CAPSTONE.ipynb

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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import seaborn as sns\n"
      ]
    },
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      "source": [
        "df = pd.read_csv('https://res.cloudinary.com/daktccmqn/raw/upload/v1684995027/Crop_recommendation_dfltdn.csv')\n",
        "df.head()"
      ],
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            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
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            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
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      "source": [
        "# Return information about the datatype,\n",
        "# NULL type of the columns of CSV file\n",
        "df.info()\n"
      ],
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        "id": "GMLCsZU9wlO0",
        "outputId": "b85ce070-0645-455d-dec1-5252d8080209"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2200 entries, 0 to 2199\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   N            2200 non-null   int64  \n",
            " 1   P            2200 non-null   int64  \n",
            " 2   K            2200 non-null   int64  \n",
            " 3   temperature  2200 non-null   float64\n",
            " 4   humidity     2200 non-null   float64\n",
            " 5   ph           2200 non-null   float64\n",
            " 6   rainfall     2200 non-null   float64\n",
            " 7   label        2200 non-null   object \n",
            "dtypes: float64(4), int64(3), object(1)\n",
            "memory usage: 137.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = df['label'].unique()\n",
        "print(labels)"
      ],
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        },
        "id": "K6CO5erNwpnS",
        "outputId": "41bfcd80-d5e8-411e-d6e6-a430e8c366bf"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['rice' 'maize' 'chickpea' 'kidneybeans' 'pigeonpeas' 'mothbeans'\n",
            " 'mungbean' 'blackgram' 'lentil' 'pomegranate' 'banana' 'mango' 'grapes'\n",
            " 'watermelon' 'muskmelon' 'apple' 'orange' 'papaya' 'coconut' 'cotton'\n",
            " 'jute' 'coffee']\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "def convert_label_encode(x) :\n",
        "  if x == 'rice':\n",
        "    return 0\n",
        "  elif x == 'maize':\n",
        "    return 1\n",
        "  elif x == 'chickpea':\n",
        "    return 2\n",
        "  elif x == 'kidneybeans': \n",
        "    return 3\n",
        "  elif x == 'pigeonpeas':\n",
        "    return 4\n",
        "  elif x == 'mothbeans':\n",
        "    return 5\n",
        "  elif x == 'mungbean':\n",
        "    return 6\n",
        "  elif x == 'blackgram':\n",
        "    return 7\n",
        "  elif x == 'lentil':\n",
        "    return 8\n",
        "  elif x == 'pomegranate':\n",
        "    return 9\n",
        "  elif x == 'banana':\n",
        "    return 10\n",
        "  elif x == 'mango':\n",
        "    return 11\n",
        "  elif x == 'grapes':\n",
        "    return 12\n",
        "  elif x == 'watermelon':\n",
        "    return 13\n",
        "  elif x == 'muskmelon':\n",
        "    return 14\n",
        "  elif x == 'apple':\n",
        "    return 15\n",
        "  elif x == 'orange':\n",
        "    return 16\n",
        "  elif x == 'papaya':\n",
        "    return 17\n",
        "  elif x == 'coconut':\n",
        "    return 18\n",
        "  elif x == 'cotton':\n",
        "    return 19\n",
        "  elif x == 'jute':\n",
        "    return 20\n",
        "  elif x == 'coffee':\n",
        "    return 21\n"
      ],
      "metadata": {
        "id": "TPOLkO8zFtLF"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features = df.copy()\n",
        "targets = features.pop('label')"
      ],
      "metadata": {
        "id": "MUOmtA1Vh241"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train, test = train_test_split(df, test_size=0.2)\n",
        "train, val = train_test_split(train, test_size=0.2)\n",
        "print(len(train), 'train examples')\n",
        "print(len(val), 'validation examples')\n",
        "print(len(test), 'test examples')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jjzNQSzyH9Ce",
        "outputId": "a7e81ab1-bff1-4bc2-94c1-e98d76f76a78"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1408 train examples\n",
            "352 validation examples\n",
            "440 test examples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
        "    dataframe = dataframe.copy()\n",
        "    dataframe['label'] =  dataframe['label'].map(convert_label_encode)\n",
        "    labels = dataframe.pop('label') \n",
        "    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))#  YOUR CODE HERE\n",
        "    \n",
        "    if shuffle:\n",
        "        ds = ds.shuffle(100) \n",
        "    ds = ds.batch(batch_size)\n",
        "    return ds"
      ],
      "metadata": {
        "id": "FCtfVAfGHk8l"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 5 # A small batch sized is used for demonstration purposes\n",
        "train_ds = df_to_dataset(train, batch_size=batch_size)\n",
        "val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\n",
        "test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)"
      ],
      "metadata": {
        "id": "8nelhdcIH5VC"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a symbolic input\n",
        "input = tf.keras.Input(shape=(), dtype=tf.float32)\n",
        "\n",
        "# Perform a calculation using the input\n",
        "result = 2*input + 1\n",
        "\n",
        "# the result doesn't have a value\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06sOS8yZibfT",
        "outputId": "53a4032c-36db-437c-be0c-c1191b2e0373"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None,) dtype=float32 (created by layer 'tf.__operators__.add')>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = {}\n",
        "\n",
        "for name, column in features.items():\n",
        "  dtype = column.dtype\n",
        "  if dtype == object:\n",
        "    dtype = tf.int64\n",
        "  else:\n",
        "    dtype = tf.float64\n",
        "\n",
        "  inputs[name] = tf.keras.Input(shape=(1,), name=name, dtype=dtype)\n",
        "\n",
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APNYHnwfpTrk",
        "outputId": "3652d216-7364-42d5-e3b5-3dbec90f0fd5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'N': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'N')>,\n",
              " 'P': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'P')>,\n",
              " 'K': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'K')>,\n",
              " 'temperature': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'temperature')>,\n",
              " 'humidity': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'humidity')>,\n",
              " 'ph': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'ph')>,\n",
              " 'rainfall': <KerasTensor: shape=(None, 1) dtype=float64 (created by layer 'rainfall')>}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_inputs = {name:input for name,input in inputs.items()\n",
        "                  if input.dtype==tf.float64}\n",
        "\n",
        "x = layers.Concatenate()(list(numeric_inputs.values()))\n",
        "norm = layers.Normalization()\n",
        "norm.adapt(np.array(df[numeric_inputs.keys()]))\n",
        "all_numeric_inputs = norm(x)\n",
        "\n",
        "all_numeric_inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvPOYnlTpbuN",
        "outputId": "5c6683cf-f908-4138-8dd5-fc90a20e2695"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 7) dtype=float32 (created by layer 'normalization')>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_inputs = [all_numeric_inputs]"
      ],
      "metadata": {
        "id": "3tzP7-8ZpyzF"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name, input in inputs.items():\n",
        "  if input.dtype == tf.float64:\n",
        "    continue\n",
        "  \n",
        "  lookup = layers.StringLookup(vocabulary=np.unique(features[name]))\n",
        "  one_hot = layers.CategoryEncoding(num_tokens=lookup.vocabulary_size())\n",
        "\n",
        "  x = lookup(input)\n",
        "  x = one_hot(x)\n",
        "  preprocessed_inputs.append(x)"
      ],
      "metadata": {
        "id": "YXWUAvHFp4nt"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessed_inputs_cat = layers.Concatenate()(preprocessed_inputs)\n",
        "\n",
        "crop_preprocessing = tf.keras.Model(inputs, preprocessed_inputs_cat)\n",
        "\n",
        "tf.keras.utils.plot_model(model = crop_preprocessing , rankdir=\"LR\", dpi=72, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "SztbzZ-sqWte",
        "outputId": "b788259d-a7a4-49c8-8810-8246b933d408"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_dict = {name: np.array(value) \n",
        "                         for name, value in features.items()}\n",
        "print(features_dict)"
      ],
      "metadata": {
        "id": "wB00XCIUqcfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e459429-e194-4ba4-9c08-be96d155a2d9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'N': array([ 90,  85,  60, ..., 118, 117, 104]), 'P': array([42, 58, 55, ..., 33, 32, 18]), 'K': array([43, 41, 44, ..., 30, 34, 30]), 'temperature': array([20.87974371, 21.77046169, 23.00445915, ..., 24.13179691,\n",
            "       26.2724184 , 23.60301571]), 'humidity': array([82.00274423, 80.31964408, 82.3207629 , ..., 67.22512329,\n",
            "       52.12739421, 60.39647474]), 'ph': array([6.50298529, 7.03809636, 7.84020714, ..., 6.36260785, 6.75879255,\n",
            "       6.77983261]), 'rainfall': array([202.9355362, 226.6555374, 263.9642476, ..., 173.3228386,\n",
            "       127.1752928, 140.9370415])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IQSeZHotFlkm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "features_dict = {name:values[:1] for name, values in features_dict.items()}\n",
        "crop_preprocessing(features_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBtlGA4-qoAF",
        "outputId": "ba8517e4-9ad3-4222-e806-f4847c854bf4"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 7), dtype=float32, numpy=\n",
              "array([[ 1.0687971 , -0.34455115, -0.10168769, -0.9355875 ,  0.47266635,\n",
              "         0.043302  ,  1.8103603 ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def crop_model(preprocessing_head, inputs):\n",
        "  body = tf.keras.Sequential([\n",
        "    layers.Dense(64),\n",
        "    layers.Dense(22),\n",
        "    layers.Dense(22)\n",
        "  ])\n",
        "\n",
        "  preprocessed_inputs = preprocessing_head(inputs)\n",
        "  result = body(preprocessed_inputs)\n",
        "  model = tf.keras.Model(inputs, result)\n",
        "\n",
        "  model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "  return model\n"
      ],
      "metadata": {
        "id": "j4d7xlWxqt0d"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crop_model = crop_model(crop_preprocessing, inputs)\n",
        "crop_model.fit(x=features_dict, y=labels, epochs=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "2b5qje_VsmLQ",
        "outputId": "39e54945-003e-4ece-e3c2-bdf535546cd2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-0818c9e3669d>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcrop_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrop_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrop_preprocessing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcrop_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeatures_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    101\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Failed to convert a NumPy array to a Tensor (Unsupported object type int)."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D1gVfwJ61ejz"
      },
      "outputs": [],
      "source": []
    }
  ]
}