08.5 Feature Union.ipynb

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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Aside: Feature Union\n",
    "======================"
   ]
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  {
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   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "data = pd.read_csv(os.path.join(\"data\", \"train.csv\"))\n",
    "y_train = np.array(data.Insult)\n",
    "text_train = data.Comment.tolist()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "from sklearn.pipeline import make_union, make_pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "\n",
    "char_and_word = make_union(CountVectorizer(analyzer=\"char\"),\n",
    "                           CountVectorizer(analyzer=\"word\"))\n",
    "\n",
    "text_pipe = make_pipeline(char_and_word, LinearSVC(dual=False))\n",
    "param_grid = {'linearsvc__C': 10. ** np.arange(-3, 3)}\n",
    "\n",
    "grid = GridSearchCV(text_pipe, param_grid=param_grid, cv=3, verbose=10)"
   ]
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   "source": [
    "grid.fit(text_train, y_train)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "param_grid = {'featureunion__countvectorizer-1__ngram_range':\n",
    "              [(1, 3), (1, 5), (2, 5)],\n",
    "              'featureunion__countvectorizer-2__ngram_range':\n",
    "              [(1, 1), (1, 2), (2, 2)],\n",
    "              'linearsvc__C': 10. ** np.arange(-3, 3)}"
   ]
  }
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