Clustering.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements\n",
    "from sklearn.datasets import make_blobs\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt# create blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the data set: 4 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = make_blobs(n_samples=200, n_features=2, centers=4, cluster_std=1.6, random_state=50)# create np array for data points\n",
    "#data = make_blobs(n_samples=200, n_features=2, centers=4, cluster_std=3.2, random_state=50)# create np array for data points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = data[0]\n",
    "plt.scatter(data[0][:,0], data[0][:,1], c=data[1], cmap='viridis')\n",
    "plt.xlim(-15,15)\n",
    "plt.ylim(-15,15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create kmeans object with 4 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit kmeans object to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans.fit(points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print location of clusters learned by kmeans object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(kmeans.cluster_centers_)# save new clusters for chart\n",
    "y_km = kmeans.fit_predict(points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(points[y_km ==0,0], points[y_km == 0,1], s=100, c='red')\n",
    "plt.scatter(points[y_km ==1,0], points[y_km == 1,1], s=100, c='black')\n",
    "plt.scatter(points[y_km ==2,0], points[y_km == 2,1], s=100, c='blue')\n",
    "plt.scatter(points[y_km ==3,0], points[y_km == 3,1], s=100, c='cyan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agglomerative Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dendrogram = sch.dendrogram(sch.linkage(points, method='ward'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create hierarchical clustering object with 4 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hc = AgglomerativeClustering(n_clusters=4, affinity = 'euclidean', linkage = 'ward')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hc = hc.fit_predict(points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(points[y_hc ==0,0], points[y_hc == 0,1], s=100, c='red')\n",
    "plt.scatter(points[y_hc==1,0], points[y_hc == 1,1], s=100, c='black')\n",
    "plt.scatter(points[y_hc ==2,0], points[y_hc == 2,1], s=100, c='blue')\n",
    "plt.scatter(points[y_hc ==3,0], points[y_hc == 3,1], s=100, c='cyan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare K-Means with Hierarchical clustreing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12.8, 4.8))\n",
    "ax[0].scatter(points[y_km ==0,0], points[y_km == 0,1], s=100, c='red')\n",
    "ax[0].scatter(points[y_km ==1,0], points[y_km == 1,1], s=100, c='black')\n",
    "ax[0].scatter(points[y_km ==2,0], points[y_km == 2,1], s=100, c='blue')\n",
    "ax[0].scatter(points[y_km ==3,0], points[y_km == 3,1], s=100, c='cyan')\n",
    "ax[0].set_title(\"K-means\")\n",
    "ax[1].scatter(points[y_hc ==0,0], points[y_hc == 0,1], s=100, c='red')\n",
    "ax[1].scatter(points[y_hc==1,0], points[y_hc == 1,1], s=100, c='black')\n",
    "ax[1].scatter(points[y_hc ==2,0], points[y_hc == 2,1], s=100, c='blue')\n",
    "ax[1].scatter(points[y_hc ==3,0], points[y_hc == 3,1], s=100, c='cyan')\n",
    "ax[1].set_title(\"Hierarchical Clustering\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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