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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "5ede69a1",
-   "metadata": {},
-   "source": [
-    "# Classification Challenge using CatBoost\n",
-    "\n",
-    "## INF2179 Fall 2021\n",
-    "### Hamid Yuksel\n",
-    "\n",
-    "This submission uses [CatBoost](https://catboost.ai/).\n",
-    "CatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.\n",
-    "\n",
-    "<img src=\"https://cdn.britannica.com/39/7139-050-A88818BB/Himalayan-chocolate-point.jpg\"\n",
-    "     alt=\"Markdown Monster icon\"\n",
-    "     style=\"float: left; margin-right: 10px;\" />\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ee82451e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: catboost in /Users/yuksel/.local/lib/python3.8/site-packages (1.0.3)\n",
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-      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.2.1 is available.\n",
-      "You should consider upgrading via the '/Users/yuksel/opt/anaconda3/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: pycparser in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (2.20)\n",
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-      "Requirement already satisfied: webencodings in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets) (0.5.1)\n",
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-      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.2.1 is available.\n",
-      "You should consider upgrading via the '/Users/yuksel/opt/anaconda3/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Enabling notebook extension jupyter-js-widgets/extension...\n",
-      "      - Validating: \u001b[32mOK\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Installing and Importing required libraries\n",
-    "! pip3 install --user catboost\n",
-    "! pip3 install --user ipywidgets\n",
-    "! jupyter nbextension enable --py widgetsnbextension\n",
-    "\n",
-    "import pandas as pd \n",
-    "import numpy as np\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.metrics import accuracy_score \n",
-    "from catboost import Pool, CatBoostClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "1853f85d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Rock       24486\n",
-       "Pop        16251\n",
-       "Hip Hop     9263\n",
-       "unknown     5000\n",
-       "Name: Genre, dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Reading data\n",
-    "df = pd.read_csv('data.csv')\n",
-    "\n",
-    "# Splitting\n",
-    "training = df.head(50000)\n",
-    "holdout_set = training.sample(5000, random_state=1) # pick 5000 observations randomly\n",
-    "training = training.drop(holdout_set.index) # Remove holdout from training data\n",
-    "testing = df.tail(5000)\n",
-    "\n",
-    "#Looking at counts per genre\n",
-    "df['Genre'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "f0a20f7a",
-   "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>Lyric</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>50000</th>\n",
-       "      <td>Feels so good,. Feels so good,. Feels so good ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50001</th>\n",
-       "      <td>Shadow of a doubt. I heard your heart,. you he...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50002</th>\n",
-       "      <td>Slaves. Hebrews born to serve to the pharaoh. ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50003</th>\n",
-       "      <td>You've been picked and it's over. What's the c...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50004</th>\n",
-       "      <td>Magic happens. But only if you are open to the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54995</th>\n",
-       "      <td>I can't believe what you did to me. Down on my...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54996</th>\n",
-       "      <td>Have all the songs been written?. Have all the...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54997</th>\n",
-       "      <td>Everything you do you do so right. The clothes...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54998</th>\n",
-       "      <td>(trecho). (Rule Number Two. Understanding what...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54999</th>\n",
-       "      <td>As fall rides off in the Sunset. I sweep the S...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5000 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                   Lyric\n",
-       "50000  Feels so good,. Feels so good,. Feels so good ...\n",
-       "50001  Shadow of a doubt. I heard your heart,. you he...\n",
-       "50002  Slaves. Hebrews born to serve to the pharaoh. ...\n",
-       "50003  You've been picked and it's over. What's the c...\n",
-       "50004  Magic happens. But only if you are open to the...\n",
-       "...                                                  ...\n",
-       "54995  I can't believe what you did to me. Down on my...\n",
-       "54996  Have all the songs been written?. Have all the...\n",
-       "54997  Everything you do you do so right. The clothes...\n",
-       "54998  (trecho). (Rule Number Two. Understanding what...\n",
-       "54999  As fall rides off in the Sunset. I sweep the S...\n",
-       "\n",
-       "[5000 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Splitting training/testing set to feature (X) and labels (y)\n",
-    "train_y = training.Genre\n",
-    "train_X = training.drop('Genre', axis=1)\n",
-    "\n",
-    "test_X = testing.drop('Genre', axis=1)\n",
-    "\n",
-    "test_X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c8eb420",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build a classifier\n",
-    "text_features = ['Lyric']\n",
-    "\n",
-    "\n",
-    "train_dataset = Pool(data=train_X,\n",
-    "                     label=train_y,\n",
-    "                     text_features=text_features)\n",
-    "\n",
-    "model = CatBoostClassifier(iterations=1000,\n",
-    "                           learning_rate=1,\n",
-    "                           depth=5,\n",
-    "                           loss_function='MultiClass')\n",
-    "\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f770a8f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Estimate accuracy\n",
-    "pred = model.predict(holdout_set.drop('Genre',axis=1))\n",
-    "estimated_accuracy = accuracy_score(holdout_set['Genre'], pred)\n",
-    "print(estimated_accuracy)\n",
-    "pd.Series(estimated_accuracy).to_csv('ea.csv', index=False, header=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "035f58c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Pop' 'Rock' 'Rock' ... 'Rock' 'Pop' 'Rock']\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Predict testing set\n",
-    "pred = model.predict(test_X)\n",
-    "print(pred.flatten())\n",
-    "pred = pd.Series(pred.flatten()).to_csv('pred.csv', index=False, header=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "2137c958",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array(['Hip Hop', 'Pop', 'Rock'], dtype=object), array([ 802, 1307, 2891]))"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# to check number of instances of each genre in pred\n",
-    "np.unique(model.predict(test_X), return_counts=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "83aa22c4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

2179-proj.ipynb → .ipynb_checkpoints/model-checkpoint.ipynb

@@ -477,13 +477,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "83aa22c4",
    "metadata": {},
    "outputs": [],
    "source": [
     "import pickle\n",
-    "pickle.dump(model, open('2179-proj.pickle', 'wb'))"
+    "pickle.dump(model, open('model.pickle', 'wb'))"
    ]
   },
   {

2179-proj.pickle

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8f64ecdff3d7935ef3bf5bb870fad3e4c619c20312e70bbb79455855cfda1204
-size 7738041

index.py

@@ -1,25 +0,0 @@
-from flask import Flask, request, redirect, url_for, flash, jsonify
-import numpy as np
-import pickle as p
-import json
-
-
-app = Flask(__name__)
-
-
-@app.route("/")
-def home():
-    return "<h1>Lyrics Classifier</h1>"
-
-@app.route('/api/', methods=['POST'])
-def makecalc():
-    data = request.get_json()
-    prediction = np.array2string(model.predict(data))
-
-    return jsonify(prediction)
-
-
-if __name__ == '__main__':
-    modelfile = '2179-proj.pickle'
-    model = p.load(open(modelfile, 'rb'))
-    app.run(debug=True, host='0.0.0.0')