Colab_multiclass_in_deep_learning.ipynb

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    {
      "cell_type": "markdown",
      "id": "8a552be4",
      "metadata": {
        "id": "8a552be4"
      },
      "source": [
        "# Multiclass Prediction in Deep Learning Models"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35602e50",
      "metadata": {
        "id": "35602e50"
      },
      "source": [
        "## Read in the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "038c919a",
      "metadata": {
        "id": "038c919a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "292c574b",
      "metadata": {
        "id": "292c574b"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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              "// See the License for the specific language governing permissions and\n",
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          "name": "stdout",
          "text": [
            "Saving meet_or_beat.csv to meet_or_beat.csv\n"
          ]
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      "id": "322048df",
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      "source": [
        "df = pd.read_csv('meet_or_beat.csv')"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "(71968, 5)"
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      "source": [
        "df.shape"
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              "    EPS  forecasted_eps  noOfEsts  after_total_returns  before_total_returns\n",
              "0  2.01            1.67      11.0             0.051444              0.018585\n",
              "1  0.17            0.19       6.0             0.112955             -0.000510\n",
              "2 -0.07            0.14       4.0             0.077167             -0.046104"
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      "source": [
        "df.head(3)"
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    {
      "cell_type": "markdown",
      "id": "fae1c189",
      "metadata": {
        "id": "fae1c189"
      },
      "source": [
        "## Preparing the Data"
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              "    EPS  forecasted_eps  noOfEsts  after_total_returns  before_total_returns  \\\n",
              "0  2.01            1.67      11.0             0.051444              0.018585   \n",
              "1  0.17            0.19       6.0             0.112955             -0.000510   \n",
              "2 -0.07            0.14       4.0             0.077167             -0.046104   \n",
              "3  0.48            0.51       8.0            -0.006130             -0.004899   \n",
              "4 -0.24           -0.27       9.0             0.089762             -0.025466   \n",
              "\n",
              "  earnings_outcome  \n",
              "0             beat  \n",
              "1             lose  \n",
              "2             lose  \n",
              "3             lose  \n",
              "4             beat  "
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              "      <td>0.018585</td>\n",
              "      <td>beat</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.19</td>\n",
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              "      <td>lose</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
              "      <td>-0.24</td>\n",
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              "      <td>9.0</td>\n",
              "      <td>0.089762</td>\n",
              "      <td>-0.025466</td>\n",
              "      <td>beat</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Generate the categorical outcome variable\n",
        "df['earnings_outcome'] = np.nan\n",
        "df.loc[(df['EPS']==df['forecasted_eps']), 'earnings_outcome'] = 'meet'\n",
        "df.loc[(df['EPS']>df['forecasted_eps']), 'earnings_outcome'] = 'beat'\n",
        "df.loc[(df['EPS']<df['forecasted_eps']), 'earnings_outcome'] = 'lose'\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e7d9653",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e7d9653",
        "outputId": "626671fd-9d67-42ae-dcbf-d63bacf1fa4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        beat\n",
              "1        lose\n",
              "2        lose\n",
              "3        lose\n",
              "4        beat\n",
              "         ... \n",
              "71963    beat\n",
              "71964    beat\n",
              "71965    lose\n",
              "71966    beat\n",
              "71967    meet\n",
              "Name: earnings_outcome, Length: 71968, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Preview the output variable \n",
        "y = df['earnings_outcome']\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d773c015",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d773c015",
        "outputId": "0e6be711-63da-42c6-ed9e-e2e4195dd5bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, ..., 1, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Encode earnings labels to integers\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(y)\n",
        "encoded_y = encoder.transform(y)\n",
        "encoded_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "306f47d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "306f47d7",
        "outputId": "d74bd46d-7586-4c2e-9a62-4f85cc8d2e74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Save the unique number of labels for future use\n",
        "number_of_classes = len(list(y.drop_duplicates()))\n",
        "number_of_classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4512446",
      "metadata": {
        "id": "c4512446"
      },
      "outputs": [],
      "source": [
        "# Convert labeled integers to a Keras `categorical` data type\n",
        "from keras.utils.np_utils import to_categorical\n",
        "y_categorical = to_categorical(encoded_y, num_classes=number_of_classes)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8085ae8a",
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          "height": 143
        },
        "id": "8085ae8a",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   forecasted_eps  before_total_returns  noOfEsts\n",
              "0            1.67              0.018585      11.0\n",
              "1            0.19             -0.000510       6.0\n",
              "2            0.14             -0.046104       4.0"
            ],
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              "      <th></th>\n",
              "      <th>forecasted_eps</th>\n",
              "      <th>before_total_returns</th>\n",
              "      <th>noOfEsts</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Specify X (predictor) variables\n",
        "X = df[['forecasted_eps',\n",
        "              'before_total_returns','noOfEsts']]\n",
        "X.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be22b90d",
      "metadata": {
        "id": "be22b90d"
      },
      "outputs": [],
      "source": [
        "# Split into training and testing windows\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_categorical, random_state=1)"
      ]
    },
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      "id": "2ce7e477",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Preview the encoded data we're trying to predict\n",
        "pd.DataFrame(y_train).head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ef48afd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ef48afd",
        "outputId": "c7bbb9d3-085f-4085-f3d7-2f171f7d374b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    32924.0\n",
              "1    16878.0\n",
              "2     4174.0\n",
              "dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# Check for class balance\n",
        "pd.DataFrame(y_train).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec0c8fde",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec0c8fde",
        "outputId": "ce9ad064-a25d-438f-cdff-b4d77ac800b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Save the count of unique predictor variables for use in model\n",
        "number_of_predictors = len(X.columns)\n",
        "len(X.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ae85fb7",
      "metadata": {
        "id": "8ae85fb7"
      },
      "source": [
        "## Building, Fitting and Predicting with Multiple Classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03388619",
      "metadata": {
        "id": "03388619"
      },
      "outputs": [],
      "source": [
        "# Build the neural network layers\n",
        "model = Sequential()\n",
        "model.add(Dense(9, input_dim=number_of_predictors, activation='relu'))\n",
        "model.add(Dense(6, activation='relu'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13d03f72",
      "metadata": {
        "id": "13d03f72"
      },
      "outputs": [],
      "source": [
        "# Add the final output layer\n",
        "# With the # of possible outputs equal to the number classes \n",
        "model.add(Dense(number_of_classes, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "04e990e4",
      "metadata": {
        "id": "04e990e4"
      },
      "outputs": [],
      "source": [
        "# Compile the model (with multi-class specific parameters)\n",
        "model.compile(loss=\"categorical_crossentropy\", \n",
        "              optimizer= \"adam\", \n",
        "              metrics=['categorical_accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be2b30af",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be2b30af",
        "outputId": "57d29a30-3fc4-4a5c-b8fc-231887a6b4b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 9)                 36        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 60        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 3)                 21        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 117\n",
            "Trainable params: 117\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Summarise the structure of the model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bc04be47",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc04be47",
        "outputId": "1e3564df-62c9-4d4d-fea2-4326c41b7e1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "54/54 [==============================] - 1s 2ms/step - loss: 2.0748 - categorical_accuracy: 0.1014\n",
            "Epoch 2/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 1.3591 - categorical_accuracy: 0.2988\n",
            "Epoch 3/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 1.0090 - categorical_accuracy: 0.4668\n",
            "Epoch 4/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8914 - categorical_accuracy: 0.6095\n",
            "Epoch 5/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8669 - categorical_accuracy: 0.6099\n",
            "Epoch 6/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8609 - categorical_accuracy: 0.6100\n",
            "Epoch 7/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8571 - categorical_accuracy: 0.6100\n",
            "Epoch 8/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8525 - categorical_accuracy: 0.6100\n",
            "Epoch 9/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8484 - categorical_accuracy: 0.6099\n",
            "Epoch 10/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8465 - categorical_accuracy: 0.6090\n",
            "Epoch 11/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8443 - categorical_accuracy: 0.6088\n",
            "Epoch 12/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8431 - categorical_accuracy: 0.6086\n",
            "Epoch 13/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8426 - categorical_accuracy: 0.6081\n",
            "Epoch 14/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8421 - categorical_accuracy: 0.6082\n",
            "Epoch 15/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8417 - categorical_accuracy: 0.6085\n",
            "Epoch 16/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8412 - categorical_accuracy: 0.6093\n",
            "Epoch 17/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8408 - categorical_accuracy: 0.6094\n",
            "Epoch 18/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8403 - categorical_accuracy: 0.6096\n",
            "Epoch 19/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8400 - categorical_accuracy: 0.6097\n",
            "Epoch 20/20\n",
            "54/54 [==============================] - 0s 2ms/step - loss: 0.8396 - categorical_accuracy: 0.6097\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f8479041e90>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# Fit the model\n",
        "model.fit(X_train,y_train, \n",
        "                    epochs=20,\n",
        "                    batch_size=1000,\n",
        "                    shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86f5dfc0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86f5dfc0",
        "outputId": "1897ff0e-f3dd-4255-b2bd-31ca5d7af3f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "563/563 - 1s - loss: 0.8395 - categorical_accuracy: 0.6155 - 777ms/epoch - 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8395379185676575, 0.6154958009719849]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Evaluate model on the test data\n",
        "model.evaluate(X_test,y_test, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c9b5e361",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9b5e361",
        "outputId": "47222916-fa12-4d7d-93df-04c03cf2a4c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "563/563 [==============================] - 1s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5742632 , 0.32977974, 0.09595694],\n",
              "       [0.7637234 , 0.1644749 , 0.07180177],\n",
              "       [0.5826478 , 0.35416842, 0.06318378],\n",
              "       ...,\n",
              "       [0.64515114, 0.28018785, 0.07466098],\n",
              "       [0.56804997, 0.34906146, 0.0828886 ],\n",
              "       [0.5159607 , 0.40440705, 0.07963229]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# Save predictions on the test data\n",
        "predictions = model.predict(X_test)\n",
        "predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "abf270f9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abf270f9",
        "outputId": "e23fcada-dabc-400a-bd8b-9107a8e1c226"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Get the most likely prediction for each observation\n",
        "most_likely = np.argmax(predictions, axis=1)\n",
        "most_likely"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd386959",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd386959",
        "outputId": "300dbb80-2dfb-453a-e623-83d4b4b69b0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['beat', 'beat', 'beat', ..., 'beat', 'beat', 'beat'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Convert most likely category back to original labels\n",
        "most_likely = encoder.inverse_transform((most_likely))\n",
        "most_likely"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2589f7f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2589f7f",
        "outputId": "2013fc83-a322-4374-8e6a-985ecf903aff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "beat    17835\n",
              "lose      157\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Evaluate prediction balance\n",
        "pd.DataFrame(most_likely).value_counts()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.11"
    },
    "colab": {
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    }
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