15_topic_modeling/03_dirichlet_distribution.ipynb

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   "source": [
    "# Latent Dirichlet Allocation - Interactive Simulation"
   ]
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  {
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   "source": [
    "## Imports"
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
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   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "from ipywidgets import interact, FloatSlider\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
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   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Simulate Dirichlet Distribution"
   ]
  },
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       "interactive(children=(FloatSlider(value=1.0, continuous_update=False, description='Alpha', max=10.0, min=0.01,…"
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   "source": [
    "f = FloatSlider(value=1,\n",
    "                min=1e-2,\n",
    "                max=10,\n",
    "                step=1e-2,\n",
    "                continuous_update=False,\n",
    "                description='Alpha')\n",
    "\n",
    "\n",
    "@interact(alpha=f)\n",
    "def sample_dirichlet(alpha):\n",
    "    topics = 10\n",
    "    draws = 9\n",
    "    alphas = np.full(shape=topics, fill_value=alpha)\n",
    "    samples = np.random.dirichlet(alpha=alphas, size=draws)\n",
    "\n",
    "    fig, axes = plt.subplots(nrows=3,\n",
    "                             ncols=3,\n",
    "                             sharex=True,\n",
    "                             sharey=True,\n",
    "                             figsize=(14, 8))\n",
    "    axes = axes.flatten()\n",
    "    plt.setp(axes, ylim=(0, 1))\n",
    "    for i, sample in enumerate(samples):\n",
    "        axes[i].bar(x=list(range(10)), height=sample,\n",
    "                    color=sns.color_palette(\"Set2\", 10))\n",
    "    fig.suptitle('Dirichlet Allocation | 10 Topics, 9 Samples')\n",
    "    sns.despine()\n",
    "    fig.tight_layout()\n",
    "    plt.subplots_adjust(top=.95)"
   ]
  }
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