ML_Pipeline_Sklearn.ipynb

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    "colab": {
      "name": "ML_Pipeline_Sklearn.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML_Pipeline/blob/main/ML_Pipeline_Sklearn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IAf9hh-cnLuh"
      },
      "source": [
        "## Pipelines In SkLearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEtde07znLuk"
      },
      "source": [
        "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.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5csIdSzdnLuu"
      },
      "source": [
        "iris_df=load_iris()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gEbvuqtnLu3",
        "outputId": "f8f846b6-60af-4b96-fa6a-8b4753a1d472",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "iris_df.data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [5. , 3.2, 1.2, 0.2],\n",
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              "       [4.9, 3.6, 1.4, 0.1],\n",
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              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
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              "       [6.5, 2.8, 4.6, 1.5],\n",
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              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
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              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
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              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBi66pg_nLvA"
      },
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(iris_df.data,iris_df.target,test_size=0.3,random_state=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3tWkpEJnLvJ"
      },
      "source": [
        "## Pipelines Creation\n",
        "## 1. Data Preprocessing by using Standard Scaler\n",
        "## 2. Reduce Dimension using PCA\n",
        "## 3. Apply  Classifier"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Epx-9IrynLvQ"
      },
      "source": [
        "pipeline_lr=Pipeline([('scalar1',StandardScaler()),\n",
        "                     ('pca1',PCA(n_components=2)),\n",
        "                     ('lr_classifier',LogisticRegression(random_state=0))])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4IISqL8nLvZ"
      },
      "source": [
        "pipeline_dt=Pipeline([('scalar2',StandardScaler()),\n",
        "                     ('pca2',PCA(n_components=2)),\n",
        "                     ('dt_classifier',DecisionTreeClassifier())])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB209BirnLvh"
      },
      "source": [
        "pipeline_randomforest=Pipeline([('scalar3',StandardScaler()),\n",
        "                     ('pca3',PCA(n_components=2)),\n",
        "                     ('rf_classifier',RandomForestClassifier())])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZ_pixA3nLvq"
      },
      "source": [
        "## LEts make the list of pipelines\n",
        "pipelines = [pipeline_lr, pipeline_dt, pipeline_randomforest]"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEwVTElEnLvw"
      },
      "source": [
        "best_accuracy=0.0\n",
        "best_classifier=0\n",
        "best_pipeline=\"\""
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8xT_7jKnLv5"
      },
      "source": [
        "# Dictionary of pipelines and classifier types for ease of reference\n",
        "pipe_dict = {0: 'Logistic Regression', 1: 'Decision Tree', 2: 'RandomForest'}\n",
        "\n",
        "# Fit the pipelines\n",
        "for pipe in pipelines:\n",
        "\tpipe.fit(X_train, y_train)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kh190Sl1nLwB",
        "outputId": "9000b3b6-2d5e-4475-d4fe-0e590ac55eb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "for i,model in enumerate(pipelines):\n",
        "    print(\"{} Test Accuracy: {}\".format(pipe_dict[i],model.score(X_test,y_test)))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression Test Accuracy: 0.8666666666666667\n",
            "Decision Tree Test Accuracy: 0.9111111111111111\n",
            "RandomForest Test Accuracy: 0.9111111111111111\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFEga8ONnLwK",
        "outputId": "922a8e9f-3d6b-418b-8eaf-e6e4559ed4bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "for i,model in enumerate(pipelines):\n",
        "    if model.score(X_test,y_test)>best_accuracy:\n",
        "        best_accuracy=model.score(X_test,y_test)\n",
        "        best_pipeline=model\n",
        "        best_classifier=i\n",
        "print('Classifier with best accuracy:{}'.format(pipe_dict[best_classifier]))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classifier with best accuracy:Decision Tree\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nr8p5TqPnLwR"
      },
      "source": [
        "## Pipelines Perform Hyperparameter Tuning Using Grid SearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqkPNjKRnLwS"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4vMl9wLnLwb",
        "outputId": "2bf9fa68-158e-473b-ddac-98c90374b9e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# Create a pipeline\n",
        "pipe = Pipeline([(\"classifier\", RandomForestClassifier())])\n",
        "# Create dictionary with candidate learning algorithms and their hyperparameters\n",
        "grid_param = [\n",
        "                {\"classifier\": [LogisticRegression()],\n",
        "                 \"classifier__penalty\": ['l2','l1'],\n",
        "                 \"classifier__C\": np.logspace(0, 4, 10)\n",
        "                 },\n",
        "                {\"classifier\": [LogisticRegression()],\n",
        "                 \"classifier__penalty\": ['l2'],\n",
        "                 \"classifier__C\": np.logspace(0, 4, 10),\n",
        "                 \"classifier__solver\":['newton-cg','saga','sag','liblinear'] ##This solvers don't allow L1 penalty\n",
        "                 },\n",
        "                {\"classifier\": [RandomForestClassifier()],\n",
        "                 \"classifier__n_estimators\": [10, 100, 1000],\n",
        "                 \"classifier__max_depth\":[5,8,15,25,30,None],\n",
        "                 \"classifier__min_samples_leaf\":[1,2,5,10,15,100],\n",
        "                 \"classifier__max_leaf_nodes\": [2, 5,10]}]\n",
        "# create a gridsearch of the pipeline, the fit the best model\n",
        "gridsearch = GridSearchCV(pipe, grid_param, cv=5, verbose=0,n_jobs=-1) # Fit grid search\n",
        "best_model = gridsearch.fit(X_train,y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO8mdj_cnLwk",
        "outputId": "750a1c71-a2e5-489c-ca6c-17b06006e185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "print(best_model.best_estimator_)\n",
        "print(\"The mean accuracy of the model is:\",best_model.score(X_test,y_test))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pipeline(memory=None,\n",
            "         steps=[('classifier',\n",
            "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
            "                                    fit_intercept=True, intercept_scaling=1,\n",
            "                                    l1_ratio=None, max_iter=100,\n",
            "                                    multi_class='auto', n_jobs=None,\n",
            "                                    penalty='l2', random_state=None,\n",
            "                                    solver='saga', tol=0.0001, verbose=0,\n",
            "                                    warm_start=False))],\n",
            "         verbose=False)\n",
            "The mean accuracy of the model is: 0.9555555555555556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MntjsSebnLwu"
      },
      "source": [
        "## MakePipelines In Sklearn\n",
        "\n",
        "The difference between above pipeline initialization and make_pipeline is that you don't need to define pipeline in terms of key-pair in later case.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGBdS-OqnLwv"
      },
      "source": [
        "from sklearn.pipeline import make_pipeline"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5t5u3UjnLw1"
      },
      "source": [
        "# Create a pipeline\n",
        "pipe = make_pipeline((RandomForestClassifier()))\n",
        "# Create dictionary with candidate learning algorithms and their hyperparameters\n",
        "grid_param = [\n",
        "                {\"randomforestclassifier\": [RandomForestClassifier()],\n",
        "                 \"randomforestclassifier__n_estimators\": [10, 100, 1000],\n",
        "                 \"randomforestclassifier__max_depth\":[5,8,15,25,30,None],\n",
        "                 \"randomforestclassifier__min_samples_leaf\":[1,2,5,10,15,100],\n",
        "                 \"randomforestclassifier__max_leaf_nodes\": [2, 5,10]}]\n",
        "# create a gridsearch of the pipeline, the fit the best model\n",
        "gridsearch = GridSearchCV(pipe, grid_param, cv=5, verbose=0,n_jobs=-1) # Fit grid search\n",
        "best_model = gridsearch.fit(X_train,y_train)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mA_iA9SnLw9",
        "outputId": "97de2696-c79b-40c9-db00-880e2ede81d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_model.score(X_test,y_test)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9111111111111111"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    }
  ]
}