examples.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Featurefilter Examples\n",
    "\n",
    "This notebooks shows a number of concise examples for common use cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns with too many NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "The NA ratio of column 'A' (0.6667) is above the threshold of 0.5000\n"
     ]
    },
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       "     B\n",
       "0  0.0\n",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from featurefilter import NaFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0, np.nan, np.nan],\n",
    "                   'B': [0, 0, np.nan]})\n",
    "\n",
    "na_filter = NaFilter(max_na_ratio=0.5)\n",
    "na_filter.columns_to_drop = ['A']\n",
    "na_filter.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns with too low or high variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variance of column 'B' (0.0000) is below the threshold of 0.0000\n"
     ]
    },
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       "      <th>A</th>\n",
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       "     A\n",
       "0  0.0\n",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from featurefilter import VarianceFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0., 1.], 'B': [0., 0.]})\n",
    "\n",
    "variance_filter = VarianceFilter()\n",
    "variance_filter.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns with too high correlation to the target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The absolute correlation of column 'B' (1.0000) to the target column 'Y' is above the threshold of 0.9500\n"
     ]
    },
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       "   A  Y\n",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from featurefilter import TargetCorrelationFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0, 0], 'B': [0, 1], 'Y': [0, 1]})\n",
    "\n",
    "target_correlation_filter = TargetCorrelationFilter(target_column='Y')\n",
    "target_correlation_filter.fit_transform(df)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns using generalized linear models (GLMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative feature importances:\n",
      "   A 1.0000\n",
      "   B 0.0000\n"
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    },
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       "   A  Y\n",
       "0  0  0\n",
       "1  0  0\n",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from featurefilter import GLMFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0, 0, 1, 1],\n",
    "                   'B': [0, 1, 0, 1],\n",
    "                   'Y': [0, 0, 1, 1]})\n",
    "\n",
    "glm_filter = GLMFilter(target_column='Y', top_features=1)\n",
    "glm_filter.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns using tree-based models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "A 1.0\n",
      "B 0.0\n",
      "['B' 'A']\n"
     ]
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       "   A  Y\n",
       "0  0  a\n",
       "1  0  a\n",
       "2  1  b\n",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "from featurefilter import TreeBasedFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0, 0, 1, 1],\n",
    "                   'B': [0, 1, 0, 1],\n",
    "                   'Y': ['a', 'a', 'b', 'b']})\n",
    "\n",
    "tree_based_filter = TreeBasedFilter(target_column='Y',\n",
    "                                    categorical_target=True,\n",
    "                                    top_features=1)\n",
    "tree_based_filter.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns using multiple filters combined with scikit-learn's Pipeline API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "The NA ratio of column 'A' (0.6667) is above the threshold of 0.5000\n",
      "The variance of column 'B' (0.0000) is below the threshold of 0.0000\n"
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       "     C\n",
       "0  0.0\n",
       "1  NaN\n",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from featurefilter import NaFilter, VarianceFilter\n",
    "\n",
    "df = pd.DataFrame({'A': [0, np.nan, np.nan],\n",
    "                   'B': [0, 0, 0],\n",
    "                   'C': [0, np.nan, 1]})\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('na_filter', NaFilter(max_na_ratio=0.5)),\n",
    "    ('variance_filter', VarianceFilter())\n",
    "])\n",
    "\n",
    "pipeline.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove columns using existing selectors provided by scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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       "   A  Y\n",
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   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from featurefilter import SklearnWrapper\n",
    "\n",
    "df = pd.DataFrame({'A': [0, 0, 1, 1],\n",
    "                   'B': [0, 1, 0, 1],\n",
    "                   'Y': [0, 0, 1, 1]})\n",
    "\n",
    "model = RFECV(LinearRegression(),\n",
    "              min_features_to_select=1,\n",
    "              cv=3)\n",
    "selector = SklearnWrapper(model, target_column='Y')\n",
    "selector.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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