SMS Text Classification.ipynb

{
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      "2.14.0-dev20230622\n"
     ]
    }
   ],
   "source": [
    "# import libraries\n",
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  !pip install tf-nightly\n",
    "except Exception:\n",
    "  pass\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "!pip install tensorflow-datasets\n",
    "import tensorflow_datasets as tfds\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lMHwYXHXCar3",
    "outputId": "207cad64-0df8-4610-d538-5807b5811a53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-22 15:37:24--  https://cdn.freecodecamp.org/project-data/sms/train-data.tsv\n",
      "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.2.33, 104.26.3.33, ...\n",
      "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 358233 (350K) [text/tab-separated-values]\n",
      "Saving to: ‘train-data.tsv.2’\n",
      "\n",
      "train-data.tsv.2    100%[===================>] 349.84K  --.-KB/s    in 0.01s   \n",
      "\n",
      "2023-06-22 15:37:24 (24.6 MB/s) - ‘train-data.tsv.2’ saved [358233/358233]\n",
      "\n",
      "--2023-06-22 15:37:24--  https://cdn.freecodecamp.org/project-data/sms/valid-data.tsv\n",
      "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.2.33, 104.26.3.33, ...\n",
      "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 118774 (116K) [text/tab-separated-values]\n",
      "Saving to: ‘valid-data.tsv.2’\n",
      "\n",
      "valid-data.tsv.2    100%[===================>] 115.99K  --.-KB/s    in 0.01s   \n",
      "\n",
      "2023-06-22 15:37:24 (11.4 MB/s) - ‘valid-data.tsv.2’ saved [118774/118774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# get data files\n",
    "!wget https://cdn.freecodecamp.org/project-data/sms/train-data.tsv\n",
    "!wget https://cdn.freecodecamp.org/project-data/sms/valid-data.tsv\n",
    "\n",
    "train_file_path = \"train-data.tsv\"\n",
    "test_file_path = \"valid-data.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "g_h508FEClxO"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "from tensorflow import keras\n",
    "# import tensorflow_datasets as tfds\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "tfds.disable_progress_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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     "height": 206
    },
    "id": "zOMKywn4zReN",
    "outputId": "ac9ac2a4-7835-4283-f471-014253954fdc"
   },
   "outputs": [
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       "  type                                                msg\n",
       "0  ham  i am in hospital da. . i will return home in e...\n",
       "1  ham         not much, just some textin'. how bout you?\n",
       "2  ham  i probably won't eat at all today. i think i'm...\n",
       "3  ham  don‘t give a flying monkeys wot they think and...\n",
       "4  ham                                who are you seeing?"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   "source": [
    "train_file_path = \"train-data.tsv\"\n",
    "test_file_path = \"valid-data.tsv\"\n",
    "\n",
    "train_df = pd.read_csv(test_file_path, sep=\"\\t\", header=None, names=[\"type\", \"msg\"])\n",
    "train_df.dropna()\n",
    "train_df.head()"
   ]
  },
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       "  type                                                msg\n",
       "0  ham  i am in hospital da. . i will return home in e...\n",
       "1  ham         not much, just some textin'. how bout you?\n",
       "2  ham  i probably won't eat at all today. i think i'm...\n",
       "3  ham  don‘t give a flying monkeys wot they think and...\n",
       "4  ham                                who are you seeing?"
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     },
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    "test_df = pd.read_csv(test_file_path, sep=\"\\t\", header=None, names=[\"type\", \"msg\"])\n",
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    "train_df.head()"
   ]
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  {
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   "execution_count": 6,
   "metadata": {
    "id": "5xWbxCC_IYSO"
   },
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    "train_df[\"type\"] = pd.factorize(train_df[\"type\"])[0]\n",
    "test_df[\"type\"] = pd.factorize(test_df[\"type\"])[0]"
   ]
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       "   type                                                msg\n",
       "0     0  i am in hospital da. . i will return home in e...\n",
       "1     0         not much, just some textin'. how bout you?\n",
       "2     0  i probably won't eat at all today. i think i'm...\n",
       "3     0  don‘t give a flying monkeys wot they think and...\n",
       "4     0                                who are you seeing?"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "train_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "SLIqvkAIJDjw"
   },
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   "source": [
    "train_labels =  train_df[\"type\"].values\n",
    "train_ds = tf.data.Dataset.from_tensor_slices(\n",
    "    (train_df[\"msg\"].values, train_labels)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "142dPxrDb1ZB",
    "outputId": "22725790-5038-442c-e877-5150dc2645e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorSpec(shape=(), dtype=tf.string, name=None),\n",
       " TensorSpec(shape=(), dtype=tf.int64, name=None))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels =  test_df[\"type\"].values\n",
    "test_ds = tf.data.Dataset.from_tensor_slices(\n",
    "    (test_df[\"msg\"].values, test_labels)\n",
    ")\n",
    "test_ds.element_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "HJE5da1MbZPn"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 100\n",
    "BATCH_SIZE = 32\n",
    "train_ds = train_ds.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
    "test_ds = test_ds.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "pOJCo3nMcCY_"
   },
   "outputs": [],
   "source": [
    "vec = TextVectorization(\n",
    "    output_mode='int',\n",
    "    max_tokens=1000,\n",
    "    output_sequence_length=1000,\n",
    ")\n",
    "\n",
    "vec.adapt(train_ds.map(lambda text, label: text))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "g2Kl2QD9RdXR",
    "outputId": "18a1bcb6-822f-46e3-df8b-29cc48750e30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', '[UNK]', 'i', 'to', 'you', 'a', 'the', 'u', 'and', 'is', 'in',\n",
       "       'me', 'my', 'your', 'for', 'have', 'it', 'of', 'call', 'that'],\n",
       "      dtype='<U16')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab = np.array(vec.get_vocabulary())\n",
    "vocab[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YEUazxbYRfrr",
    "outputId": "72ce4df5-a9c6-4050-c29a-450bc4866eb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " text_vectorization (TextVe  (None, 1000)              0         \n",
      " ctorization)                                                    \n",
      "                                                                 \n",
      " embedding (Embedding)       (None, 1000, 64)          64000     \n",
      "                                                                 \n",
      " bidirectional (Bidirection  (None, 1000, 128)         66048     \n",
      " al)                                                             \n",
      "                                                                 \n",
      " bidirectional_1 (Bidirecti  (None, 64)                41216     \n",
      " onal)                                                           \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 175489 (685.50 KB)\n",
      "Trainable params: 175489 (685.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "model = tf.keras.Sequential([\n",
    "    vec,\n",
    "    tf.keras.layers.Embedding(\n",
    "        len(vec.get_vocabulary()),\n",
    "        64,\n",
    "        mask_zero=True,\n",
    "    ),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64,  return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.summary()\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.Adam(1e-4),\n",
    "    metrics=['accuracy'],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OshR4BkHRj6A",
    "outputId": "6f3ddcb8-7faa-4c99-ebc2-ec60ecdfed11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "44/44 [==============================] - 44s 287ms/step - loss: 0.6768 - accuracy: 0.8657 - val_loss: 0.6526 - val_accuracy: 0.8604\n",
      "Epoch 2/10\n",
      "44/44 [==============================] - 5s 112ms/step - loss: 0.6035 - accuracy: 0.8657 - val_loss: 0.5453 - val_accuracy: 0.8604\n",
      "Epoch 3/10\n",
      "44/44 [==============================] - 4s 85ms/step - loss: 0.5042 - accuracy: 0.8657 - val_loss: 0.4774 - val_accuracy: 0.8604\n",
      "Epoch 4/10\n",
      "44/44 [==============================] - 5s 113ms/step - loss: 0.4417 - accuracy: 0.8657 - val_loss: 0.4044 - val_accuracy: 0.8604\n",
      "Epoch 5/10\n",
      "44/44 [==============================] - 2s 53ms/step - loss: 0.3482 - accuracy: 0.8657 - val_loss: 0.2793 - val_accuracy: 0.8604\n",
      "Epoch 6/10\n",
      "44/44 [==============================] - 3s 61ms/step - loss: 0.2108 - accuracy: 0.8858 - val_loss: 0.1468 - val_accuracy: 0.9490\n",
      "Epoch 7/10\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.1157 - accuracy: 0.9705 - val_loss: 0.0869 - val_accuracy: 0.9823\n",
      "Epoch 8/10\n",
      "44/44 [==============================] - 3s 66ms/step - loss: 0.0773 - accuracy: 0.9813 - val_loss: 0.0626 - val_accuracy: 0.9833\n",
      "Epoch 9/10\n",
      "44/44 [==============================] - 4s 85ms/step - loss: 0.0577 - accuracy: 0.9856 - val_loss: 0.0511 - val_accuracy: 0.9844\n",
      "Epoch 10/10\n",
      "44/44 [==============================] - 2s 55ms/step - loss: 0.0515 - accuracy: 0.9864 - val_loss: 0.0415 - val_accuracy: 0.9906\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_ds,\n",
    "    validation_data=test_ds,\n",
    "    validation_steps=30,\n",
    "    epochs=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "yVxRWm6JRrBX"
   },
   "outputs": [],
   "source": [
    "def plot_graphs(h, metric):\n",
    "    plt.plot(h.history[metric])\n",
    "    plt.plot(h.history['val_'+metric])\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([metric, 'val_'+metric])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 718
    },
    "id": "xK7M9ZWHR1ZB",
    "outputId": "0622e3bf-c4ff-4fbb-fcf8-a571456ea534"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.7085518540814519)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plot_graphs(history, 'accuracy')\n",
    "plt.ylim(None, 1)\n",
    "plt.subplot(1, 2, 2)\n",
    "plot_graphs(history, 'loss')\n",
    "plt.ylim(0, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nZpPUrgGRwyt",
    "outputId": "2b6d34f7-6d2b-4094-dae8-f71361949443"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6767897009849548, 0.6034532189369202, 0.5042389035224915, 0.4417330324649811, 0.3481524586677551, 0.21084800362586975, 0.11567652970552444, 0.07732830196619034, 0.05767146497964859, 0.051452625542879105]\n",
      "[0.6525961756706238, 0.5452825427055359, 0.4774115979671478, 0.40444251894950867, 0.2793481647968292, 0.14683164656162262, 0.08693816512823105, 0.06259531527757645, 0.05114849656820297, 0.04154663905501366]\n",
      "[0.8656609058380127, 0.8656609058380127, 0.8656609058380127, 0.8656609058380127, 0.8656609058380127, 0.8857758641242981, 0.9705459475517273, 0.9813218116760254, 0.9856321811676025, 0.9863505959510803]\n",
      "[0.8604166507720947, 0.8604166507720947, 0.8604166507720947, 0.8604166507720947, 0.8604166507720947, 0.9489583373069763, 0.9822916388511658, 0.9833333492279053, 0.984375, 0.9906250238418579]\n"
     ]
    }
   ],
   "source": [
    "h = history.history\n",
    "print(h['loss'])\n",
    "print(h['val_loss'])\n",
    "print(h['accuracy'])\n",
    "print(h['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ss7bYD4AU6kM",
    "outputId": "e4bf7823-652d-401c-8976-4dc7e3012726"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 5s 5s/step\n",
      "[[-3.9512646]]\n",
      "[-3.9512646, 'ham']\n"
     ]
    }
   ],
   "source": [
    "# function to predict messages based on model\n",
    "# (should return list containing prediction and label, ex. [0.008318834938108921, 'ham'])\n",
    "def predict_message(pred_text):\n",
    "    ps = model.predict([pred_text])\n",
    "    print(ps)\n",
    "    p = ps[0][0]\n",
    "    return [p, \"ham\" if p <0.5 else \"spam\"]\n",
    "\n",
    "pred_text = \"how are you doing today?\"\n",
    "\n",
    "prediction = predict_message(pred_text)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Edj-_xj6VCRh",
    "outputId": "2f30351b-d6c8-41f9-a954-f14d9fe0f4b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 139ms/step\n",
      "[[-3.9512646]]\n",
      "[-3.9512646, 'ham']\n",
      "1/1 [==============================] - 0s 144ms/step\n",
      "[[0.9769191]]\n",
      "[0.9769191, 'spam']\n",
      "1/1 [==============================] - 0s 144ms/step\n",
      "[[-8.950563]]\n",
      "[-8.950563, 'ham']\n",
      "1/1 [==============================] - 0s 187ms/step\n",
      "[[1.5406302]]\n",
      "[1.5406302, 'spam']\n",
      "1/1 [==============================] - 0s 256ms/step\n",
      "[[2.8592432]]\n",
      "[2.8592432, 'spam']\n",
      "1/1 [==============================] - 0s 132ms/step\n",
      "[[-7.866118]]\n",
      "[-7.866118, 'ham']\n",
      "1/1 [==============================] - 0s 141ms/step\n",
      "[[-8.211721]]\n",
      "[-8.211721, 'ham']\n",
      "You passed the challenge. Great job!\n"
     ]
    }
   ],
   "source": [
    "# Run this cell to test your function and model. Do not modify contents.\n",
    "def test_predictions():\n",
    "  test_messages = [\"how are you doing today\",\n",
    "                   \"sale today! to stop texts call 98912460 4\",\n",
    "                   \"i dont want to go. can we try it a different day? available sat\",\n",
    "                   \"our new mobile video service is live. just install on your phone to start watching.\",\n",
    "                   \"you have won £1000 cash! call to claim your prize.\",\n",
    "                   \"i'll bring it tomorrow. don't forget the milk.\",\n",
    "                   \"wow, is your arm alright. that happened to me one time too\"\n",
    "                  ]\n",
    "\n",
    "  test_answers = [\"ham\", \"spam\", \"ham\", \"spam\", \"spam\", \"ham\", \"ham\"]\n",
    "  passed = True\n",
    "\n",
    "  for msg, ans in zip(test_messages, test_answers):\n",
    "    prediction = predict_message(msg)\n",
    "    print(prediction)\n",
    "    if prediction[1] != ans:\n",
    "      passed = False\n",
    "\n",
    "  if passed:\n",
    "    print(\"You passed the challenge. Great job!\")\n",
    "  else:\n",
    "    print(\"You haven't passed yet. Keep trying.\")\n",
    "\n",
    "test_predictions()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}