbqEhw8fwtjYGAqFAgqFAp07d0ZKSgoePnyIOnXqoGbNmpgwYQI6duwIZ2dnnDhxQu2yasWKFaWfjf95e3xGRgbMzMzg5+eHfv36wc7ODoMHD4ZSqYSpqWme7QkKCoJSqZRS+fLli67zRERE9MkrscFbcnIy4uLipG0hBHR0dJCZmYnKlSsjOjpaSjExMbh69SqUSiV27dqFOnXqIDk5Ga1bt8Yff/yBRo0aqdWtq6v7xvnEP4/vnz9/Pi5evIj+/fvjxIkT+Pzzz7Fjx4482xkQEACVSiWlxMTEQhoBIiIi0kQlNngLDg6Gv7+/tK1SqWBhYQFnZ2fcunULpUuXhpOTE5ycnBAfH48JEyZAJpNh8eLF6N27N3777Tf06dMHVapUwfXr16Xg7G2Sk5MxaNAgODk54aeffsKpU6fQrFkzbNmyJc9j5HI5jI2N1RIRERGVXCU2ePPw8EBkZCT27t2LmJgYzJs3Dy1atICXlxfs7Ozg4+ODCxcu4NChQ+jfvz8MDAygra0Nc3NzHD16FBcuXEBsbCx8fX2RlJSE9PT0d57TzMwMGzduxPDhw3H9+nUcPHgQ0dHRqF279gfoMRERERUHJTZ4a9u2LUaMGIHu3bvDzc0Nbm5uCAgIgLa2NrZs2YLs7Gx8/vnn6NChA9q0aYM5c+YAAAIDA2FpaYn69eujRYsW0NPTg5+fH86dO/fOc+rq6mLLli2Ijo7GZ599hm+++QZ9+vRB3759i7q7REREVEzIRH6u99EnIzU1FUqlEoAKAC+hFib+JRAR0ceU8x2vUqneeptUiZ15IyIiItJEDN6IiIiINIjOx24AvR+VCuDCUyIiopKHM29EREREGoTBGxEREZEGYfBGREREpEF4z5uGUio/dguKBz4ehIiINA1n3oiIiIg0CIM3IiIiIg3yUYM3e3t7yGSyN5KbmxsAwNPTE4GBgR+ziURERESflI9+z9usWbPQuXNntTxdXV0AwMaNG6WfiYiIiOgTCN6USiWsra1z3WdmZvaBW0NERET0afuk73l7/bKpr68v/P390blzZxgYGKB8+fJYtWqVVDY9PR3Dhg2DhYUFLCws4OPjg5SUlDzrPnPmDOrXrw99fX00bNgQ48ePh6enJwBACIH//e9/cHBwgK6uLsqUKYOJEyfm2i4ASEhIgEwmQ0JCAgDg4cOHaN++PRQKBRwdHbFw4ULIZDKp/MWLF9GkSRPo6+vD2dkZ8+fP/++DRURERCXCJx28/VtISAjq1KmDixcvokOHDhgwYABUKhUAYMyYMTh16hS2b9+O/fv3Q6VSoVOnTrnWo1Kp0KpVK9SpUwfR0dHo2rUrgoKCpP0rV67ErFmzsGTJEsTFxWH8+PEIDAzE2bNn89XOb7/9Fn///TeOHDmCkJAQtcDvxYsXaN26Ndzc3BATE4Pg4GBMmjRJLRAlIiIiypP4iOzs7IRcLheGhoZq6enTp0IIIRo3biwmTJgghBCiZ8+eom7dutKxKpVKABBHjhwRz549E7q6uiImJkba/+jRI6GlpaWWl+O3334T9vb2IjMzU8r79ttvRePGjYUQQkRGRoqtW7eqHWNtbS1Wrlz5RruEECI+Pl4AEPHx8eLKlSsCgLh+/bq0f+HChSJnqJcsWSJcXFzU6p4zZ84beTnS0tKESqWSUmJiogAgAJV49ZQypv+SiIiIPhU5sY1KpXpruY9+z9ukSZPQvn17tTwDA4Ncy1asWFH62fift7JnZGTgxo0bePnyJRo0aKBWPjs7G3FxcahRo4ZafkxMDFxcXKCtrS3lNWjQABs3bgQANGnSBCdOnEBAQAAuXbqEc+fOITk5GVlZWe/sT0xMDMzMzODo6KhWd45Lly7h/PnzUCgUUl5WVhZ0dHL/KIKCgtRm7oiIiKhkK3DwZmdnhy5duqBz586oXbv2f26ApaUlnJyc8lU2t5WnQghkZmYCAA4fPqwWFAGAlZXVG8fo6OhA/OvR+q9vL1myBMOHD0ffvn3RoUMHBAcHo0mTJtL+1+9fAyCdPz91Z2ZmolmzZpg3b16e/XxdQEAA/P39pe3U1FSUL18+X8cSERFR8VPge95mzJiBhIQEeHh4oHLlypgwYQIuXbpUFG3LtwoVKkBbWxsPHz6Ek5MTnJycYGxsjOHDh+PevXtvlK9WrRqio6ORnZ0t5Z05c0b6eeHChRg/fjxmzpyJ7t27w8LCAvfu3ZOCMF1dXTx58kQqf+PGDennqlWr4tGjR4iPj8+1bmdnZ8TFxcHBwUFq6/HjxzF37txc+yaXy2FsbKyWiIiIqOQqcPDWoUMH/P7777h//z4mT56MK1euwM3NDTVr1sTPP/8srbj8kIyMjNCvXz/4+fkhKioKf/31F3r06IFr167BwcHhjfJdunRBamoq/P39ERcXh8WLF+P333+XZtTMzc2xd+9exMXF4cyZM+jcuTMyMjKQnp4OAHB1dcX69etx6tQpnDp1CuPHj5fqrlSpElq2bInevXsjJiYGe/bsUdvv4+OD58+fY8CAAbh8+TK2b9+OoUOHwtLSsohHiYiIiIqD915tqq+vjw4dOqB///7o2rUrrl69ipkzZ6JatWrw8vJCXFxcYbbznaZPn47mzZujQ4cOqF+/PnR0dLB9+3a1+9pyKBQKRERE4MCBA6hRowZWrFiBbt26SZdlZ8+ejdTUVNSsWRPt27dHzZo18fXXX+PcuXMAAH9/f7i4uMDDwwNdunTBuHHj1Opfvnw5DA0N8fnnn8PPzw+9evWS6jYyMsKOHTsQFxeHWrVqoV+/fhg8eDACAgKKeISIiIioOJCJf9+g9Q7Z2dmIjIzEhg0bsHnzZmRmZqJ9+/b49ttv0aRJEzx9+hQDBw7E2bNncfny5aJq938SHx+PO3fuSK/hAoBBgwbh2bNnCA0N/U91P3/+HHv37kXr1q1RqlQpAMCGDRvwww8/FMqsZGpqKpRKJQAVAF5C/a8K9ttPRERUdHK+41Uq1VtvkyrwggVLS0ukpaXhiy++wKJFi9C6dWu1hQTGxsZo3749Tpw48X4t/wBUKhWaN2+O1atXw9XVFWfOnMGqVauwdu3a/1y3np4eevfuDT8/P/Tu3RvJycmYOHFins+cIyIiIiqIAs+8TZo0Cf7+/m+s6tQ0S5YswS+//ILExETY2tpi1KhR6Nu3b6HUffjwYYwcORIxMTEwNjaGj48Ppk6dCrlc/p/r5sxb4eLMGxERfSryO/NW4ODNwsIC+/fvf+PZafRh5PeDJSIiIs2S3+/4Ai9YqFatGo4fP/6fGkdERERE76fA97yZmZlh4MCBmDBhAuzt7aGnp6e2PzIystAaR0RERETqChy81apVC7Vq1SqCphARERHRuxT4nrfXpaamIisrC6ampoXZJnoLLlh4Oy5AICIiTVVk97wBrx5iW7ZsWZiamsLCwgLW1taYNGnSezeWiIiIiPKnwJdNJ0+ejLlz52Ly5Mlo2LAhsrKycPToUQQGBkJXVxc//vhjUbSTiIiIiPAeM2+LFi3C0qVLMWDAANSoUQO1atXCd999h8WLF2PBggVF0cYPIiMjA4GBgXB0dIRcLoetrS38/f2lF9Db29vn+fYFmUyGqKioPOuOjIzEpUuXiqDVREREVNIUOHhLTU1FpUqV3sh3dnbG33//XSiN+hhGjx6N8PBwLF68GFeuXMHy5cuxe/dudO3a9Z3HJiUloWHDhnnub9asGe7du1eYzSUiIqISqsCXTRs2bIjg4GD89ttv0NJ6FftlZWUhODgY9erVK/QGfiihoaFYtmwZmjVrBuDVTNvChQvh7u6OpKSktx5rbW39IZpIREREVPCZtxkzZmDz5s1wdHREhw4d0KFDB1SoUAHbtm3D7Nmzi6KNH4SWlhYiIyORnZ0t5TVo0ACxsbGwsLBQK3vixAkoFAosW7YMgPplU3t7e4wePRo2NjaoXbs27OzsAABNmjRBYGAgMjIy0K9fP1hYWEChUKBt27a4c+fOh+kkERERabwCB29VqlTB5cuX4e/vDysrK9jZ2SEgIABXr15FzZo1i6KNH8SwYcMwd+5c2Nvbw8/PD+Hh4Xjx4gWqVq2KUqVKSeXi4uLwxRdfYOLEiejdu3eudYWFhWH37t0IDQ3F6dOnAQDh4eEYOXIkQkJCcODAAezevRunT5/GkydPMHz48A/SRyIiItJ8Bb5sCgDm5uYYOnRoYbfloxo3bhwcHR0xf/58LFq0CAsXLoSRkRFmz56NXr16AQDu3buHVq1aoV+/fhgxYkSedfn4+Lzx7lczMzMoFAokJCRAX18f9vb2MDMzQ2hoKB4+fJhnXenp6UhPT5e2U1NT/2NPiYiISJMVeOZNS0sL2trauSZ9fX04Ojrixx9/REZGRlG0t0h169YNR44cwf379xEWFoZq1aqhT58+OHPmDABg/PjxSEhIQPny5d9aj729fZ77+vfvj6SkJFhbW8PLywvbt29HlSpV8iwfFBQEpVIppXedm4iIiIq3AgdvCxYsgJWVFRYsWIDo6GicO3cOixcvRpkyZTBq1Cj8+uuv2LZtG8aOHVsU7S0SMTExajNp5ubm6Nq1Kw4cOIBy5cpJ72v19vbGrFmz8NNPP711Ze2/3/f6umrVqiEhIQFhYWGwsbFBQEAAvLy8kNeLLgICAqBSqaSUmJj4nr0kIiKi4qDAl02nTZuGZcuWoVWrVlLeZ599hvLly2PQoEGYOHEiypYtiw4dOuCXX34p1MYWlczMTMyYMQM+Pj6oXbu2lK+rqwsDAwOULl0aANC2bVt0794dS5YswejRo6UFCwWxcuVKyOVydO7cGZ06dcLx48fRoEED3L9/H1ZWVm+Ul8vlkMvl7985IiIiKlYKPPN27949lCtX7o18a2tradWkjY2NRt2b5eLiAm9vb7Rr1w5r1qxBQkICjh8/Dj8/P6SlpaFDhw5SWW1tbcyZMwehoaE4duxYvuo3NDTExYsXpdmzYcOGYd++fYiPj0dYWBjKlSv3xopWIiIiotwUeOatRYsWGDRoEFauXCk9BuPmzZsYNmwYmjVrhqysLCxbtuyNG/Y/devXr8fUqVMRGBiIW7duwdDQEC1btsTBgwdhZGSkVtbT0xMdOnTAoEGDcOrUqXfWPXToUPzwww+4fv06pk+fjtu3b6N79+5ISUlB3bp1sWXLFmhraxdV14iIiKgYkYm8brbKQ0pKCjp37ox9+/bB3NwcQgg8evQILVu2xLJly3Dq1Cn07t0bf/7551vfOkDvJzU1FUqlEoAKgPHHbs4np2C/zURERJ+OnO94lUoFY+O8v+MLHLzluHr1KmJiYqCjo4OqVauiYsWKAIAXL15AT08PMpns/VpOb8Xg7e0YvBERkabKb/BW4HvegFevw4qLi8OtW7fQuHFjpKSkQKVSAQD09fUZuBEREREVkQLf85aYmAgvLy+kpKQgJSUF7dq1w6+//oqjR49i9+7dGnevm6ZSqYC3BOVERERUTBV45m3w4MFwd3fH3bt3peeZ/f7772jRokWxe+sCERER0aemwDNvBw8exIkTJ9RWR5YqVQrjxo2Di4tLoTaOiIiIiNQVeObNwMAA9+7deyP/ypUrb725joiIiIj+uwIHbwMHDsSAAQOwbds2CCEQFxeH5cuXo1+/fujTp09RtJGIiIiI/lHgy6bjxo2DiYkJ/Pz88Pz5c7Rp0waWlpbw9/fHyJEji6KNlAul8mO3oHDxER9ERET5U+DnvN26dQvlypWDlpYWnj17hszMTCiVSmRlZeH8+fO8762IFdfnvDF4IyKikq7InvPm4OCAhw8fAnj1zk7lP1NA8fHxcHNze8/mEhEREVF+5Ct4W7JkCRwdHeHo6AghBOrWrStt5yRXV1dUrVq1qNv7XkJDQ2Fvb/+f6ggMDISnp+d7HXv//n1s2LDhP52fiIiICMjnPW89e/aErq4usrOz0bt3b4wYMUKacQMAmUwGQ0NDNG3atMgaqslGjx4NIQQ6der0sZtCREREGi5fwVupUqXQo0cPAK8umzZq1Ag6OgVe61BivefrY4mIiIjeUOB73jw8PLB161ZMnToVkyZNwqRJkzBx4kSMGTMGrVu3Loo2FqojR47Azc0NBgYGMDQ0RJs2bZCUlCTt37lzJ1xcXGBgYICaNWti3759b9SRlpYGNzc3eHl54eXLlwCA3377DQ4ODlAoFPD09MSFCxcAvLrcumLFCqxYsUK6dLtu3To4OztDT08PVatWxebNm4u830RERFQ8FDh4GzJkCLp06YJt27Zh0qRJ2Lt3LxYtWoRffvkFFSpUKIo2FhqVSgVvb294eXkhNjYWu3fvxrVr1xAUFAQAiI2NxZdffon27dvj/Pnz6NKlC9q1a4fk5GSpjuzsbHTp0gVZWVnYtGkTdHV1ERERgcDAQMydOxfnzp2Du7s7mjRpgkePHmHkyJH45ptv8M033+DUqVO4f/8+unfvjoCAAFy5cgW9e/dGly5dkJKSkmub09PTkZqaqpaIiIio5Crwtc9169YhLCwM7du3R+XKlbFgwQI4OzvD19dXmoX6VL148QLjxo2Dv78/ZDIZHBwc0KFDB5w8eRIAsHTpUjRq1Ahjx44FAPz444949uwZHj9+LNUxZMgQXL16FQcPHoShoSEA4Ndff8WYMWPwxRdfAAAmT56M7du3Y/Xq1RgyZAj09fUBAKVLl8a5c+eQkZGBcuXKwc7ODiNGjMBnn30mvSf234KCgjBx4sSiGhIiIiLSMAWeeUtNTUXdunUBADVq1MDJkyeho6ODgIAAbN++vdAbWJisra3Rs2dPzJw5Ez169EDdunURHByMrKwsAK9e8VWnTh21YyZPnozKlSsDAI4dO4YFCxbAyMgIpqamUplLly5h1KhRUCgUUjp//jzi4uLeaEOtWrXg7e2NFi1aoHLlyvjxxx/h4OAAAwODXNscEBAAlUolpcTExMIaDiIiItJABQ7eHB0dce7cOQBAtWrVpFkrIQRUKlXhtu49JScnqwVOQgjo6Ojgzp07qFGjBiIjI1GnTh3MnDkTI0aMkMqVKlXqrfUaGRkhKioKsbGxWLp0qZSfmZmJWbNmITo6WkqXL1/GuHHj3qhDJpNh69atOHHiBDp27IiIiAi4uLggOjo613PK5XIYGxurJSIiIiq5CnzZdMSIEejatSuWLVuGzp07o06dOtDR0cHRo0c/mYf0BgcH4/Lly9i6dSuAV/e6WVhYYNOmTTAzM5PyAWDu3LnSatCKFStKgWmOhg0bYujQoQCA6tWrw8PDA2PHjkVAQADat28PMzMzODs74/bt23BycpKO69WrF77++mu0bdsWMplMOsfly5exZMkSBAcHo169epg8eTKqVauGXbt2oVatWkU5LERERFQMFHjmrW/fvtixYwcqVaqEKlWqYNOmTUhOToarqyuWL19eFG0sMA8PD0RGRmLv3r2IiYnBvHnz0KJFC5ibm+PWrVvYt28fbty4gV9++QXh4eFIT08HAAwcOBCHDh3CjBkzpIUMsbGx8PDwUKv/+++/h6mpKcaMGQMA8Pf3x6xZs7Bq1Spcv34do0ePxvr161GlShUAr95EkZCQgDt37sDExAQLFizAlClTEB8fj23btiEhIQG1a9f+sINEREREmkm8h5iYGHHixAlpe9q0aeL8+fPvU1WRGTt2rLC2thZGRkbC19dXPHv2TGRmZoqBAwcKExMTYWpqKry8vMTMmTOFsbGxSEtLE0IIERERIapVqybkcrlwcXERBw4cEEIIMWHCBNG4cWOp/q1btwotLS1x6tQpIYQQs2fPFnZ2dkJPT0/UqVNH7Nu3Typ7/PhxYW1tLczNzUV2drbYuXOnqFmzptDT0xO2trZi1qxZ+e6XSqUSAASgEq/eCFo8EhERUUmX8x2vUqneWq7AL6Zft24dfH19ERQUhO+//x4A8O2332LLli1Yu3Yt2rVrV/gRJkn4YnoiIqLiKb8vpi9w8Fa5cmUEBASgZ8+eavmhoaGYNm0aYmNj36/FlC8M3oiIiIqn/AZvBb7nLTExEQ0bNnwj383NDTdu3ChodfSeVKqPfaGzcBMRERHlT4GDNxcXF4SEhLyRv3jxYtSsWbNQGkVEREREuSvwo0JmzJgBLy8vbNu2TVohGRMTg4cPH2Lbtm2F3kAiIiIi+n8FDt5cXV0RFxeH33//HVeuXEGpUqXQpEkTdOvW7Z97sYiIiIioqBR4wQJ9XPm9mZGIiIg0S36/4ws880afhuI0ycn/fSAiIsq/Ai9YICIiIqKPh8EbERERkQYpcPDWtGlTPH78+I38v//+G3Xr1i2MNhUKmUyGqKioj90MIiIiokKVr3vedu7ciZMnTwIADhw4gP/9739QKBRqZa5evYqEhIRCbyARERER/b98BW/Ozs749ddfIYSAEAJHjhyBrq6utF8mk8HQ0BBLly4tsoYSERERUT4vmzo4OCAyMhL79+9Hz549sXPnTuzfv19KkZGRiIiI0KiX0m/duhUuLi7Q19dH1apVsXHjRmmfp6cnAgMDpe2EhATIZDJpZlEmk2H16tWoXr065HI53N3dER8fL5U/c+YM6tevD319fTRs2BDjx4+Hp6entH/Tpk2oWrUqDAwMUK9ePRw4cKCou0tERETFRIHveVu4cCEmT56MefPmSXl16tTBjz/+iIyMjEJtXFGJjIxE+/bt0aNHD5w/fx59+/ZF586dcebMmXzXMWHCBMyZMwdnzpzBgwcPMHbsWACASqVCq1atUKdOHURHR6Nr164ICgqSjjt//jx69uyJsWPHIiYmBj4+PmjdujWuXbuW63nS09ORmpqqloiIiKjkKnDwNmzYMGzbtg21atWS8saPH4+tW7di5MiRhdm2IhMSEoKOHTvi+++/R6VKleDv748OHTogODg433X4+/ujadOmqF69Ovz8/HDq1CkAwLp166BQKDBnzhw4Oztj8ODB6Nixo3RccHAw+vXrh65du8LJyQlDhw5F69atsWDBglzPExQUBKVSKaXy5cv/t84TERGRRitw8BYeHo6wsDA0atRIymvXrh1CQ0Px+++/F2rjisqlS5fw+eefq+U1bNgQly5dyncdFStWlH42NjaWZh1jYmLg4uICbW1taX+DBg3Uzh0SEgKFQiGliIgIxMXF5XqegIAAqFQqKSUmJua7jURERFT8FPgNC0IIpKWl5Zr/8uXLQmlUUdPT03sjLysrC1lZWQBe3dP2uszMzDfKv75gA3jVfwDQ0dHBv9849vp2ZmYmRo8ejR49eqiV0dfXz7Wtcrkccrk8r64QERFRCVPgmbcOHTqgX79+OHToEJ49e4Znz57h6NGj8PPzQ/v27YuijYXO2dkZx48fV8s7duwYnJ2dAbwKzJ48eSLtu3HjRr7rrlatGqKjo5GdnS3lvX4vnbOzM+Lj4+Hk5CSlRYsWYceOHe/bHSIiIipBCjzzNnPmTPTp0wdNmzaVAhQtLS307NkTs2bNKuz2/ScnT558Y5bQw8MDw4cPh5ubG+rXr482bdpg69at2LhxI3bv3g0AcHV1xYoVK/Dtt98CeHVPX3516dIFAQEB8Pf3x3fffYcDBw7g999/ly4zDx8+HO7u7nB1dYW3tzciIiIwY8YMREZGFlKviYiIqFgT7+nRo0fi1KlTIjo6WqSmpr5vNUUGQK7p6tWrQggh1q5dK5ydnYWurq6oUaOGCA8Pl459+PChaNu2rdDT0xMVKlQQW7duFQBEfHy8VPf+/ful8suXLxd2dnbS9tGjR0WtWrWErq6uaNSokfD19RVeXl7S/tfPXaVKFbF+/fp890ulUv3TF5V49Up3zU9ERET0/9/xKpXqreVkQvzrBq13OHjw4Fv3e3h4vEcIWXzEx8fjzp07cHNzk/IGDRqEZ8+eITQ09D/Xn5qaCqVSCUAFwPg/1/cpKNhvIBERUfGU8x2vUqlgbJz3d3yBL5u+/rDZ18nlctjY2BTo/rDiSKVSoXnz5li9ejVcXV1x5swZrFq1CmvXrv3YTSMiIqJioMDB2+s34gOvVmlev34dgwcPRrdu3QqtYZqqVq1aCAkJQUBAABITE2Fra4sZM2bA29v7YzeNiIiIioECXzbNy8WLF9GmTRvcunWrMKqjPOR3SpWIiIg0S36/4wv8qJC83L9/H48fPy6s6oiIiIgoFwW+bNq7d+838p48eYI9e/agU6dOhdIoIiIiIsrde71h4XUymQzm5uaYPn06unfvXmgNIyIiIqI3Fdo9b/RhfMhHhfA3g4iI6MMp1EeFTJo0Kd8nLsjbCIiIiIioYPIVvO3fv1/6OSsrC4cPH0aZMmVQq1Yt6Orq4vz587h16xbatGlTZA0lIiIionyuNt2/f7+Uatasif79+yMhIUF6J+i1a9cwZMgQKBSKom5vkcrIyEBgYCAcHR0hl8tha2sLf39/tZfU54e9vX2hvE2BiIiI6N8KfM+bkZERzpw5g0qVKqnlx8XFoXbt2nj27FmhNvBD8vf3x549ezBr1ixUqFAB169fx7Bhw+Dg4ICIiIh81/P3339DoVBAX1+/0NvIe96IiIiKpyJ7PVaZMmWwc+fON4K38PBwODo6Fryln5DQ0FAsW7YMzZo1A/BqBm3hwoVwd3dHUlISbGxs8lVP6dKli7KZREREVIIV+CG9P//8M0aOHIkWLVrghx9+wMiRI9G4cWNMmTIFs2bNKoImfjhaWlqIjIxUewVYgwYNEBsbiylTpqBjx45S/tSpUyGXy5GWlgbg1cyjnp4enj17pnbZ1NPTE8HBwWjRogX09fVRr149XLt2Df3794dCoUDFihVx4MCBD9pPIiIi0lwFDt6+/vprREdHo3bt2rh06RIuX76MBg0aICYmRpqx0lTDhg3D3LlzYW9vDz8/P4SHh+PFixeoWrUqvL29cfDgQek5dwcOHEBGRgZOnz4NANizZw/c3NxgaGj4Rr2TJk1C//79cebMGahUKri6usLa2hqnT59G9erVMXTo0DzblJ6ejtTUVLVEREREJZh4T8+ePRPnz58XZ8+eFSqV6n2r+eSsXr1aNGzYUGhpaQkAwsjISCxbtkw8e/ZM6OrqigsXLoiMjAxhZGQkWrVqJX7++WchhBBfffWVmDZtmhBCCDs7O7F8+XIhhBCNGzcWXbp0keofNWqUsLW1FdnZ2UIIIbZv3y50dXXzbM+ECRMEgFySSry6K63oEhEREX04KpVKAHhnXFXgmbeMjAwMHz4cpqamqF27NurWrYvSpUujd+/eePnyZaEGlh9Dt27dcOTIEdy/fx9hYWGoVq0a+vTpg0uXLsHNzQ1RUVE4e/Ys7Ozs0KZNGxw+fBhZWVmIiopCq1atcq3z9XsB9fX1YWdnB5lMJm2/bdwCAgKgUqmklJiYWLgdJiIiIo1S4OBt5MiRiIiIwJYtW/D48WM8fPgQmzZtwsGDB/HTTz8VRRs/iJiYGIwYMULaNjc3R9euXXHgwAGUK1cOkZGR8PLyQlRUFA4ePAg3Nze4ubnh6NGjOHXqFBQKBapXr55r3To66utCtLTyP+xyuRzGxsZqiYiIiEquAgdva9aswZIlS9CyZUsYGRnBxMQEbdq0weLFi7F69eqiaOMHkZmZiRkzZuDcuXNq+bq6ujAwMEDp0qXRsmVLHDx4EIcPH4a7uztq1qyJzMxMzJ49Gy1btvxILSciIqKSpMDBW3Z2NiwtLd/IL126dIEfZvspcXFxgbe3N9q1a4c1a9YgISEBx48fh5+fH9LS0tChQwfUrFkTWlpa2LFjB9zc3KClpYWGDRti3bp1eV4yJSIiIipMBQ7emjVrhtGjR6utenz8+DECAgLQpEmTQm3ch7Z+/Xp0794dgYGBqFy5Mry9vaFSqXDw4EEYGRlBJpOhRYsWsLKygq2tLQDA3d0dWlpaaN68+UduPREREZUEBX7Dwp07d9CkSRMkJSVJD+qNi4uDo6MjtmzZAjs7uyJpKL3CNywQEREVT0X2hgUTExPExsZix44duHz5MvT09ODs7IwWLVoU6EZ8IiIiIiq4As+82dvbY9OmTahdu3ZRtYnegjNvRERExVORzbxpa2sXi+e5aTqVCuBTQ4iIiEqeAgdv3t7eaNGiBb744gvY29tDT09Pbf/48eMLrXFEREREpK7Al03ftqJUJpMhMjLyPzeK8pbfKVUiIiLSLEV22XT//v3/qWFERERE9P7yHbytXr0aGzduhK6uLr766it8++23Rdkuegelsmjr52IFIiKiT1O+nu0xe/Zs9OrVCy9evMCzZ8/Qs2dPjBkzpqjbRkRERET/kq/gbeHChVi6dCl27NiBiIgIrF27FvPmzUMBb5cjIiIiov8oX8Hb9evX0axZM2m7bdu2ePbsGZKSkoqkUUlJSejfvz9sbGygr6+PatWqITg4GJmZmfk6/uXLl1i8eHGRtI2IiIjoY8pX8JaZmYlSpUpJ2zo6OtDX10daWlqhNygxMRH16tVDfHw81q9fj7/++gvjx49HSEgI2rZti+zs7HfWsXbtWkydOrXQ20ZERET0sRV4tWlRGzJkCBwdHbFz505oa2sDABwcHNCgQQNUq1YNCxYswKBBg95aBy/nEhERUXGV75eRrl+/HitXrpRSVlYWNm3apJa3cuXK/9SYe/fuYcuWLRg9erQUuOWwtbWFr68vFi9ejNDQUNjb26vt9/T0RGBgIKKiotCrVy/cvHkTMpkMCQkJEEJg8uTJKFOmDExMTPDll1/i1q1b0rEymQzjx4+HhYUFvvzyS2hra+PixYsAgIyMDBgaGmLChAlS+a5du2Ls2LEAgEOHDqFu3brQ19dHjRo1EB4eLpV7+fIl/P39UbZsWZQqVQr29vZYtGiRtN/e3h6jR4+GjY0NateuzaCTiIiI3ilfM2+2traYPn26Wp6VlRVCQkLU8mQyGXr06PHejTl79iyEEHB1dc11v5ubG0JCQpCenp5nHQ0bNsSsWbMQHByMU6dOoXTp0ggJCUFYWBjWrFkDa2trBAcHw8vLCxcuXJAuB0dERODIkSPIysrCvXv3EBUVherVq+PUqVN48eIFjhw5AuDVrN6+ffvw3XffITk5GV988QWmTp2KVq1a4fjx4/D19YWlpSXc3d0RFBSEbdu2ITw8HJaWllixYgUGDx6Mdu3awcrKCgAQFhaG3bt3Izs7GzKZ7I3+pKenq/U3NTX1vceXiIiINF++greEhIQibsYrKSkpAABTU9Nc9+fk55TLja6uLpRKJbS1tWFtbQ0A+PXXXzF//nx4enoCAH777TfY2Nhg586d+PLLLwEAAwYMgLOzMwDAy8sLUVFRGDx4MA4ePIjWrVvj4MGDyMrKwsWLF5Geno769etj4sSJaN68OQYPHgwAcHJywrlz5zBr1iy4u7ujZs2aaNasGerXrw8AGDNmDCZNmoS4uDgpePPx8UGNGjXy7E9QUBAmTpyYr/EjIiKi4i/fl00/BDMzMwBAcnJyrvvv3r2rVi4/nj59itu3b6Nz585QKBRQKBRQKpV4+PAh4uLipHKvX4Zt2bIlDh48CCEEDh48iF69ekEulyM6Ohp79+5F8+bNoaOjg0uXLiEiIkKqV6FQICQkRKr3q6++wosXLzBixAh4e3tL58jKysr1vLkJCAiASqWSUmJiYr77TkRERMXPJ7VgoW7dutDW1saZM2dQrly5N/afPn0an332GfT09N7Yl9djRHLyN2zYIM2s5Xg9CHy9zgYNGiAtLQ0xMTE4cuQIli9fjkaNGuHIkSPYt28f2rdvL9Xt4+PzxgOLcy7Fjh07FosXL0avXr3Qo0cPzJ8//41gLbe+vE4ul0Mul7+1DBEREZUcn9TMW+nSpfH1119j8uTJarNTwKtHiCxduhT9+vWDrq4unjx5Iu0TQiA+Pl7afv3eMRMTE1haWiI5ORlOTk5wcnKCra0tRo0ahStXruTaDh0dHTRt2hTz5s2DlZUVrKys4O7ujn379uHAgQNo1aoVAMDZ2RlXr16V6nVycsKff/6JsLAwAK8ebhwSEoKff/4ZnTt3xrNnz6T2EhEREb2PTyp4A169iislJQWtW7fG4cOHcevWLWzatAlNmjSBp6cnvvvuO9StWxcpKSmYO3cubty4AX9/f7X74AwNDfHo0SNcvXoVmZmZ8Pf3x08//YSIiAhcvXoVffv2xZEjR1C5cuU82+Hl5YUVK1bAzc0NAODu7o6IiAg4ODhIs4LfffcdTp8+jbFjx+Lq1atYs2YNxowZAzs7OwCAubk5IiIicOPGDRw+fBjdu3cHgLcuuCAiIiJ6m08ueCtTpgyOHz8OZ2dndO3aFc7Ozhg7diwGDhyIiIgIaGlpoWLFiggODsaUKVOkR2x07NhRqqNp06ZwcnJCjRo1EB0djZEjR6Jv377o378/atWqhZs3b2LXrl15LowAXt339vLlSyl4c3Fxgb6+vjTrBgB2dnaIiIjAjh07UL16dYwdOxbTp09Ht27dAADLli1DdHQ0qlWrBl9fX3zzzTeoV68ezp07V0SjR0RERMWdTPAankZJTU2FUqkEoAJgXGTn4W8FERHRh5XzHa9SqWBsnPd3/Cc380ZEREREeWPwRkRERKRBPqlHhVD+qVTAW2ZUiYiIqJjizBsRERGRBmHwRkRERKRBGLwRERERaRDe86ahlMqiq5uPCSEiIvp0ceaNiIiISIMweCMiIiLSICUyeMvIyEBgYCAcHR0hl8tha2sLf39/6WX39vb2CA0NLbTzPXnyBCtXriy0+oiIiKjkKpHB2+jRoxEeHo7FixfjypUrWL58OXbv3o2uXbsWyflmzJiBZcuWFUndREREVLKUyAULoaGhWLZsGZo1awbg1UzbwoUL4e7ujqSkpEI/H18fS0RERIWlRM68aWlpITIyEtnZ2VJegwYNEBsbCwsLCwBAbGwsGjZsCD09PdSuXRvR0dFS2du3b+Obb76BmZkZLCwsMHToUKSnpwN4FRg2atQIX3/9NZRKJaZOnYqJEyfiwIEDkMlkAIDIyEjUqlULenp6cHR0xG+//fbhOk9EREQarUQGb8OGDcPcuXNhb28PPz8/hIeH48WLF6hatSpKlSoFAFiyZAlGjx6NmJgYmJmZYeDAgQCAly9fomnTpnj27BkOHDiA9evXY9u2bRg1apRU/9GjR1GtWjUcP34cPXr0wIgRI9CgQQMkJSUhKysLnTp1QqdOnXD58mVMnjwZ3333Hf7666+PMhZERESkWUrkZdNx48bB0dER8+fPx6JFi7Bw4UIYGRlh9uzZ6NWrFwDAz88P7dq1AwAMHToU3377LQBg586duHPnDk6cOAFTU1MAwLx58/Dll19i6tSpAACZTIaffvoJ+vr6AACFQgFdXV1YW1sjJSUFKSkpsLKygr29Pezt7VGmTBnY2Njk2tb09HRpVg8AUlNTi2ZQiIiISCOUyJk3AOjWrRuOHDmC+/fvIywsDNWqVUOfPn1w5swZAECFChWkskqlEmlpaQCAS5cuoVKlSlLgBgANGzZEZmYmrl27BgCwtLSUArd/MzMzg5+fH/r16wc7OzsMHjwYSqVSrb7XBQUFQalUSql8+fKF0n8iIiLSTCUueIuJicGIESOkbXNzc3Tt2hUHDhxAuXLlEBkZCQDQ1tbO9Xg9Pb038rKystT+za3M6+bPn4+LFy+if//+OHHiBD7//HPs2LEj17IBAQFQqVRSSkxMfHcniYiIqNgqccFbZmYmZsyYgXPnzqnl6+rqwsDAAKVLl37r8c7OzoiLi0NKSoqUd+zYMejo6KjN1r0uZ6ECACQnJ2PQoEFwcnLCTz/9hFOnTqFZs2bYsmVLrsfK5XIYGxurJSIiIiq5Slzw5uLiAm9vb7Rr1w5r1qxBQkICjh8/Dj8/P6SlpaFDhw5vPb5FixZwdHRE9+7dceHCBezfvx9DhgxB165dYWJikusxhoaGuHv3LhISEmBmZoaNGzdi+PDhuH79Og4ePIjo6GjUrl27CHpLRERExU2JC94AYP369ejevTsCAwNRuXJleHt7Q6VS4eDBgzAyMnrrsdra2tIs2eeff45vv/0W7dq1e+vjPr7++mtkZ2ejatWqePz4MbZs2YLo6Gh89tln+Oabb9CnTx/07du3UPtIRERExZNM8AmyGiU1NRVKpRKACkDRXELlbwQREdGHl/Mdr1Kp3nqbVImceSMiIiLSVAzeiIiIiDRIiXxIb3GgUgFceEpERFTycOaNiIiISIMweCMiIiLSIAzeiIiIiDQI73nTUErlfzuejwMhIiLSTJx5IyIiItIgDN6IiIiINAiDNwAZGRkIDAyEo6Mj5HI5bG1t4e/vjydPngAA7O3tERoa+nEbSURERATe8wYAGD16NPbs2YPFixejQoUKuH79OoYNG4arV68iIiLiYzePiIiISMLgDUBoaCiWLVuGZs2aAXg107Zw4UK4u7sjKSnpI7eOiIiI6P/xsikALS0tREZGIjs7W8pr0KABYmNjYWFhoVZWCIHJkyejTJkyMDExwZdffolbt25J+x8/fozu3bvD2NgYZcqUwZAhQ/DixQsAQFRUFMqVK4c5c+bA3NwcVlZWmDp16ofpJBERERULDN4ADBs2DHPnzoW9vT38/PwQHh6OFy9eoGrVqihVqpRa2ZCQEISFhWHNmjU4fvw4rKys4OXlhYyMDABAnz59oFKpcOTIEWzevBmnTp3C4MGDpePv3buHlStXYs+ePfjtt9/w66+/YvHixR+0v0RERKS5ZELwiV8AEBYWhvnz5+P48ePIzs6GkZERZs+ejV69esHe3h6BgYHw9fVF+fLlMX/+fHz55ZcAgKysLNjY2GDp0qWoWrUqKlWqhJSUFCj/eRDbhQsXUKtWLaSkpODcuXNo0qQJoqOjUbNmTQDAhAkTsG3bNpw+fTrXdqWnpyM9PV3aTk1NRfny5QGoALz/y035qRMREX1aUlNToVQqoVKpYPyWF5hz5u0f3bp1w5EjR3D//n2EhYWhWrVq6NOnD86cOSOVefr0KW7fvo3OnTtDoVBAoVBAqVTi4cOHiIuLw6VLl5CdnY2yZctK+xs0aIDs7Gxcu3YNAKBQKKTADQDq1q2LS5cu5dmuoKAgKJVKKb0K3IiIiKikKvELFmJiYrBixQpMnz4dAGBubo6uXbuiY8eOcHJyQmRkpFQ2MzMTALBhwwY4Ozur1WNmZoaDBw9CqVTmOotWtmxZnDhxAjo66kOelZUFLa28Y+iAgAD4+/tL2/8/80ZEREQlUYmfecvMzMSMGTNw7tw5tXxdXV0YGBigdOnSUp6JiQksLS2RnJwMJycnODk5wdbWFqNGjcKVK1fg7OwMlUoFmUwm7X/x4gV++OEH6dLn48ePkZCQINV5+vRpfPbZZ3m2Ty6Xw9jYWC0RERFRyVXigzcXFxd4e3ujXbt2WLNmDRISEnD8+HH4+fkhLS0NHTp0UCvv7++Pn376CREREbh69Sr69u2LI0eOoHLlyqhSpQpatWqFbt264dSpUzh79ix8fX3x9OlTmJiYSHX069cPFy9eRHh4OObMmYNBgwZ94F4TERGRpirxl00BYP369Zg6dSoCAwNx69YtGBoaomXLljh48CCMjIzUyo4cORJPnjxB//79kZqairp162LXrl0wNTUFAKxatQpDhgxBs2bNoKOjg1atWmHu3LlqdbRu3Rpubm5QKBT43//+h65du36wvhIREZFm42rTDygqKgpNmjTBfxnynJUoXG1KRERUvHC1KREREVExxOCNiIiISIPwnrcPyNPT8z9dMn2dSgVw4SkREVHJw5k3IiIiIg3C4I2IiIhIgzB4IyIiItIgvOdNQ/3z3vt84WNBiIiIig/OvBERERFpEAZvRERERBpEo4M3mUyGqKioj90MIiIiog9Go4M3IiIiopKGwRsRERGRBinWwdumTZtQtWpVGBgYoF69ejhw4IC0Lzs7Gz/++CMsLCxgbm6OKVOmwMnJCVFRUWjXrh1Gjhwple3Xrx/s7Oyk7d27d6N8+fIAgMePH6N79+4wNjZGmTJlMGTIELx48UIqu2XLFtSuXRt6enowMTFBly5d8PTpUwBAYGAgvvrqK3h4eMDMzEytfURERES5KbbB2/nz59GzZ0+MHTsWMTEx8PHxQevWrXHt2jUAQFBQEFauXIk1a9Zg79692Lp1K27cuAEAaNmypdq9dAcOHEBiYiJu374NANizZw9atmwJAOjTpw9UKhWOHDmCzZs349SpUxg8eDAA4Pr16+jYsSO+++47XL58GevXr8fevXuxaNEiqe4///wTXbt2RWRkJOrVq/chhoaIiIg0mEwU1ss2PwKZTIb9+/fD09PzjX3du3eHpaUlpk+fLuV16NAB9vb2mD59OsqWLYvJkyejd+/eAIArV66gcuXK2L9/P8qVK4fKlSvj4cOHePHiBapVq4ZKlSrh+++/R+fOnVG7dm389NNPqF27NipVqoSUlBQo/3nw2oULF1CrVi2kpKTg/v37iIyMxIABA6Q2dOnSBQYGBli6dCkCAwOxcOFCJCcn59nH9PR0pKenS9upqan/zPqpAOTv5aaa+wkTERGVHKmpqVAqlVCpVDB+ywvMi+1Dei9duoT169fjt99+k/JevnyJli1b4sGDB7h79y5cXV2lfc7OzjA1NQUAODk5wc7ODocOHcLz58/RsGFDVKpUCYcPH0azZs0QGxuL5s2b4/Dhw8jOzkbZsmXVzp2dnY1r166hTp06kMvlmDp1Ki5evIjY2FjExsaie/fuUll7e/u39iMoKAgTJ04shBEhIiKi4qDYBm+ZmZkYPXo0evTooZavr68PHZ1X3f73pOPr215eXoiKikJaWhrc3NxQqVIlTJ48Wbq8aWJigszMTCiVSpw+ffqN85ctWxbnz5+Hm5sb2rZtCw8PD/j7+2PWrFlq5fT09N7aj4CAAPj7+0vb/z/zRkRERCVRsQ3enJ2dER8fDycnJylv1KhRqFSpEvr27YsyZcrgzJkz+OyzzwAAN27cwOPHj6WyLVu2xNSpU5GZmYl58+ahYsWKuHDhAv744w+0atVKOodKpYJMJkOFChUAvLpsOn78eCxfvhyrVq2Ch4cHwsLCpHqvXr2KKlWq5Lsfcrkccrn8vwwFERERFSMaH7ydPHkSaWlpankeHh4YPnw43N3d4erqCm9vb0RERGDGjBmIjIwEAAwZMgTjx4+Hra0tLCwsMHToUACv7qMDgKZNm+Lbb7+Fjo4O6tatC11dXTg5OeGPP/7AqFGjAABVqlRBq1at0K1bN8ydOxfa2tro168fzMzMYGJiAnNzc8TExODkyZMwMTHBb7/9hlOnTsHR0fEDjhAREREVJxofvI0ePfqNvKtXr6J+/fpYtWoVAgMD8cMPP6BChQpYu3YtPDw8AAAjR45EUlISOnToAG1tbQQEBODQoUPQ1dUFABgbG8PV1RUymUzKc3d3x8OHD1GnTh3pXKtWrcKQIUPQrFkz6OjooFWrVpg7dy4AYOjQoTh37hyaN28OPT09eHh4YPz48fj999+LeliIiIiomNLo1ab/xc6dO1GnTh2ULl0aAPD333/D0tIS8fHx71xE8DHlrEThalMiIqLipcSvNn2X3377DZmZmfjll18gk8kwfvx4uLq6ftKBGxEREVGxfUjvu4SEhEBbWxsNGzZE/fr1kZWVhU2bNn3sZhERERG9VYm9bKqp8julSkRERJolv9/xJXbmjYiIiEgTMXgjIiIi0iAM3oiIiIg0SIldbarplMr8leMdjURERMULZ96IiIiINAiDNyIiIiINonHBm6enJwIDA99Z7uXLl1i8eHGBj3sfGzZswP3794ukbiIiIqLXaVzwll9r167F1KlTpe2NGzdi5MiRhX6emzdv4ptvvsHz588LvW4iIiKifyu2Cxb+/exhMzOzD3IeIiIioqKksTNvoaGh8PT0xIQJE2BhYQETExP4+/tDCIGoqCj06tULN2/ehEwmQ0JCgtplU19fX4wePRqdO3eGgYEBqlatinPnzmHs2LEwMTFBuXLlsGHDBulcR44cgZubGwwMDGBoaIg2bdogKSkJAODg4CD9GxoaCgDYtGkTqlatCgMDA9SrVw8HDhyQ6jp//jwaNmwIAwMDlC1bFpMmTfowA0ZERETFgsYGbwBw9OhRXLlyBUeOHEFISAhmz56NvXv3omHDhpg1axbKlSuHpKQklC9f/o1jZ82aBU9PT8TExMDc3BxNmzbFvXv3cPz4cbRt2xYDBgxAdnY2VCoVvL294eXlhdjYWOzevRvXrl1DUFAQAODkyZPSv507d8b58+fRs2dPjB07FjExMfDx8UHr1q1x7do1AECPHj1Qu3ZtxMbGYunSpfjll1+wffv2DzdoREREpNE0OnjLysrCokWL4OzsDB8fH9SsWROnTp2Crq4ulEoltLW1YW1tDW1t7TeOrVOnDvz8/ODk5ISuXbvi+fPnmDNnDipXrowhQ4bg0aNHuHfvHl68eIFx48Zh3LhxcHBwQKNGjdChQwfExsYCAEqXLi39q6+vj+DgYPTr1w9du3aFk5MThg4ditatW2PBggUAgISEBJibm8POzg6tWrXC3r174eLikmcf09PTkZqaqpaIiIio5NLo4M3Kykrtxa3GxsbIyMjI17GOjo7Sz/r6+rCysoK+vr60DbwKnKytrdGzZ0/MnDkTPXr0QN26dREcHIysrKxc67106RJCQkKgUCikFBERgbi4OADAmDFjMGXKFNjY2KBPnz7SOfISFBQEpVIppdxmEYmIiKjk0OjgTVdX9428/C4g0NFRX6uhpZX7UNy5cwc1atRAZGQk6tSpg5kzZ2LEiBF51puZmYnRo0cjOjpaSn/99RcWLlwIABg9ejSuX7+O0aNH48aNG2jatCmWLFmSZ30BAQFQqVRSSkxMzFf/iIiIqHjS6ODtbWQyWaHUs2nTJpiZmWHr1q0YNmwY3N3dcePGDSlI/Pd5nJ2dER8fDycnJyktWrQIO3bsQFpaGoYNGwZdXV34+/tj//796N+/P8LDw/M8v1wuh7GxsVoiIiKikqvYPirE0NAQjx49wtWrV6UVoe/D3Nwct27dwr59++Dg4IANGzYgPDwcrq6u0nmAV6tILSwsMHz4cLi7u8PV1RXe3t6IiIjAjBkzEBkZCT09PRw+fBiJiYkICgrCkydPcPDgQXz11VeF0WUiIiIqAYrtzFvTpk3h5OSEGjVqIDo6+r3r+eabb+Dj44OOHTuibt26iIyMxPTp03Hp0iWkp6fDwsICPj4++Oabb7BkyRLUr18fq1atwvz581G1alUsWrQIa9euhYeHBwBg3bp1ePbsGVxdXeHl5QUPDw+MGzeukHpNRERExZ1M8CmzGiU1NRVKpRKACsC7L6Hy0yUiItIMOd/xKpXqrbdJFduZNyIiIqLiiMEbERERkQZh8KahVKpXl0TflYiIiKh4YfBGREREpEEYvBERERFpEAZvRERERBqEwRsRERGRBim2b1go7pTKd5fhggUiIqLihzNvRERERBqEwRsRERGRBtHo4E0mkyEqKupjNwOhoaGwt7f/2M0gIiKiEkCjg7dPRefOnXHq1KmP3QwiIiIqAbhgoRDo6+tDX1//YzeDiIiISoBiO/MmhMD//vc/ODg4QFdXF2XKlMHEiRMBAEeOHIGOjg7+/vtvqfyZM2dgYGCAJ0+eQAiByZMno0yZMjAxMcGXX36JW7duSWVlMhnGjx8PCwsLtG3bVu2yaVRUFOzt7bFgwQKULVsWhoaG6N69O9LT06Xjw8LCUKFCBRgYGKBr167o0qULAgMDP8i4EBERkWYrtsHbypUrMWvWLCxZsgRxcXEYP348AgMDcfbsWTRs2BBly5bFpk2bpPLr16+Ht7c3jIyMEBISgrCwMKxZswbHjx+HlZUVvLy8kJGRIZWPiIjAkSNH8PPPP79x7rt37+KPP/7Azp07sXHjRoSHh2PlypUAgMOHD6N3794YNWoUzp49C0NDQ6xbty7PfqSnpyM1NVUtERERUclVbIM3W1tbLF++HM2aNYO9vT0GDhwIa2trxMbGQiaT4dtvv8WGDRuk8hs2bMC3334LAPj1118xbdo0eHp6onLlyvjtt9+QkpKCnTt3SuUHDBgAZ2dnVK1a9Y1zZ2RkYM6cOahRowZatmyJVq1aSffEzZ8/H507d8aAAQNQuXJlLFiwAOXKlcuzH0FBQVAqlVIqX758YQ0RERERaaBiG7w1adIEFhYWCAgIwFdffQU7OzskJycjKysLANClSxdERUXh4cOHOHXqFB48eABvb288ffoUt2/fRufOnaFQKKBQKKBUKvHw4UPExcVJ9b9rdWnFihWln42NjaVZu5iYGLi6ukr7dHR0ULdu3TzrCQgIgEqlklJiYuL7DAcREREVE8V2wcKSJUswfPhw9O3bFx06dEBwcDCaNGki7a9VqxacnJywefNmxMXFoV27dtDT08Pjx48BvJqJc3Z2VqvTzMxM+llPT++t59fV1VXbFv+87kBHR0f6+d/7ciOXyyGXy996LiIiIio5iu3M28KFCzF+/HjMnDkT3bt3h4WFBe7du6cWKHXt2hURERHYtm2bdMnUxMQElpaWSE5OhpOTE5ycnGBra4tRo0bhypUr/7ld1apVw5kzZ6TtrKwsREdH/+d6iYiIqGTQ+Jm3kydPIi0tTS3Pw8MD5ubm2Lt3L9q1a4cnT55gzJgxyMjIUFv12aVLF/zvf/+DgYEBvLy8pHx/f3/89NNPsLS0ROXKlTF58mQcOXIES5cu/c/tHTx4MDw9PeHh4QE3NzeEhIQgISEBMpnsP9dNRERExZ/GB2+jR49+I+/q1auYPXs2evXqhZo1a8LS0hKdO3eGoaEhzp07J5VzcnJC1apV4eLiglKlSkn5I0eOxJMnT9C/f3+kpqaibt262LVrF0xNTf9zexs0aIB58+Zh4sSJePDgATp16oQGDRq8cZmViIiIKDcy8bYbroq57Oxs2NnZYeXKlWr3wxWlkydPQqlUqt1PV61aNfzwww/w9fV95/GpqalQKpUAVACM31q25H6yREREmifnO16lUsHYOO/veI2feXtf27Ztw65du6Cvr4/GjRt/sPMeO3YMc+fOxcqVK2FjY4O1a9ciMTERrVq1+mBtICIiIs1VYoO34OBgXLlyBevWrYOW1odbtzFo0CDEx8ejffv2UKlUqFWrFnbs2AFra+sC1aNSAW8JyomIiKiYKtGXTTVRfqdUiYiISLPk9zu+2D4qhIiIiKg4YvBGREREpEEYvBERERFpkBK7YEHTKZVv3887GYmIiIonzrwRERERaRAGb0REREQapMQHb76+vpDJZHmmqKioIj1/aGgo7O3ti/QcREREVHyU+OBt9uzZSEpKQlJSEmbNmoVy5cpJ20lJSWjYsOHHbiIRERGRpMQvWFAqlf+8K/TVz9ra2gV+2wERERHRh1LiZ97eJiEhATKZDJMnT4apqSkGDx4MANi0aROqVq0KAwMD1KtXDwcOHJCO8fT0xNSpU9GyZUvo6+ujUqVK2LVrl7T/7t27aN26NQwNDeHi4oLr169/8H4RERGR5irxM2/5ceTIEZw+fRrZ2dk4f/48evbsiYULF6JevXrYvn07WrdujZiYGDg5OQEApk6divnz52P+/PkICAhAv379kJCQAC0tLXTs2BEKhQInT57ExYsX0bdvX5ibm+d57vT0dKSnp0vbqampRd5fIiIi+nQxeMuH77//HhUqVAAAdO/eHf369UPXrl0BAEOHDsWBAwewYMECTJ8+HQDg7e0NX19fAMDYsWNRs2ZNJCcn49GjRzh27Bhu3rwJW1tbVKtWDWfOnMH69evzPHdQUBAmTpxYtB0kIiIijcHLpvnw+mrQS5cuISQkBAqFQkoRERGIi4uTylSsWFH6OefFshkZGfjrr79gZmYGW1tbab+rq+tbzx0QEACVSiWlxMTEQuoVERERaSLOvOWDnp6e9HNmZiZGjx6NHj16qJXR19eXftbV1X2jDvHPKw/Ev159kFvZ18nlcsjl8gK3mYiIiIonzrwVkLOzM+Lj4+Hk5CSlRYsWYceOHe88tnr16nj06BGuXbsm5Z07d64om0tERETFDGfeCmj48OFwd3eHq6srvL29ERERgRkzZiAyMvKdx1apUgXNmjVD7969ERISgvj4eMydOxdGRkYfoOVERERUHHDmrYDq16+PVatWYf78+ahatSoWLVqEtWvXwsPDI1/Hr1u3DhYWFmjQoAECAgIwbNiwIm4xERERFScy8e+bsOiTlpqa+s9DhVUAjPMsx0+ViIhIs+R8x6tUKmnBY24480ZERESkQRi8EREREWkQBm8aSqV6dWk0r0RERETFE1ebapicWxT5miwiIqLiJee7/V3LERi8aZiHDx8CAMqXL/+RW0JERERF4cmTJ/8sTswdgzcNY2ZmBgC4devWWz9YUpeamory5csjMTHxrSt4SB3H7f1x7N4Px+39cNze36c0dkIIPHnyBGXKlHlrOQZvGkZL69Vtikql8qP/kmkiY2Njjtt74Li9P47d++G4vR+O2/v7VMYuPxMzXLBAREREpEEYvBERERFpEAZvGkYul2PChAmQy+UfuykaheP2fjhu749j9344bu+H4/b+NHHs+HosIiIiIg3CmTciIiIiDcLgjYiIiEiDMHjTIGlpaejTpw9MTExgY2OD6dOnf+wmfVTp6emoXr06oqKipLz4+Hg0b94choaGqFq1Knbv3q12zN69e1G9enUYGBigadOmuHHjhtr+WbNmoWzZsjAyMkKfPn3w/PnzD9GVD+LOnTvo2LEjzMzMULZsWfj7+yMtLQ0Ax+1drl27hpYtW0KhUMDW1hbTpk2T9nHs8sfb2xu+vr7S9rlz5/D555/DwMAArq6uOHPmjFr5tWvXokKFCjAwMMDXX3+NBw8eSPuEEPjxxx9RunRpmJmZYdSoUcjOzv5QXfkgNm3aBJlMppY6duwIgGP3Nunp6Rg0aBBMTU1hZWWFMWPGSG8rKFbjJkhjDB48WHz22WfizJkzYuPGjcLIyEhs2LDhYzfro3jx4oX4+uuvBQCxf/9+IYQQ2dnZ4rPPPhPdunUTf/31l/jf//4nDAwMxM2bN4UQQty8eVMYGhqK4OBgcfHiRfHNN9+IGjVqiOzsbCGEEH/88YdQKpUiIiJCnDx5UlStWlUMGjToY3WxUGVnZ4v69euL1q1bi4sXL4qDBw8KJycnMXLkSI7bO2RlZYlKlSqJbt26ibi4OLFt2zZhbGwswsLCOHb5tHbtWgFA9OzZUwghxNOnT4W1tbUYMWKE+Ouvv8TQoUOFlZWVePr0qRBCiBMnTgh9fX2xYsUKcf78edG4cWPh7e0t1RccHCzKly8vDh06JCIjI0WZMmXEtGnTPkbXisyUKVPEl19+KZKSkqT06NEjjt07DBgwQFSsWFGcOHFC7N27V1hYWIiFCxcWu3Fj8KYhnj59KvT09KRARQghJk+eLBo3bvzR2vSxxMbGipo1a4rPPvtMLXjbt2+fMDQ0lP4YhRCiWbNmYsKECUIIIcaNG6c2Xs+ePRNGRkbS8e7u7lJZIYQ4dOiQ0NfXF8+ePSviHhW9S5cuCQAiOTlZyluzZo0oU6YMx+0d7t69K7755huRmpoq5X399dfCz8+PY5cPDx8+FOXKlROurq5S8LZ06VLh4OAgBbHZ2dnCyclJLF++XAghRPfu3aWyQghx69YtIZPJxI0bN4QQQpQvX14qK4QQq1atEnZ2dh+gNx9Ot27dREBAwBv5HLu8PXz4UOjo6IioqCgpLygoSPTq1avYjRsvm2qI8+fPIyMjAw0bNpTy3NzccOLEiWI15Z0fBw4cQJMmTXDs2DG1/OPHj8PFxQWGhoZSnpubm1Tu+PHj8PDwkPYZGBjAxcUFx44dQ1ZWFk6dOqW2v379+nj58iXOnz9fxD0qetbW1ti5cyesrKzU8lUqFcftHWxsbLBu3ToYGRlBCIEjR47g4MGD8PT05Njlw8iRI9G9e3dUrVpVyjt+/Djc3Nwgk8kAADKZDI0aNcpz3MqXLw9bW1scP34cd+/eRWJiotp+Nzc33Lx5E0lJSR+oV0Xvr7/+QqVKld7I59jl7fDhw1AqlWjcuLGU9+OPP2LZsmXFbtwYvGmIpKQkWFhYQFdXV8qzsrJCWlqa9LL6ksLPzw8zZ86EgYGBWn5SUtIb74OzsrLC7du337n/8ePHSEtLU9uvo6MDc3Nz6XhNZmJigpYtW0rb2dnZCAkJQbNmzThuBWBvbw83Nzc0aNAAHTp04Ni9Q2RkJA4ePIhx48ap5f+Xccv5snx9f87/lBSXcRNC4MqVK9i1axcqVaqEChUq4Mcff8TLly85dm9x48YN2NvbY+XKlahcuTIcHR0xefJkZGdnF7tx47tNNcTz58/feIBgznZ6evrHaNInJ68xyhmft+3PuUn8bccXJ6NGjcLZs2dx6tQpzJw5k+OWT+Hh4UhOToafnx+GDx/O37m3SEtLw4ABAzBv3jzo6+ur7SvscStu/y28deuWNAbr169HfHw8hg4dihcvXnDs3uLp06e4evUqfvvtNyxfvhxJSUkYMGAADAwMit24MXjTEHp6em/8kuRs/3sGqqTS09N7YxYyPT1dGp+8xtDExAR6enrSdl7HFxejR4/GrFmzsG7dOlSvXp3jVgB169YF8Cow6datG3r37o1nz56pleHYvTJx4kTUrVtXbcY3R17j8q5xMzAwUBu3f49hcRg3ALCzs8PDhw9hamoKmUyGWrVqITs7Gz4+PvD09OTY5UFHRwepqalYs2YN7OzsALwKhOfPn4+KFSsWq3HjZVMNUbZsWTx48ACZmZlSXnJyMvT19WFiYvLxGvYJKVu2LJKTk9XykpOTYWNj88795ubm0NPTU9ufmZmJhw8fSscXB0OGDMH06dOxevVqdOjQAQDH7V3u3buHzZs3q+VVrVoVL1++hI2NDccuD7///js2b94MhUIBhUKBsLAwhIWFQaFQ/KffubJly0rbr+8DUCzGLYeZmZl0fxYAVKlSBWlpabC2tubY5cHGxgZ6enpS4AYAzs7OSExMLHa/cwzeNEStWrVQqlQpHD9+XMo7fPgwXF1doaXFjxF4dbP32bNn8eLFCynv8OHDqF+/vrT/8OHD0r7nz5/j3LlzqF+/PrS0tODq6qq2/9ixYyhVqhRq1qz54TpRhCZOnIiFCxfi/9q5/5iq6j4O4G+E8AJ3gOC9IAL3uhAIyqEZEYJRCtdl4NA2oKktpkazbBWDnJYwIUYaobWoVslyogliXsJJ/gBDkUEoingVFhcFZmtCWFKUVz7PHz6eyQM8oT3Ec+392tg43/M9318w9uacc7+7du1CYmKiUs51++/MZjMWL16Mrq4upayhoQEajQYRERFcuxFUVVWhqakJjY2NaGxsRFxcHOLi4tDY2IiwsDDU1NQo+2/d+iDISOvW0dGBjo4OhIWFwcvLC76+voPOHzt2DL6+vvdEAAGAiooKuLu7D9rzr7GxEe7u7oiMjOTajSAsLAz9/f1oaWlRykwmE/R6/b33Ozdun3OlO/bCCy9IcHCw1NXVyd69e8XZ2Vn27Nkz3sMaV7htqxCLxSJBQUGSkJAgZ8+elZycHFGr1cqeW2azWVQqleTk5Ch7bs2YMUP56PjOnTvF2dlZ9u7dK3V1dRIcHCwvv/zyeE3tf+rcuXNia2sr69evH7Rv1OXLl7luf8Jiscjs2bMlJiZGmpubpby8XDw8PCQ/P59rdweee+45ZSuGq1evikajkTVr1khzc7OsWbNGPD09lS1XampqxN7eXj799FM5ffq0REVFSWxsrNJWTk6OeHl5SWVlpVRWVoqXl5e8++674zGtMfHzzz/L1KlTJSkpSc6fPy/79+8XLy8vyc3N5dr9iYULF8pjjz0mjY2NcuDAAdFoNLJly5Z7bt0Y3qxIX1+fLF++XJycnMTLy0vee++98R7SuLs9vImItLa2yty5c2XixIkSHBwsBw8eHFR///794u/vLw4ODjJv3jxlD59bcnJyRKvViouLiyQnJ8tvv/32d0xjzOXk5AiAYb9EuG5/pqurS+Lj48XZ2VmmTJki2dnZSgDj2o3O7eFN5OamqDNnzhSVSiWhoaFy8uTJQfW3bdsmPj4+4uTkJPHx8XLlyhXlnMVikVdffVVcXV1l8uTJkp6ervw87hVnz56V+fPni1qtlilTpkhGRoYyR67dyHp7e2XZsmWiVqtFq9VKZmbmPbluNiL/vodIRERERP/3+LIUERERkRVheCMiIiKyIgxvRERERFaE4Y2IiIjIijC8EREREVkRhjciIiIiK8LwRkRERGRFGN6IiIiIrAjDGxFZLRsbGzz77LNDygsLC6HX68ekT71ej8LCwjFpezSMRiO8vb3h6OiIioqKMe1rvOdKRMNjeCMiq7Zz504cOXJkvIfxt3nrrbdgMBhgMpkwd+7cMe2rvr4eCQkJY9oHEd05hjcismp6vR6rV6/GH3/8Md5D+VtcvXoVERER0Ol0cHBwGNO+NBrNmPdBRHeO4Y2IrFpWVha6urqwadOmYc+3t7fDxsYG7e3tSllGRgaioqIA3HzEGhUVhezsbEyaNAmenp7Yvn07SkpKoNPp4OrqivT09EFtnj17FjNnzoRKpYLBYMClS5eUcx0dHYiLi4OjoyP0ej0yMzNx48YNpa85c+YgPj4eLi4u2LFjx5Dx9vf3Iz09HT4+PnByckJcXBw6OjoA3Ayq7e3tSE5OHvaxcFVVFfR6PV588UW4uLggNzcXAPDxxx9j2rRpUKvViIqKQlNTEwDgo48+GtLOJ598gunTpyv93XpsKiLYuHEjvLy84OrqitjYWGXeixYtQmpqqtLGypUrodPplONvvvkGPj4+AIAvv/wSAQEBUKlUCAoKwldffTVkHkT03zG8EZFVmzp1KjIzM5GdnQ2z2XxXbZw4cQJtbW2or69HUlISUlJSsGXLFpSVlSEvLw/vvPMOTp06pdQvKChAWloavvvuO1gsFixfvhzAzYCzePFiaLVanDp1CoWFhSgqKsLbb7+tXFtTU4Pg4GDU1tbCYDAMGUtKSgpKS0vxxRdf4MSJE7h+/ToWLVqEgYEB1NfXw9vbG/n5+aivrx92LhcvXkR/fz8aGhqQlJSEsrIyZGRk4P3338epU6cQGRmJJ554Aj/99BOeeeYZdHV1oaGhQbl+z549wz4q/eCDD7Bjxw4UFRWhtrYWHh4eiImJwfXr12EwGFBVVaXUPXr0KDo6OtDZ2QkAOHjwIAwGA3788UcsW7YMa9euxYULF5CcnIykpCT09PTc2Q+M6J9OiIisFACprKwUi8UiM2bMkKefflpERLZt2yY6nU5ERMxmswAQs9msXLdhwwZ5/PHHlbp2dnZy7do1ERE5d+6cAJDDhw8r9bVarRQVFYmIiE6nk9TUVOXcrfZNJpMcOnRINBqN3LhxQzlvNBrFzc1N6cvGxkZ+/fXXYefT09MjEyZMkIqKCqWsu7tbHB0d5cCBA0r/27ZtG/b6yspKZSy3REREyNatWwfVmzVrllJmMBhk7dq1Sv/33XefnDlzZkhf3t7eYjQalTYsFotoNBoxGo3S2toqtra20tvbK5cvXxY3NzcJCwuTXbt2iYhISEiIFBcXy8mTJwWAHDx4UEREBgYGpKKiQvr6+oadDxENj3feiMjq2draoqCgAOXl5Xf1GM7DwwNOTk4AoLzjdfvjRAcHB/z+++/KcWhoqPK9Xq+Hm5sbTCYTTCYTuru74ezsDLVaDbVajYSEBPT09KC7uxsAoNVqR3yPrKWlBQMDA3j00UeVMjc3NwQEBMBkMo16PreP3WQyIS0tTRmPWq3G6dOn0dLSAgBITExEaWkpAGDfvn2YPn06HnrooUHtXbt2DZ2dnUhISFDacHFxQXd3N1paWuDn5wedTofq6mp8++23CA8PR3h4OI4dO4YrV66gubkZ8+fPR0hICBYuXIjo6GgEBgbijTfewLRp0+Do6DjquRERYDfeAyAi+l8IDw9HcnIyXnnlFaSlpSnlNjY2Q+paLJZBx3Z2Q/8UTpgw8v+2tra2g44HBgZgb28Pi8WCwMBA7Nu3b8g1Li4uAACVSjViuyOdu3HjhvLe3Gjc3o7FYkF+fj7mzZs3qI6zszMAID4+HikpKWhubh7xkemt9SouLkZAQMCgc25ubgCAmJgYVFVVob+/HxEREfD398fGjRtx5MgRhIaGwtXVFQDw9ddfo66uDkajEaWlpfjwww9RXV2NkJCQUc+P6J+Od96I6J6Rm5uLvr4+bN68WSmzt7cHAPzyyy9KWVtb21/q59YL/wDQ2tqK3t5eBAQEICAgAJcuXYJGo4Gfnx/8/PxgNpuxYcOGYUPkf7r//vthZ2eH2tpapay7uxutra1DQtNoBQQEoLOzUxmPn58fsrOzlT5cXFywYMEC7N69G4cOHUJiYuKQNlxdXaHVavHDDz8obfj6+iItLQ0XLlwAABgMBhw9ehTHjx9HZGQkIiIi0NTUhJKSEixYsAAAcP78eaSmpiI0NBRZWVlobm6Gj4/PmO9XR3SvYXgjonuGu7s7cnNzB32y1MPDAz4+Pti0aRPa2tpQWFiI8vLyv9RPXl4eSktLcfr0aTz//POIjY2Fn58fYmJioNPpsHTpUjQ1NaG6uhqrVq2Co6PjkLt1w1Gr1Vi5ciVeeuklVFVV4cyZM1i6dCl8fHwQHR19V2N97bXXkJ+fj+3bt+P7779Heno6du/ejQceeECpk5iYiLy8PAQGBsLf33/EdtatW4eysjK0trZixYoVOH78OAIDAwEATz75JJqamtDa2orZs2crAfb28Obq6oqCggJkZWXBbDajvLwc7e3tmDlz5l3NjeifiuGNiO4pycnJCA8PV44nTJiAzz77DHV1dQgKCkJxcTHWrVv3l/p4/fXXsX79eoSFhUGr1eLzzz8HcPNxqtFoVN5bW7JkCZ566ils3bp11G1v3rwZ0dHRWLJkCebMmQOVSoVDhw5h4sSJdzXWhIQEZGdn480338SDDz6Iw4cPo6ysTNkOBABiY2MxMDAw7F23W1JTU7FixQqsWrUKISEhuHjxIioqKjBp0iQANx/DPvLII5g1a5ZytzMyMhKTJ0/Gww8/DADw9PREaWkpSkpKEBQUhNWrVyMnJwcxMTF3NTeifyobEZHxHgQRERERjQ7vvBERERFZEYY3IiIiIivC8EZERERkRRjeiIiIiKwIwxsRERGRFWF4IyIiIrIiDG9EREREVoThjYiIiMiKMLwRERERWRGGNyIiIiIrwvBGREREZEUY3oiIiIisyL8AHXzRUkje5aYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Group the data by product category and count the number of reviews in each category\n",
    "count_sentiment_per_category = count_sentiment_per_category.sort_values(ascending=False)\n",
    "\n",
    "# Create a barplot using seaborn\n",
    "barplot = sns.barplot(\n",
    "    data=count_sentiment_per_category.reset_index(),  # Reset the index to have 'product_category' as a column\n",
    "    y='product_category',\n",
    "    x='star_rating',\n",
    "    color=\"b\",\n",
    "    saturation=1\n",
    ")\n",
    "\n",
    "# Set the size of the figure\n",
    "sns.set(rc={'figure.figsize': (15.0, 10.0)})\n",
    "\n",
    "# Set title and labels\n",
    "plt.title(\"Number of reviews per product category\")\n",
    "plt.xlabel(\"Number of reviews\")\n",
    "plt.ylabel(\"Product category\")\n",
    "\n",
    "# Save the plot as an image\n",
    "plt.tight_layout()\n",
    "plt.savefig('num_reviews_per_category.png', dpi=300)\n",
    "\n",
    "# Show the barplot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot distribution of reviews per sentiment per category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   product_category  star_rating  count_reviews\n",
      "4           Blouses            5           1611\n",
      "3           Blouses            4            645\n",
      "2           Blouses            3            384\n",
      "1           Blouses            2            228\n",
      "0           Blouses            1            115\n",
      "..              ...          ...            ...\n",
      "89            Trend            5             52\n",
      "88            Trend            4             26\n",
      "87            Trend            3             19\n",
      "86            Trend            2             11\n",
      "85            Trend            1             10\n",
      "\n",
      "[90 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# Group the data by 'product_category' and 'star_rating', then count the reviews\n",
    "grouped = df_transformed.groupby(['product_category', 'star_rating']).size().reset_index(name='count_reviews')\n",
    "\n",
    "# Sort the DataFrame as requested\n",
    "result = grouped.sort_values(by=['product_category', 'star_rating'], ascending=[True, False])\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_category</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>count_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>5</td>\n",
       "      <td>1611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>4</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>3</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>2</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blouses</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Trend</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Trend</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Trend</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Trend</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Trend</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_category  star_rating  count_reviews\n",
       "0           Blouses            5           1611\n",
       "1           Blouses            4            645\n",
       "2           Blouses            3            384\n",
       "3           Blouses            2            228\n",
       "4           Blouses            1            115\n",
       "..              ...          ...            ...\n",
       "85            Trend            5             52\n",
       "86            Trend            4             26\n",
       "87            Trend            3             19\n",
       "88            Trend            2             11\n",
       "89            Trend            1             10\n",
       "\n",
       "[90 rows x 3 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_reviews = pd.DataFrame(result)\n",
    "count_reviews.reset_index(drop=True, inplace=True)\n",
    "count_reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of reviews: 22620\n"
     ]
    }
   ],
   "source": [
    "# Create grouped dataframes by category and by sentiment\n",
    "grouped_category = count_reviews.groupby('product_category')\n",
    "grouped_star = count_reviews.groupby('star_rating')\n",
    "\n",
    "# Create sum of sentiments per star sentiment\n",
    "df_sum = count_reviews.groupby(['star_rating']).sum()\n",
    "\n",
    "# Calculate total number of sentiments\n",
    "total = df_sum['count_reviews'].sum()\n",
    "print('Total number of reviews: {}'.format(total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Blouses': [1611, 645, 384, 228, 115],\n",
       " 'Dresses': [3267, 1367, 830, 458, 222],\n",
       " 'Fine gauge': [608, 229, 118, 74, 30],\n",
       " 'Intimates': [91, 26, 16, 8, 6],\n",
       " 'Jackets': [436, 114, 61, 38, 34],\n",
       " 'Jeans': [693, 215, 110, 55, 30],\n",
       " 'Knits': [2448, 1074, 605, 324, 174],\n",
       " 'Layering': [78, 35, 9, 7, 3],\n",
       " 'Legwear': [97, 29, 16, 6, 10],\n",
       " 'Lounge': [393, 151, 78, 24, 22],\n",
       " 'Outerwear': [173, 81, 29, 22, 14],\n",
       " 'Pants': [792, 282, 154, 81, 41],\n",
       " 'Shorts': [175, 65, 39, 16, 9],\n",
       " 'Skirts': [515, 199, 104, 51, 34],\n",
       " 'Sleep': [134, 41, 16, 14, 9],\n",
       " 'Sweaters': [776, 260, 193, 103, 48],\n",
       " 'Swim': [183, 67, 42, 28, 10],\n",
       " 'Trend': [52, 26, 19, 11, 10]}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution = {}\n",
    "count_reviews_per_rating = []\n",
    "i=0\n",
    "\n",
    "for category, ratings in grouped_category:\n",
    "    count_reviews_per_rating = []\n",
    "    for star in ratings['star_rating']:\n",
    "        count_reviews_per_rating.append(ratings.at[i, 'count_reviews'])\n",
    "        i=i+1;\n",
    "    distribution[category] = count_reviews_per_rating\n",
    "\n",
    "distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5</th>\n",
       "      <th>4</th>\n",
       "      <th>3</th>\n",
       "      <th>2</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Blouses</th>\n",
       "      <td>54.006034</td>\n",
       "      <td>21.622528</td>\n",
       "      <td>12.872947</td>\n",
       "      <td>7.643312</td>\n",
       "      <td>3.855179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dresses</th>\n",
       "      <td>53.173828</td>\n",
       "      <td>22.249349</td>\n",
       "      <td>13.509115</td>\n",
       "      <td>7.454427</td>\n",
       "      <td>3.613281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fine gauge</th>\n",
       "      <td>57.412653</td>\n",
       "      <td>21.624174</td>\n",
       "      <td>11.142587</td>\n",
       "      <td>6.987724</td>\n",
       "      <td>2.832861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intimates</th>\n",
       "      <td>61.904762</td>\n",
       "      <td>17.687075</td>\n",
       "      <td>10.884354</td>\n",
       "      <td>5.442177</td>\n",
       "      <td>4.081633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jackets</th>\n",
       "      <td>63.836018</td>\n",
       "      <td>16.691069</td>\n",
       "      <td>8.931186</td>\n",
       "      <td>5.563690</td>\n",
       "      <td>4.978038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeans</th>\n",
       "      <td>62.828649</td>\n",
       "      <td>19.492294</td>\n",
       "      <td>9.972801</td>\n",
       "      <td>4.986401</td>\n",
       "      <td>2.719855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knits</th>\n",
       "      <td>52.929730</td>\n",
       "      <td>23.221622</td>\n",
       "      <td>13.081081</td>\n",
       "      <td>7.005405</td>\n",
       "      <td>3.762162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Layering</th>\n",
       "      <td>59.090909</td>\n",
       "      <td>26.515152</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>5.303030</td>\n",
       "      <td>2.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Legwear</th>\n",
       "      <td>61.392405</td>\n",
       "      <td>18.354430</td>\n",
       "      <td>10.126582</td>\n",
       "      <td>3.797468</td>\n",
       "      <td>6.329114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lounge</th>\n",
       "      <td>58.832335</td>\n",
       "      <td>22.604790</td>\n",
       "      <td>11.676647</td>\n",
       "      <td>3.592814</td>\n",
       "      <td>3.293413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outerwear</th>\n",
       "      <td>54.231975</td>\n",
       "      <td>25.391850</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>6.896552</td>\n",
       "      <td>4.388715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pants</th>\n",
       "      <td>58.666667</td>\n",
       "      <td>20.888889</td>\n",
       "      <td>11.407407</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>57.565789</td>\n",
       "      <td>21.381579</td>\n",
       "      <td>12.828947</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>2.960526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skirts</th>\n",
       "      <td>57.032115</td>\n",
       "      <td>22.037652</td>\n",
       "      <td>11.517165</td>\n",
       "      <td>5.647841</td>\n",
       "      <td>3.765227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sleep</th>\n",
       "      <td>62.616822</td>\n",
       "      <td>19.158879</td>\n",
       "      <td>7.476636</td>\n",
       "      <td>6.542056</td>\n",
       "      <td>4.205607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweaters</th>\n",
       "      <td>56.231884</td>\n",
       "      <td>18.840580</td>\n",
       "      <td>13.985507</td>\n",
       "      <td>7.463768</td>\n",
       "      <td>3.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swim</th>\n",
       "      <td>55.454545</td>\n",
       "      <td>20.303030</td>\n",
       "      <td>12.727273</td>\n",
       "      <td>8.484848</td>\n",
       "      <td>3.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trend</th>\n",
       "      <td>44.067797</td>\n",
       "      <td>22.033898</td>\n",
       "      <td>16.101695</td>\n",
       "      <td>9.322034</td>\n",
       "      <td>8.474576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    5          4          3         2         1\n",
       "Blouses     54.006034  21.622528  12.872947  7.643312  3.855179\n",
       "Dresses     53.173828  22.249349  13.509115  7.454427  3.613281\n",
       "Fine gauge  57.412653  21.624174  11.142587  6.987724  2.832861\n",
       "Intimates   61.904762  17.687075  10.884354  5.442177  4.081633\n",
       "Jackets     63.836018  16.691069   8.931186  5.563690  4.978038\n",
       "Jeans       62.828649  19.492294   9.972801  4.986401  2.719855\n",
       "Knits       52.929730  23.221622  13.081081  7.005405  3.762162\n",
       "Layering    59.090909  26.515152   6.818182  5.303030  2.272727\n",
       "Legwear     61.392405  18.354430  10.126582  3.797468  6.329114\n",
       "Lounge      58.832335  22.604790  11.676647  3.592814  3.293413\n",
       "Outerwear   54.231975  25.391850   9.090909  6.896552  4.388715\n",
       "Pants       58.666667  20.888889  11.407407  6.000000  3.037037\n",
       "Shorts      57.565789  21.381579  12.828947  5.263158  2.960526\n",
       "Skirts      57.032115  22.037652  11.517165  5.647841  3.765227\n",
       "Sleep       62.616822  19.158879   7.476636  6.542056  4.205607\n",
       "Sweaters    56.231884  18.840580  13.985507  7.463768  3.478261\n",
       "Swim        55.454545  20.303030  12.727273  8.484848  3.030303\n",
       "Trend       44.067797  22.033898  16.101695  9.322034  8.474576"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_distribution_pct = pd.DataFrame(distribution).transpose().apply(\n",
    "    lambda num_sentiments: num_sentiments/sum(num_sentiments)*100, axis=1\n",
    ")\n",
    "df_distribution_pct.columns=['5', '4', '3','2', '1']\n",
    "df_distribution_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = df_distribution_pct.index\n",
    "\n",
    "# Plot bars\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "df_distribution_pct.plot(kind=\"barh\", \n",
    "                         stacked=True, \n",
    "                         edgecolor='white',\n",
    "                         width=1.0,\n",
    "                         color=['green', \n",
    "                                'orange', \n",
    "                                'blue',\n",
    "                                'yellow',\n",
    "                                'red'])\n",
    "\n",
    "plt.title(\"Distribution of reviews per sentiment per category\", \n",
    "          fontsize='16')\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.04,1), \n",
    "           loc=\"upper left\",\n",
    "           labels=['5', \n",
    "                   '4', \n",
    "                   '3',\n",
    "                   '2',\n",
    "                   '1'])\n",
    "\n",
    "plt.xlabel(\"% Breakdown of sentiments\", fontsize='14')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot a sentiment word cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Extract the text data from the \"review_body\" column in your DataFrame\n",
    "text_data = \" \".join(df_transformed['review_body'])\n",
    "\n",
    "# Create a WordCloud object and generate the word cloud\n",
    "wordcloud = WordCloud(width=800, height=400, background_color='white')\n",
    "wordcloud.generate(text_data)\n",
    "\n",
    "# Display the word cloud\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot distribution of words per review (This information is used for fine-tuning a bert model later on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  \n",
       "0              Blouses  \n",
       "1              Dresses  \n",
       "2              Dresses  \n",
       "3                Pants  \n",
       "4              Blouses  \n",
       "...                ...  \n",
       "22617          Dresses  \n",
       "22618            Knits  \n",
       "22619          Dresses  \n",
       "22620          Dresses  \n",
       "22621          Dresses  \n",
       "\n",
       "[22620 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the amount of words in each review\n",
    "df_transformed['count'] = df_transformed['review_body'].apply(lambda x: len(x.split()))\n",
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find out the max words across all reviews\n",
    "df_transformed['count'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot distribution of words\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.displot(df_transformed['count'])\n",
    "plt.xlim(0,150)\n",
    "plt.xlabel('The num of words', fontsize=16)\n",
    "plt.title('The number of words distribution', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform inference on dataset to predict sentiment using pre-trained models from huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First model to try out: BERT model pre-trained to produce 5 classes which is suitable for our dataset which has 5 ratings (1,2,3,4,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import model from huggingface (nlptown/bert-base-multilingual-uncased-sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "model_name = \"nlptown/bert-base-multilingual-uncased-sentiment\"  # Replace with a valid model name from Hugging Face's model hub\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test out model on single data (encode and fit model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encode and calculate sentiment \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SequenceClassifierOutput(loss=None, logits=tensor([[-2.4798, -2.7428, -1.5865,  1.0678,  4.7910]],\n",
       "       grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenizer.encode('I love this, absolutely the best!', return_tensors='pt')\n",
    "result = model(tokens)\n",
    "result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 574,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 574,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#argmax get highest value\n",
    "#logits=tensor([[-2.4798, -2.7428, -1.5865,  1.0678,  4.7910]]\n",
    "int(torch.argmax(result.logits))+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for inference on whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
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       "      <th>2</th>\n",
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       "      <td>I had such high hopes for this dress and reall...</td>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 803,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        If this product was in petite  i would get the...\n",
       "1        Love this dress!  it's sooo pretty.  i happene...\n",
       "2        I had such high hopes for this dress and reall...\n",
       "3        I love  love  love this jumpsuit. it's fun  fl...\n",
       "4        This shirt is very flattering to all due to th...\n",
       "                               ...                        \n",
       "22617    I was very happy to snag this dress at such a ...\n",
       "22618    It reminds me of maternity clothes. soft  stre...\n",
       "22619    This fit well  but the top was very see throug...\n",
       "22620    I bought this dress for a wedding i have this ...\n",
       "22621    This dress in a lovely platinum is feminine an...\n",
       "Name: review_body, Length: 22620, dtype: object"
      ]
     },
     "execution_count": 804,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review = df_transformed['review_body']\n",
    "review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>I was very happy to snag this dress at such a ...</td>\n",
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       "      <td>This fit well  but the top was very see throug...</td>\n",
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       "      <th>22618</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
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       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
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      "text/plain": [
       "                                                 reviews\n",
       "0      If this product was in petite  i would get the...\n",
       "1      Love this dress!  it's sooo pretty.  i happene...\n",
       "2      I had such high hopes for this dress and reall...\n",
       "3      I love  love  love this jumpsuit. it's fun  fl...\n",
       "4      This shirt is very flattering to all due to th...\n",
       "...                                                  ...\n",
       "22615  I was very happy to snag this dress at such a ...\n",
       "22616  It reminds me of maternity clothes. soft  stre...\n",
       "22617  This fit well  but the top was very see throug...\n",
       "22618  I bought this dress for a wedding i have this ...\n",
       "22619  This dress in a lovely platinum is feminine an...\n",
       "\n",
       "[22620 rows x 1 columns]"
      ]
     },
     "execution_count": 805,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert into dataframe\n",
    "\n",
    "text = pd.DataFrame(np.array(review), columns=['reviews'])\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"If this product was in petite  i would get the petite. the regular is a little long on me but a tailor can do a simple fix on that.     fits nicely! i'm 5'4  130lb and pregnant so i bough t medium to grow into.     the tie can be front or back so provides for some nice flexibility on form fitting.\""
      ]
     },
     "execution_count": 806,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['reviews'].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming 'text' is a DataFrame with a 'reviews' column\n",
    "#text['sentiment'] = text['reviews'].apply(lambda x: sentiment_score(x))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save prediction as csv\n",
    "#text.to_csv('five_class_prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>22615</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>5</td>\n",
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       "      <th>22616</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>3</td>\n",
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       "      <th>22617</th>\n",
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       "      <td>3</td>\n",
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       "      <th>22618</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>3</td>\n",
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       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 reviews  sentiment\n",
       "0      If this product was in petite  i would get the...          4\n",
       "1      Love this dress!  it's sooo pretty.  i happene...          5\n",
       "2      I had such high hopes for this dress and reall...          3\n",
       "3      I love  love  love this jumpsuit. it's fun  fl...          5\n",
       "4      This shirt is very flattering to all due to th...          5\n",
       "...                                                  ...        ...\n",
       "22615  I was very happy to snag this dress at such a ...          5\n",
       "22616  It reminds me of maternity clothes. soft  stre...          3\n",
       "22617  This fit well  but the top was very see throug...          3\n",
       "22618  I bought this dress for a wedding i have this ...          3\n",
       "22619  This dress in a lovely platinum is feminine an...          5\n",
       "\n",
       "[22620 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in prediction\n",
    "five_class = pd.read_csv('./predicted/five_class.csv')\n",
    "five_class.drop(columns='Unnamed: 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
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       "      <th>22619</th>\n",
       "      <td>3</td>\n",
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       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4\n",
       "1        5\n",
       "2        3\n",
       "3        5\n",
       "4        5\n",
       "        ..\n",
       "22617    5\n",
       "22618    3\n",
       "22619    3\n",
       "22620    3\n",
       "22621    5\n",
       "Name: star_rating, Length: 22620, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get ground_truth label\n",
    "ground_truth = df_transformed['star_rating']\n",
    "ground_truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4\n",
       "1        5\n",
       "2        3\n",
       "3        5\n",
       "4        5\n",
       "        ..\n",
       "22615    5\n",
       "22616    3\n",
       "22617    3\n",
       "22618    3\n",
       "22619    5\n",
       "Name: sentiment, Length: 22620, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = five_class['sentiment']\n",
    "predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "five_class_metrics = evaluate_classification(ground_truth, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BERT base (5 classes)</td>\n",
       "      <td>0.566092</td>\n",
       "      <td>0.653298</td>\n",
       "      <td>0.566092</td>\n",
       "      <td>0.592059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  BERT base (5 classes)  0.566092   0.653298  0.566092  0.592059"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "five_class_table = get_metrics_as_dataframe(\"BERT base (5 classes)\", five_class_metrics)\n",
    "five_class_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 984,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot confusion matrix\n",
    "plot_confusion_matrix(ground_truth, predicted, class_names_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second model to try out: BERT model pre-trained to produce 3 classes (Negative, Neutral and Positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "##Load model\n",
    "model_name = \"cardiffnlp/twitter-roberta-base-sentiment-latest\"  # Replace with a valid model name from Hugging Face's model hub\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_class = df_transformed.copy()\n",
    "three_class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert original 5 star rating into 3 classes (0,1,2) [0:negative, 1:neutral, 2:positive]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "      <th>count</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body product_category  \\\n",
       "0      If this product was in petite  i would get the...          Blouses   \n",
       "1      Love this dress!  it's sooo pretty.  i happene...          Dresses   \n",
       "2      I had such high hopes for this dress and reall...          Dresses   \n",
       "3      I love  love  love this jumpsuit. it's fun  fl...            Pants   \n",
       "4      This shirt is very flattering to all due to th...          Blouses   \n",
       "...                                                  ...              ...   \n",
       "22617  I was very happy to snag this dress at such a ...          Dresses   \n",
       "22618  It reminds me of maternity clothes. soft  stre...            Knits   \n",
       "22619  This fit well  but the top was very see throug...          Dresses   \n",
       "22620  I bought this dress for a wedding i have this ...          Dresses   \n",
       "22621  This dress in a lovely platinum is feminine an...          Dresses   \n",
       "\n",
       "       count  sentiment  \n",
       "0         60          2  \n",
       "1         62          2  \n",
       "2         98          1  \n",
       "3         22          2  \n",
       "4         36          2  \n",
       "...      ...        ...  \n",
       "22617     28          2  \n",
       "22618     38          1  \n",
       "22619     42          1  \n",
       "22620     86          1  \n",
       "22621     19          2  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_sentiment(star_rating):\n",
    "    if star_rating in {1, 2}: # negative\n",
    "        return 0 \n",
    "    if star_rating == 3:      # neutral\n",
    "        return 1\n",
    "    if star_rating in {4, 5}: # positive\n",
    "        return 2\n",
    "\n",
    "# transform star_rating into the sentiment\n",
    "three_class['sentiment'] = three_class['star_rating'].apply(lambda star_rating: to_sentiment(star_rating=star_rating))\n",
    "\n",
    "# drop the star rating column\n",
    "three_class.drop(columns=['star_rating'],\n",
    "                    inplace=True)\n",
    "\n",
    "three_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save a copy of three_class into a csv (will be used for google colab's fine-tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_class.to_csv('cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Love this dress!  it\\'s sooo pretty.  i happened to find it in a store  and i\\'m glad i did bc i never would have ordered it online bc it\\'s petite.  i bought a petite and am 5\\'8\".  i love the length on me- hits just a little below the knee.  would definitely be a true midi on someone who is truly petite.'"
      ]
     },
     "execution_count": 827,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_class['review_body'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 829,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test on single data\n",
    "sentiment_score_2(three_class['review_body'][1])\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare input data for inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_body</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body\n",
       "0      If this product was in petite  i would get the...\n",
       "1      Love this dress!  it's sooo pretty.  i happene...\n",
       "2      I had such high hopes for this dress and reall...\n",
       "3      I love  love  love this jumpsuit. it's fun  fl...\n",
       "4      This shirt is very flattering to all due to th...\n",
       "...                                                  ...\n",
       "22617  I was very happy to snag this dress at such a ...\n",
       "22618  It reminds me of maternity clothes. soft  stre...\n",
       "22619  This fit well  but the top was very see throug...\n",
       "22620  I bought this dress for a wedding i have this ...\n",
       "22621  This dress in a lovely platinum is feminine an...\n",
       "\n",
       "[22620 rows x 1 columns]"
      ]
     },
     "execution_count": 832,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_class_input = three_class['review_body']\n",
    "three_class_input = pd.DataFrame(three_class['review_body'])\n",
    "three_class_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [],
   "source": [
    "#three_class_input['predicted_sentiment'] = three_class_input['review_body'].apply(lambda x: sentiment_score_2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save predicted table as csv\n",
    "#three_class_input.to_csv('three_class_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read predicted csv\n",
    "three_class_pred = pd.read_csv('./predicted/three_class_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        2\n",
       "2        2\n",
       "3        2\n",
       "4        2\n",
       "        ..\n",
       "22615    2\n",
       "22616    2\n",
       "22617    0\n",
       "22618    0\n",
       "22619    2\n",
       "Name: predicted_sentiment, Length: 22620, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred = three_class_pred['predicted_sentiment']\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        2\n",
       "2        1\n",
       "3        2\n",
       "4        2\n",
       "        ..\n",
       "22617    2\n",
       "22618    1\n",
       "22619    1\n",
       "22620    1\n",
       "22621    2\n",
       "Name: sentiment, Length: 22620, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original = three_class['sentiment']\n",
    "original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_class_metrics = evaluate_classification(original, ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Three_class_table</td>\n",
       "      <td>0.793413</td>\n",
       "      <td>0.771015</td>\n",
       "      <td>0.793413</td>\n",
       "      <td>0.776169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  Three_class_table  0.793413   0.771015  0.793413  0.776169"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_class_table = get_metrics_as_dataframe(\"Three_class_table\", three_class_metrics)\n",
    "three_class_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot confusion matrix\n",
    "plot_confusion_matrix(original,ypred,class_names_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third model using Binary classification (positive, negative)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n",
    "\n",
    "##Load model\n",
    "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"  # Replace with a valid model name from Hugging Face's model hub\n",
    "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)\n",
    "model = DistilBertForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
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       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 843,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>22620 rows × 4 columns</p>\n",
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      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  count  \n",
       "0              Blouses     60  \n",
       "1              Dresses     62  \n",
       "2              Dresses     98  \n",
       "3                Pants     22  \n",
       "4              Blouses     36  \n",
       "...                ...    ...  \n",
       "22617          Dresses     28  \n",
       "22618            Knits     38  \n",
       "22619          Dresses     42  \n",
       "22620          Dresses     86  \n",
       "22621          Dresses     19  \n",
       "\n",
       "[22620 rows x 4 columns]"
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     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_class = df_transformed.copy()\n",
    "binary_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert star rating into binary labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>review_body</th>\n",
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       "      <td>1</td>\n",
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       "      <td>Knits</td>\n",
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       "      <th>22619</th>\n",
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       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body product_category  \\\n",
       "0      If this product was in petite  i would get the...          Blouses   \n",
       "1      Love this dress!  it's sooo pretty.  i happene...          Dresses   \n",
       "2      I had such high hopes for this dress and reall...          Dresses   \n",
       "3      I love  love  love this jumpsuit. it's fun  fl...            Pants   \n",
       "4      This shirt is very flattering to all due to th...          Blouses   \n",
       "...                                                  ...              ...   \n",
       "22617  I was very happy to snag this dress at such a ...          Dresses   \n",
       "22618  It reminds me of maternity clothes. soft  stre...            Knits   \n",
       "22619  This fit well  but the top was very see throug...          Dresses   \n",
       "22620  I bought this dress for a wedding i have this ...          Dresses   \n",
       "22621  This dress in a lovely platinum is feminine an...          Dresses   \n",
       "\n",
       "       count  sentiment  \n",
       "0         60          1  \n",
       "1         62          1  \n",
       "2         98          0  \n",
       "3         22          1  \n",
       "4         36          1  \n",
       "...      ...        ...  \n",
       "22617     28          1  \n",
       "22618     38          0  \n",
       "22619     42          0  \n",
       "22620     86          0  \n",
       "22621     19          1  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_binary_sentiment(star_rating):\n",
    "    if star_rating in {1, 2, 3}: # Negative\n",
    "        return 0 \n",
    "    if star_rating in {4, 5}:      # Positive\n",
    "        return 1\n",
    "\n",
    "# Transform star_rating into the sentiment\n",
    "binary_class['sentiment'] = binary_class['star_rating'].apply(lambda star_rating: to_binary_sentiment(star_rating=star_rating))\n",
    "\n",
    "# Drop the star rating column\n",
    "binary_class.drop(columns=['star_rating'], inplace=True)\n",
    "\n",
    "binary_class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 846,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test out on single data\n",
    "sentiment_score_2(binary_class['review_body'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [],
   "source": [
    "#binary_class['predicted_sentiment'] = binary_class['review_body'].apply(lambda x: sentiment_score_3(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save predicton as csv\n",
    "#binary_class.to_csv('binary_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Unnamed: 0                                        review_body  \\\n",
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       "1               1  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2               2  I had such high hopes for this dress and reall...   \n",
       "3               3  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4               4  This shirt is very flattering to all due to th...   \n",
       "...           ...                                                ...   \n",
       "22615       22617  I was very happy to snag this dress at such a ...   \n",
       "22616       22618  It reminds me of maternity clothes. soft  stre...   \n",
       "22617       22619  This fit well  but the top was very see throug...   \n",
       "22618       22620  I bought this dress for a wedding i have this ...   \n",
       "22619       22621  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  sentiment  predicted_sentiment  \n",
       "0              Blouses          1                    1  \n",
       "1              Dresses          1                    1  \n",
       "2              Dresses          0                    1  \n",
       "3                Pants          1                    1  \n",
       "4              Blouses          1                    1  \n",
       "...                ...        ...                  ...  \n",
       "22615          Dresses          1                    1  \n",
       "22616            Knits          0                    1  \n",
       "22617          Dresses          0                    1  \n",
       "22618          Dresses          0                    0  \n",
       "22619          Dresses          1                    1  \n",
       "\n",
       "[22620 rows x 5 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in prediction\n",
    "binary_pred = pd.read_csv('./predicted/binary_pred.csv')\n",
    "binary_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "original = binary_pred['sentiment']\n",
    "ypred = binary_pred['predicted_sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Accuracy': 0.8372236958443855,\n",
       " 'Precision': 0.8496382951756084,\n",
       " 'Recall': 0.8372236958443855,\n",
       " 'F1 Score': 0.8417815862826272,\n",
       " 'Confusion Matrix': array([[ 3828,  1364],\n",
       "        [ 2318, 15110]], dtype=int64)}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_metrics = evaluate_classification(original, ypred)\n",
    "binary_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>binary classification</td>\n",
       "      <td>0.837224</td>\n",
       "      <td>0.849638</td>\n",
       "      <td>0.837224</td>\n",
       "      <td>0.841782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  binary classification  0.837224   0.849638  0.837224  0.841782"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_table = get_metrics_as_dataframe(\"binary classification\", binary_metrics)\n",
    "binary_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot confusion matrix\n",
    "plot_confusion_matrix(original, ypred, class_names_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>star_rating</th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>5</td>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>3</td>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>3</td>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>3</td>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>5</td>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       star_rating                                        review_body  \\\n",
       "0                4  If this product was in petite  i would get the...   \n",
       "1                5  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2                3  I had such high hopes for this dress and reall...   \n",
       "3                5  I love  love  love this jumpsuit. it's fun  fl...   \n",
       "4                5  This shirt is very flattering to all due to th...   \n",
       "...            ...                                                ...   \n",
       "22617            5  I was very happy to snag this dress at such a ...   \n",
       "22618            3  It reminds me of maternity clothes. soft  stre...   \n",
       "22619            3  This fit well  but the top was very see throug...   \n",
       "22620            3  I bought this dress for a wedding i have this ...   \n",
       "22621            5  This dress in a lovely platinum is feminine an...   \n",
       "\n",
       "      product_category  \n",
       "0              Blouses  \n",
       "1              Dresses  \n",
       "2              Dresses  \n",
       "3                Pants  \n",
       "4              Blouses  \n",
       "...                ...  \n",
       "22617          Dresses  \n",
       "22618            Knits  \n",
       "22619          Dresses  \n",
       "22620          Dresses  \n",
       "22621          Dresses  \n",
       "\n",
       "[22620 rows x 3 columns]"
      ]
     },
     "execution_count": 708,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-tuned distilbert-base-uncased model for 3 classes sentiment analysis (positive, neutral and negative) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Load model after model is uploaded to huggingface hub\n",
    "model_name = \"ongaunjie/distilbert-cloths-sentiment\"  # Replace with a valid model name from Hugging Face's model hub\n",
    "tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)\n",
    "model = DistilBertForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_body</th>\n",
       "      <th>product_category</th>\n",
       "      <th>count</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>If this product was in petite  i would get the...</td>\n",
       "      <td>Blouses</td>\n",
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       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
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       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>Pants</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Blouses</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22617</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22618</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
       "      <td>Knits</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>Dresses</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body product_category  \\\n",
       "0      If this product was in petite  i would get the...          Blouses   \n",
       "1      Love this dress!  it's sooo pretty.  i happene...          Dresses   \n",
       "2      I had such high hopes for this dress and reall...          Dresses   \n",
       "3      I love  love  love this jumpsuit. it's fun  fl...            Pants   \n",
       "4      This shirt is very flattering to all due to th...          Blouses   \n",
       "...                                                  ...              ...   \n",
       "22617  I was very happy to snag this dress at such a ...          Dresses   \n",
       "22618  It reminds me of maternity clothes. soft  stre...            Knits   \n",
       "22619  This fit well  but the top was very see throug...          Dresses   \n",
       "22620  I bought this dress for a wedding i have this ...          Dresses   \n",
       "22621  This dress in a lovely platinum is feminine an...          Dresses   \n",
       "\n",
       "       count  sentiment  \n",
       "0         60          2  \n",
       "1         62          2  \n",
       "2         98          1  \n",
       "3         22          2  \n",
       "4         36          2  \n",
       "...      ...        ...  \n",
       "22617     28          2  \n",
       "22618     38          1  \n",
       "22619     42          1  \n",
       "22620     86          1  \n",
       "22621     19          2  \n",
       "\n",
       "[22620 rows x 4 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tuned = three_class.copy()\n",
    "fine_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 868,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        If this product was in petite  i would get the...\n",
       "1        Love this dress!  it's sooo pretty.  i happene...\n",
       "2        I had such high hopes for this dress and reall...\n",
       "3        I love  love  love this jumpsuit. it's fun  fl...\n",
       "4        This shirt is very flattering to all due to th...\n",
       "                               ...                        \n",
       "22617    I was very happy to snag this dress at such a ...\n",
       "22618    It reminds me of maternity clothes. soft  stre...\n",
       "22619    This fit well  but the top was very see throug...\n",
       "22620    I bought this dress for a wedding i have this ...\n",
       "22621    This dress in a lovely platinum is feminine an...\n",
       "Name: review_body, Length: 22620, dtype: object"
      ]
     },
     "execution_count": 868,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned = fine_tuned['review_body']\n",
    "tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>review_body</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>If this product was in petite  i would get the...</td>\n",
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       "      <td>This shirt is very flattering to all due to th...</td>\n",
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       "      <th>22617</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
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       "      <th>22618</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
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       "      <th>22619</th>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
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       "    <tr>\n",
       "      <th>22620</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
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       "    <tr>\n",
       "      <th>22621</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body\n",
       "0      If this product was in petite  i would get the...\n",
       "1      Love this dress!  it's sooo pretty.  i happene...\n",
       "2      I had such high hopes for this dress and reall...\n",
       "3      I love  love  love this jumpsuit. it's fun  fl...\n",
       "4      This shirt is very flattering to all due to th...\n",
       "...                                                  ...\n",
       "22617  I was very happy to snag this dress at such a ...\n",
       "22618  It reminds me of maternity clothes. soft  stre...\n",
       "22619  This fit well  but the top was very see throug...\n",
       "22620  I bought this dress for a wedding i have this ...\n",
       "22621  This dress in a lovely platinum is feminine an...\n",
       "\n",
       "[22620 rows x 1 columns]"
      ]
     },
     "execution_count": 862,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_df = pd.DataFrame(tuned)\n",
    "tuned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 869,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Love this dress!  it\\'s sooo pretty.  i happened to find it in a store  and i\\'m glad i did bc i never would have ordered it online bc it\\'s petite.  i bought a petite and am 5\\'8\".  i love the length on me- hits just a little below the knee.  would definitely be a true midi on someone who is truly petite.'"
      ]
     },
     "execution_count": 869,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_df['review_body'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 870,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 870,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test model on single data\n",
    "sentiment_score_2(tuned_df['review_body'][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 872,
   "metadata": {},
   "outputs": [],
   "source": [
    "#inference on full dataset\n",
    "#tuned_df['predicted_sentiment'] = tuned_df['review_body'].apply(lambda x: sentiment_score_2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 873,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Save prediction\n",
    "#tuned_df.to_csv('tuned_full_data_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load prediction csv\n",
    "tuned_full_pred = pd.read_csv('./predicted/tuned_full_data_pred.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>I had such high hopes for this dress and reall...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I love  love  love this jumpsuit. it's fun  fl...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>22615</th>\n",
       "      <td>I was very happy to snag this dress at such a ...</td>\n",
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       "      <th>22616</th>\n",
       "      <td>It reminds me of maternity clothes. soft  stre...</td>\n",
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       "      <th>22617</th>\n",
       "      <td>This fit well  but the top was very see throug...</td>\n",
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       "      <th>22618</th>\n",
       "      <td>I bought this dress for a wedding i have this ...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22619</th>\n",
       "      <td>This dress in a lovely platinum is feminine an...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22620 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             review_body  predicted_sentiment\n",
       "0      If this product was in petite  i would get the...                    2\n",
       "1      Love this dress!  it's sooo pretty.  i happene...                    2\n",
       "2      I had such high hopes for this dress and reall...                    1\n",
       "3      I love  love  love this jumpsuit. it's fun  fl...                    2\n",
       "4      This shirt is very flattering to all due to th...                    2\n",
       "...                                                  ...                  ...\n",
       "22615  I was very happy to snag this dress at such a ...                    2\n",
       "22616  It reminds me of maternity clothes. soft  stre...                    1\n",
       "22617  This fit well  but the top was very see throug...                    1\n",
       "22618  I bought this dress for a wedding i have this ...                    1\n",
       "22619  This dress in a lovely platinum is feminine an...                    2\n",
       "\n",
       "[22620 rows x 2 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_full_pred.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "tuned_full_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expected high since this dataset was used to fine-tune the distilbert model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Accuracy': 0.9702033598585322,\n",
       " 'Precision': 0.970677230610605,\n",
       " 'Recall': 0.9702033598585322,\n",
       " 'F1 Score': 0.9703566470175023,\n",
       " 'Confusion Matrix': array([[ 2154,   169,    46],\n",
       "        [  113,  2585,   125],\n",
       "        [   24,   197, 17207]], dtype=int64)}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original = fine_tuned['sentiment']\n",
    "ypred = tuned_full_pred['predicted_sentiment']\n",
    "tuned_full_data = evaluate_classification(original,ypred)\n",
    "tuned_full_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tuned_full_table</td>\n",
       "      <td>0.970203</td>\n",
       "      <td>0.970677</td>\n",
       "      <td>0.970203</td>\n",
       "      <td>0.970357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  tuned_full_table  0.970203   0.970677  0.970203  0.970357"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_full_table = get_metrics_as_dataframe(\"tuned_full_table\", tuned_full_data)\n",
    "tuned_full_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate metrics on test_dataset that has not been seen (train_test_split from google colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fit is perfect! very flattering. they are very...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gorgeous skirt. runs a bit big.  well made. pe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I bought this dress in the red option. was a l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I purchased this sweater in my usual xs  but f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I like this layer for fall for many reasons. f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>I bought this jacket in white specifically for...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>Every spring/summer i look forward to t.la's t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>Bought these online - sale  and am really happ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This top is soo pretty with a cool edge.  it l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>This will be my go-to shirt for fall! its a gr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2036 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                reviews\n",
       "0     Fit is perfect! very flattering. they are very...\n",
       "1     Gorgeous skirt. runs a bit big.  well made. pe...\n",
       "2     I bought this dress in the red option. was a l...\n",
       "3     I purchased this sweater in my usual xs  but f...\n",
       "4     I like this layer for fall for many reasons. f...\n",
       "...                                                 ...\n",
       "2031  I bought this jacket in white specifically for...\n",
       "2032  Every spring/summer i look forward to t.la's t...\n",
       "2033  Bought these online - sale  and am really happ...\n",
       "2034  This top is soo pretty with a cool edge.  it l...\n",
       "2035  This will be my go-to shirt for fall! its a gr...\n",
       "\n",
       "[2036 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv('./test data and label/test_dataset.csv')\n",
    "test_data.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "test_data.rename(columns={'0': 'reviews'}, inplace=True)\n",
    "test_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inference on testdata\n",
    "#test_data['predicted_sentiment'] = test_data['reviews'].apply(lambda x: sentiment_score_2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save prediction as csv\n",
    "#test_data.to_csv('./predicted/tuned_test_pred.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "      <th>predicted_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fit is perfect! very flattering. they are very...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gorgeous skirt. runs a bit big.  well made. pe...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I bought this dress in the red option. was a l...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I purchased this sweater in my usual xs  but f...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I like this layer for fall for many reasons. f...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>I bought this jacket in white specifically for...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>Every spring/summer i look forward to t.la's t...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>Bought these online - sale  and am really happ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This top is soo pretty with a cool edge.  it l...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>This will be my go-to shirt for fall! its a gr...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2036 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                reviews  predicted_sentiment\n",
       "0     Fit is perfect! very flattering. they are very...                    2\n",
       "1     Gorgeous skirt. runs a bit big.  well made. pe...                    2\n",
       "2     I bought this dress in the red option. was a l...                    2\n",
       "3     I purchased this sweater in my usual xs  but f...                    1\n",
       "4     I like this layer for fall for many reasons. f...                    2\n",
       "...                                                 ...                  ...\n",
       "2031  I bought this jacket in white specifically for...                    2\n",
       "2032  Every spring/summer i look forward to t.la's t...                    2\n",
       "2033  Bought these online - sale  and am really happ...                    2\n",
       "2034  This top is soo pretty with a cool edge.  it l...                    2\n",
       "2035  This will be my go-to shirt for fall! its a gr...                    2\n",
       "\n",
       "[2036 rows x 2 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load prediction\n",
    "tuned_test_pred = pd.read_csv('./predicted/tuned_test_pred.csv')\n",
    "tuned_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       2\n",
       "2       2\n",
       "3       1\n",
       "4       2\n",
       "       ..\n",
       "2031    2\n",
       "2032    2\n",
       "2033    2\n",
       "2034    2\n",
       "2035    2\n",
       "Name: predicted_sentiment, Length: 2036, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred = tuned_test_pred['predicted_sentiment']\n",
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ground_truth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2036 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ground_truth\n",
       "0                2\n",
       "1                2\n",
       "2                2\n",
       "3                0\n",
       "4                2\n",
       "...            ...\n",
       "2031             2\n",
       "2032             2\n",
       "2033             2\n",
       "2034             2\n",
       "2035             2\n",
       "\n",
       "[2036 rows x 1 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label = pd.read_csv('./test data and label/test_datalabels.csv')\n",
    "test_label.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "test_label.rename(columns={'0': 'ground_truth'}, inplace=True)\n",
    "test_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2\n",
       "1       2\n",
       "2       2\n",
       "3       0\n",
       "4       2\n",
       "       ..\n",
       "2031    2\n",
       "2032    2\n",
       "2033    2\n",
       "2034    2\n",
       "2035    2\n",
       "Name: ground_truth, Length: 2036, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yactual = test_label['ground_truth']\n",
    "yactual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evalute performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Accuracy': 0.8487229862475442,\n",
       " 'Precision': 0.8601183616966918,\n",
       " 'Recall': 0.8487229862475442,\n",
       " 'F1 Score': 0.852878312552522,\n",
       " 'Confusion Matrix': array([[ 114,   83,   15],\n",
       "        [  48,  141,   58],\n",
       "        [  13,   91, 1473]], dtype=int64)}"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_model = evaluate_classification(yactual,ypred)\n",
    "tuned_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot confusion matrix\n",
    "plot_confusion_matrix(yactual, ypred, class_names_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tuned_model_on_test_data</td>\n",
       "      <td>0.848723</td>\n",
       "      <td>0.860118</td>\n",
       "      <td>0.848723</td>\n",
       "      <td>0.852878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  tuned_model_on_test_data  0.848723   0.860118  0.848723  0.852878"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_test_table = get_metrics_as_dataframe(\"tuned_model_on_test_data\", tuned_model)\n",
    "tuned_test_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare fine-tuned model with a pretrained mode using testdata set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "##Load model\n",
    "model_name = \"cardiffnlp/twitter-roberta-base-sentiment-latest\"  # Replace with a valid model name from Hugging Face's model hub\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fit is perfect! very flattering. they are very...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gorgeous skirt. runs a bit big.  well made. pe...</td>\n",
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       "      <td>I bought this dress in the red option. was a l...</td>\n",
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       "      <td>I purchased this sweater in my usual xs  but f...</td>\n",
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       "      <th>4</th>\n",
       "      <td>I like this layer for fall for many reasons. f...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2031</th>\n",
       "      <td>I bought this jacket in white specifically for...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>Every spring/summer i look forward to t.la's t...</td>\n",
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       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>Bought these online - sale  and am really happ...</td>\n",
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       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This top is soo pretty with a cool edge.  it l...</td>\n",
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       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>This will be my go-to shirt for fall! its a gr...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2036 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                reviews\n",
       "0     Fit is perfect! very flattering. they are very...\n",
       "1     Gorgeous skirt. runs a bit big.  well made. pe...\n",
       "2     I bought this dress in the red option. was a l...\n",
       "3     I purchased this sweater in my usual xs  but f...\n",
       "4     I like this layer for fall for many reasons. f...\n",
       "...                                                 ...\n",
       "2031  I bought this jacket in white specifically for...\n",
       "2032  Every spring/summer i look forward to t.la's t...\n",
       "2033  Bought these online - sale  and am really happ...\n",
       "2034  This top is soo pretty with a cool edge.  it l...\n",
       "2035  This will be my go-to shirt for fall! its a gr...\n",
       "\n",
       "[2036 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load test data\n",
    "test_data_2 = pd.read_csv('./test data and label/test_dataset.csv')\n",
    "test_data_2.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "test_data_2.rename(columns={'0': 'reviews'}, inplace=True)\n",
    "test_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inference on testdata\n",
    "#test_data_2['predicted_sentiment'] = test_data_2['reviews'].apply(lambda x: sentiment_score_2(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save prediction\n",
    "test_data_2.to_csv('./predicted/pretrained_compare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load prediction\n",
    "pretrained_compare = pd.read_csv('./predicted/pretrained_compare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>I purchased this sweater in my usual xs  but f...</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>I like this layer for fall for many reasons. f...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2031</th>\n",
       "      <td>I bought this jacket in white specifically for...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>Every spring/summer i look forward to t.la's t...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>Bought these online - sale  and am really happ...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This top is soo pretty with a cool edge.  it l...</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>This will be my go-to shirt for fall! its a gr...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2036 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                reviews  predicted_sentiment\n",
       "0     Fit is perfect! very flattering. they are very...                    2\n",
       "1     Gorgeous skirt. runs a bit big.  well made. pe...                    2\n",
       "2     I bought this dress in the red option. was a l...                    2\n",
       "3     I purchased this sweater in my usual xs  but f...                    0\n",
       "4     I like this layer for fall for many reasons. f...                    2\n",
       "...                                                 ...                  ...\n",
       "2031  I bought this jacket in white specifically for...                    2\n",
       "2032  Every spring/summer i look forward to t.la's t...                    2\n",
       "2033  Bought these online - sale  and am really happ...                    2\n",
       "2034  This top is soo pretty with a cool edge.  it l...                    2\n",
       "2035  This will be my go-to shirt for fall! its a gr...                    2\n",
       "\n",
       "[2036 rows x 2 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ground_truth</th>\n",
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       "      <th>2031</th>\n",
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       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2035</th>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "      ground_truth\n",
       "0                2\n",
       "1                2\n",
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       "4                2\n",
       "...            ...\n",
       "2031             2\n",
       "2032             2\n",
       "2033             2\n",
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       "2035             2\n",
       "\n",
       "[2036 rows x 1 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load test labels\n",
    "test_label_2 = pd.read_csv('./test data and label/test_datalabels.csv')\n",
    "test_label_2.drop('Unnamed: 0', axis=1, inplace=True)\n",
    "test_label_2.rename(columns={'0': 'ground_truth'}, inplace=True)\n",
    "test_label_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yactual and ypred\n",
    "ypred = test_data_2['predicted_sentiment']\n",
    "yactual = test_label_2['ground_truth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Accuracy': 0.7892927308447937,\n",
       " 'Precision': 0.7723245915406566,\n",
       " 'Recall': 0.7892927308447937,\n",
       " 'F1 Score': 0.7736442424745588,\n",
       " 'Confusion Matrix': array([[ 149,   12,   51],\n",
       "        [ 129,   21,   97],\n",
       "        [  76,   64, 1437]], dtype=int64)}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate performance\n",
    "pretrained_model_for_comparison = evaluate_classification(yactual, ypred)\n",
    "pretrained_model_for_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pretrained_model_on_test_data</td>\n",
       "      <td>0.789293</td>\n",
       "      <td>0.772325</td>\n",
       "      <td>0.789293</td>\n",
       "      <td>0.773644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  pretrained_model_on_test_data  0.789293   0.772325  0.789293  0.773644"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert into dataframe\n",
    "pretrained_model_comparison_table = get_metrics_as_dataframe('pretrained_model_on_test_data', pretrained_model_for_comparison)\n",
    "pretrained_model_comparison_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BERT base (5 classes)</td>\n",
       "      <td>0.566092</td>\n",
       "      <td>0.653298</td>\n",
       "      <td>0.566092</td>\n",
       "      <td>0.592059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Three_class_table</td>\n",
       "      <td>0.793413</td>\n",
       "      <td>0.771015</td>\n",
       "      <td>0.793413</td>\n",
       "      <td>0.776169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>binary classification</td>\n",
       "      <td>0.837224</td>\n",
       "      <td>0.849638</td>\n",
       "      <td>0.837224</td>\n",
       "      <td>0.841782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  BERT base (5 classes)  0.566092   0.653298  0.566092  0.592059\n",
       "1      Three_class_table  0.793413   0.771015  0.793413  0.776169\n",
       "2  binary classification  0.837224   0.849638  0.837224  0.841782"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparison between pretrained models using different class labels\n",
    "combined_tables = pd.concat([five_class_table, three_class_table, binary_table], axis=0)\n",
    "combined_tables = combined_tables.reset_index(drop=True)\n",
    "combined_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pretrained_model_on_test_data</td>\n",
       "      <td>0.789293</td>\n",
       "      <td>0.772325</td>\n",
       "      <td>0.789293</td>\n",
       "      <td>0.773644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tuned_model_on_test_data</td>\n",
       "      <td>0.848723</td>\n",
       "      <td>0.860118</td>\n",
       "      <td>0.848723</td>\n",
       "      <td>0.852878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Model  Accuracy  Precision    Recall  F1 Score\n",
       "0  pretrained_model_on_test_data  0.789293   0.772325  0.789293  0.773644\n",
       "1       tuned_model_on_test_data  0.848723   0.860118  0.848723  0.852878"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comparison table between fine-tuned model for 3 classes against a pretrained-model using datasets with 3 classes\n",
    "combined_table_2 = pd.concat([pretrained_model_comparison_table, tuned_test_table,], axis=0)\n",
    "combined_table_2 = combined_table_2.reset_index(drop=True)\n",
    "combined_table_2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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