examples/01 Flowers and Forests - A Simple Pipeline.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.datasets\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn_pandas import DataFrameMapper, make_dataframe_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gather a Tidy Dataframe\n",
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "    class  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data = sklearn.datasets.load_iris()\n",
    "iris = pandas.DataFrame(data = iris_data[\"data\"], columns=iris_data[\"feature_names\"])\n",
    "iris[\"class\"] = iris_data[\"target_names\"][iris_data[\"target\"]]\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assemble a Simple Learning Pipeline\n",
    "--------\n",
    "\n",
    "A DataFramePipeline begins with a DataFrameMapper, specify how features **`X`** and targets **`y`** are extracted from an input frame. It ends with an estimator object.\n",
    "\n",
    "In this case, extract each available feature without transformation and specify the class label as the target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forest_pipeline = make_dataframe_pipeline([\n",
    "        DataFrameMapper(iris_data[\"feature_names\"], \"class\"),\n",
    "        RandomForestClassifier(n_estimators=200)\n",
    "    ])\n",
    "\n",
    "logistic_pipeline = make_dataframe_pipeline([\n",
    "        DataFrameMapper(iris_data[\"feature_names\"], \"class\"),\n",
    "        LogisticRegression()\n",
    "    ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Validate\n",
    "-----\n",
    "\n",
    "Cross validation requires the target **`y`** to perform train-test splits. Use the pipeline's DataFrameMapper to extract the target feature array from input data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>forest</th>\n",
       "      <th>logistic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.027889</td>\n",
       "      <td>0.043461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         forest  logistic\n",
       "count  5.000000  5.000000\n",
       "mean   0.960000  0.960000\n",
       "std    0.027889  0.043461\n",
       "min    0.933333  0.900000\n",
       "25%    0.933333  0.933333\n",
       "50%    0.966667  0.966667\n",
       "75%    0.966667  1.000000\n",
       "max    1.000000  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_result = pandas.DataFrame.from_dict({\n",
    "    \"forest\" : cross_val_score(\n",
    "        estimator = forest_pipeline,\n",
    "        X = iris, y = forest_pipeline._dataframe_mapper.extract_y(iris),\n",
    "        cv = 5, scoring=\"accuracy\"),\n",
    "    \"logistic\" : cross_val_score(\n",
    "        estimator = logistic_pipeline,\n",
    "        X = iris, y = logistic_pipeline._dataframe_mapper.extract_y(iris),\n",
    "        cv = 5, scoring=\"accuracy\")\n",
    "    })\n",
    "\n",
    "cross_val_result.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract Feature Metadata\n",
    "----\n",
    "\n",
    "The DataFrameMapper may be used to associate estimator metadata with feature source information. In this case, the `feature_importances_` vector is associated with the source column name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0.111118\n",
       "sepal width (cm)     0.028009\n",
       "petal length (cm)    0.455807\n",
       "petal width (cm)     0.405066\n",
       "Name: feature_importances, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_pipeline.fit(iris)\n",
    "pandas.Series(\n",
    "    data = forest_pipeline._final_estimator.feature_importances_,\n",
    "    index = forest_pipeline._dataframe_mapper.X_columns_,\n",
    "    name=\"feature_importances\"\n",
    ")"
   ]
  }
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