Pipelines_in_scikit_learn.ipynb

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      "cell_type": "markdown",
      "source": [
        "\n",
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Creating a Pipeline in scikit-learn**</p>"
      ],
      "metadata": {
        "id": "XxJJZw6NoYby"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Example 1**"
      ],
      "metadata": {
        "id": "bnUyvzKhoftJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1: Import the Necessary Libraries**"
      ],
      "metadata": {
        "id": "rGOtoCMxoo1a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "TEEmT64_otWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2: Load and Split the Data**"
      ],
      "metadata": {
        "id": "Lq2TlhVBpDyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "# Split the data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "6yRVDdzrpGY6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3: Define the Pipeline**"
      ],
      "metadata": {
        "id": "GS2U3Y1hpNea"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the pipeline\n",
        "pipeline = Pipeline([\n",
        " ('scaler', StandardScaler()),\n",
        " ('pca', PCA(n_components=2)),\n",
        " ('classifier', LogisticRegression())\n",
        "])"
      ],
      "metadata": {
        "id": "rg1mxVy5pUaq"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "**Step 4: Train the Pipeline**"
      ],
      "metadata": {
        "id": "pA1TtfdGpaNB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the pipeline\n",
        "pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
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        "id": "IG1g7yT_phGR",
        "outputId": "a82cc3f7-091f-4886-ef09-1327d906b163"
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          "data": {
            "text/plain": [
              "Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA(n_components=2)),\n",
              "                ('classifier', LogisticRegression())])"
            ],
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              "                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;pca&#x27;, PCA(n_components=2)),\n",
              "                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 5: Make Predictions and Evaluate the Model**"
      ],
      "metadata": {
        "id": "U6AQC38vplzi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = pipeline.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJ7T4mWwpweK",
        "outputId": "2aedc374-6c1c-493c-cf0f-595ff7e03c88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 90.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 6:Hyperparameter Tuning with Pipelines**"
      ],
      "metadata": {
        "id": "EsIoKU5Dp0_S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameter Tuning with GridSearchCV**"
      ],
      "metadata": {
        "id": "7zZaS0dCp9ES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "# Define the parameter grid\n",
        "param_grid = {\n",
        "'pca__n_components': [2, 3],\n",
        "'classifier__C': [0.1, 1, 10]\n",
        "}\n",
        "# Create a GridSearchCV object\n",
        "grid_search = GridSearchCV(pipeline, param_grid, cv=5)\n",
        "# Perform grid search\n",
        "grid_search.fit(X_train, y_train)\n",
        "# Best parameters and score\n",
        "print(f\"Best Parameters: {grid_search.best_params_}\")\n",
        "print(f\"Best Cross-Validation Score: {grid_search.best_score_:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKDtpupWqBzJ",
        "outputId": "d8480606-66b4-4f6f-c170-854dac4e9333"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'classifier__C': 1, 'pca__n_components': 3}\n",
            "Best Cross-Validation Score: 0.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**SK Factor**</p>"
      ],
      "metadata": {
        "id": "vAd0UjmEqinM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1-Install SK Factor:**"
      ],
      "metadata": {
        "id": "80TqBfSYq19N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/B2F/sk-factor.git\n",
        "\n"
      ],
      "metadata": {
        "id": "XlYCFBUCq6VU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install SK Factor and restart the kernel\n",
        "!pip install git+https://github.com/B2F/sk-factor.git\n",
        "\n",
        "# This comment is important: After running this cell, please restart the kernel\n",
        "# (Kernel -> Restart) to make the installed package available to your notebook.\n",
        "\n",
        "#Import the sys module to interact with the Python runtime environment.\n",
        "import sys\n",
        "\n",
        "# Import SK Factor's pipeline builder and scikit-learn's dataset\n",
        "from skfactor.pipeline_builder import PipelineBuilder # This import will now work as skfactor is installed in the same cell.\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = load_iris()\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "FX0QNzTCrxQj",
        "outputId": "839b8b3a-2976-4bf0-fe07-9cc3b2eecc7e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/B2F/sk-factor.git\n",
            "  Cloning https://github.com/B2F/sk-factor.git to /tmp/pip-req-build-vrbzyz51\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/B2F/sk-factor.git /tmp/pip-req-build-vrbzyz51\n",
            "  Resolved https://github.com/B2F/sk-factor.git to commit 326a426732cb1be0e6b4a6438d608f8446f4a110\n",
            "\u001b[31mERROR: git+https://github.com/B2F/sk-factor.git does not appear to be a Python project: neither 'setup.py' nor 'pyproject.toml' found.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'skfactor'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-0f1acef9911d>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Import SK Factor's pipeline builder and scikit-learn's dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mskfactor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipeline_builder\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPipelineBuilder\u001b[0m \u001b[0;31m# This import will now work as skfactor is installed in the same cell.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_iris\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'skfactor'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a simple pipeline using SK Factor for classification\n",
        "pipeline = PipelineBuilder(task='classification') \\\n",
        "            .with_data(X, y) \\\n",
        "            .with_model('decision_tree') \\\n",
        "            .build()\n",
        "\n",
        "# Train the model\n",
        "pipeline.train()\n",
        "\n",
        "# Evaluate the model\n",
        "pipeline.evaluate()"
      ],
      "metadata": {
        "id": "XL2AxZu7s5Gi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**References**</p>"
      ],
      "metadata": {
        "id": "r2dqhiWMtNgL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1-SK Factor](https://github.com/B2F/sk-factor)"
      ],
      "metadata": {
        "id": "4F1WJ_HVtZKs"
      }
    }
  ]
}