Generated gh-pages for commit d4ed39f

Merge: ebe6bda 6dd07ed
Author: Nathaniel Saul <nat@saulgill.com>

    Merge pull request #167 from cmorph1/remove-mutable-arguments

_downloads/0ce505890120247252446d98e83f71e2/digits.ipynb

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\nDigits Dataset\n================\n\nsomething something something\n\n\n\n![](../../../examples/images/digits-tooltip.png)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import io\nimport sys\nimport base64\n\nimport numpy as np\nimport sklearn\nfrom sklearn import datasets\nimport kmapper as km\n\ntry:\n    from scipy.misc import imsave, toimage\nexcept ImportError as e:\n    print(\"imsave requires you to install pillow. Run `pip install pillow` and then try again.\")\n    sys.exit()\n\n\n# Load digits dat\ndata, labels = datasets.load_digits().data, datasets.load_digits().target\n\n# Create images for a custom tooltip array\ntooltip_s = []\nfor image_data in data:\n    output = io.BytesIO()\n    img = toimage(image_data.reshape((8, 8)))  # Data was a flat row of 64 \"pixels\".\n    img.save(output, format=\"PNG\")\n    contents = output.getvalue()\n    img_encoded = base64.b64encode(contents)\n    img_tag = \"\"\"<img src=\"data:image/png;base64,{}\">\"\"\".format(img_encoded.decode('utf-8'))\n    tooltip_s.append(img_tag)\n    output.close()\n\ntooltip_s = np.array(tooltip_s)  # need to make sure to feed it as a NumPy array, not a list\n\n# Initialize to use t-SNE with 2 components (reduces data to 2 dimensions). Also note high overlap_percentage.\nmapper = km.KeplerMapper(verbose=2)\n\n# Fit and transform data\nprojected_data = mapper.fit_transform(data,\n                                      projection=sklearn.manifold.TSNE())\n\n# Create the graph (we cluster on the projected data and suffer projection loss)\ngraph = mapper.map(projected_data,\n                   clusterer=sklearn.cluster.DBSCAN(eps=0.3, min_samples=15),\n                   cover=km.Cover(35, 0.4))\n\n# Create the visualizations (increased the graph_gravity for a tighter graph-look.)\n\n# Tooltips with image data for every cluster member\nmapper.visualize(graph,\n                 title=\"Handwritten digits Mapper\",\n                 path_html=\"output/digits_custom_tooltips.html\",\n                 color_function=labels,\n                 custom_tooltips=tooltip_s)\n# Tooltips with the target y-labels for every cluster member\nmapper.visualize(graph,\n                 title=\"Handwritten digits Mapper\",\n                 path_html=\"output/digits_ylabel_tooltips.html\",\n                 custom_tooltips=labels)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/24aaea14d382dbea62b56766a05e6d9b/plot_breast_cancer.py

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+"""
+
+
+Breast Cancer
+================
+
+
+
+This example generates a Mapper built from the `Wisconsin Breast Cancer Dataset <https://www.kaggle.com/uciml/breast-cancer-wisconsin-data>`_.
+
+
+.. image:: ../../../examples/images/breast-cancer.png
+
+"""
+
+import sys
+try:
+    import pandas as pd
+except ImportError as e:
+    print("pandas is required for this example. Please install with `pip install pandas` and then try again.")
+    sys.exit()
+
+import numpy as np
+import kmapper as km
+import sklearn
+from sklearn import ensemble
+
+# For data we use the Wisconsin Breast Cancer Dataset
+# Via: 
+df = pd.read_csv("data/breast-cancer.csv")
+feature_names = [c for c in df.columns if c not in ["id", "diagnosis"]]
+df["diagnosis"] = df["diagnosis"].apply(lambda x: 1 if x == "M" else 0)
+X = np.array(df[feature_names].fillna(0))  # quick and dirty imputation
+y = np.array(df["diagnosis"])
+
+# We create a custom 1-D lens with Isolation Forest
+model = ensemble.IsolationForest(random_state=1729)
+model.fit(X)
+lens1 = model.decision_function(X).reshape((X.shape[0], 1))
+
+# We create another 1-D lens with L2-norm
+mapper = km.KeplerMapper(verbose=3)
+lens2 = mapper.fit_transform(X, projection="l2norm")
+
+# Combine both lenses to create a 2-D [Isolation Forest, L^2-Norm] lens
+lens = np.c_[lens1, lens2]
+
+# Create the simplicial complex
+graph = mapper.map(lens,
+                   X,
+                   cover=km.Cover(n_cubes=15, perc_overlap=0.4),
+                   clusterer=sklearn.cluster.KMeans(n_clusters=2,
+                                                    random_state=1618033))
+
+# Visualization
+mapper.visualize(graph,
+                 path_html="output/breast-cancer.html",
+                 title="Wisconsin Breast Cancer Dataset",
+                 custom_tooltips=y)

_downloads/2eb975460e21ae89ccc5461faf57bc20/plot_horse.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n3D Horse Data\n================\n\n\nThis example generates a Mapper built from a point-cloud sampled from a 3D model of a horse.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport numpy as np\nimport sklearn\n\nimport kmapper as km\n\ndata = np.genfromtxt('data/horse-reference.csv', delimiter=',')\n\nmapper = km.KeplerMapper(verbose=2)\n\n\nlens = mapper.fit_transform(data)\n\n\ngraph = mapper.map(lens,\n                   data,\n                   clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=5),\n                   cover=km.Cover(30, 0.2))\n\nmapper.visualize(graph,\n                 path_html=\"output/horse_keplermapper.html\",\n                 custom_tooltips=np.arange(len(lens)))\n\n\nkm.drawing.draw_matplotlib(graph)\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/35016702dc7836dc0a96ff7b1005e407/plot_cat.py

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+""" 
+
+3D Cat Data
+============
+
+
+This example generates a Mapper built from a point-cloud sampled from a 3D model of a cat.
+
+"""
+
+
+import numpy as np
+import sklearn
+import kmapper as km
+
+data = np.genfromtxt('data/cat-reference.csv', delimiter=',')
+
+mapper = km.KeplerMapper(verbose=2)
+
+
+lens = mapper.fit_transform(data)
+
+graph = mapper.map(lens,
+                   data,
+                   clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=5),
+                   cover=km.Cover(n_cubes=15, perc_overlap=0.2))
+
+mapper.visualize(graph,
+                 path_html="output/cat_keplermapper.html")
+
+km.draw_matplotlib(graph)
+
+import matplotlib.pyplot as plt
+plt.show()

_downloads/8a5b90c8d1e8e24f002588e2741438fd/plot_cat.ipynb

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "3D Cat Data\n============\n\n\nThis example generates a Mapper built from a point-cloud sampled from a 3D model of a cat.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport sklearn\nimport kmapper as km\n\ndata = np.genfromtxt('data/cat-reference.csv', delimiter=',')\n\nmapper = km.KeplerMapper(verbose=2)\n\n\nlens = mapper.fit_transform(data)\n\ngraph = mapper.map(lens,\n                   data,\n                   clusterer=sklearn.cluster.DBSCAN(eps=0.1, min_samples=5),\n                   cover=km.Cover(n_cubes=15, perc_overlap=0.2))\n\nmapper.visualize(graph,\n                 path_html=\"output/cat_keplermapper.html\")\n\nkm.draw_matplotlib(graph)\n\nimport matplotlib.pyplot as plt\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/c36804259dbeba696d63e13c0538f21c/plot_breast_cancer.ipynb

@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\nBreast Cancer\n================\n\n\n\nThis example generates a Mapper built from the `Wisconsin Breast Cancer Dataset <https://www.kaggle.com/uciml/breast-cancer-wisconsin-data>`_.\n\n\n![](../../../examples/images/breast-cancer.png)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import sys\ntry:\n    import pandas as pd\nexcept ImportError as e:\n    print(\"pandas is required for this example. Please install with `pip install pandas` and then try again.\")\n    sys.exit()\n\nimport numpy as np\nimport kmapper as km\nimport sklearn\nfrom sklearn import ensemble\n\n# For data we use the Wisconsin Breast Cancer Dataset\n# Via: \ndf = pd.read_csv(\"data/breast-cancer.csv\")\nfeature_names = [c for c in df.columns if c not in [\"id\", \"diagnosis\"]]\ndf[\"diagnosis\"] = df[\"diagnosis\"].apply(lambda x: 1 if x == \"M\" else 0)\nX = np.array(df[feature_names].fillna(0))  # quick and dirty imputation\ny = np.array(df[\"diagnosis\"])\n\n# We create a custom 1-D lens with Isolation Forest\nmodel = ensemble.IsolationForest(random_state=1729)\nmodel.fit(X)\nlens1 = model.decision_function(X).reshape((X.shape[0], 1))\n\n# We create another 1-D lens with L2-norm\nmapper = km.KeplerMapper(verbose=3)\nlens2 = mapper.fit_transform(X, projection=\"l2norm\")\n\n# Combine both lenses to create a 2-D [Isolation Forest, L^2-Norm] lens\nlens = np.c_[lens1, lens2]\n\n# Create the simplicial complex\ngraph = mapper.map(lens,\n                   X,\n                   cover=km.Cover(n_cubes=15, perc_overlap=0.4),\n                   clusterer=sklearn.cluster.KMeans(n_clusters=2,\n                                                    random_state=1618033))\n\n# Visualization\nmapper.visualize(graph,\n                 path_html=\"output/breast-cancer.html\",\n                 title=\"Wisconsin Breast Cancer Dataset\",\n                 custom_tooltips=y)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/e455a86edd899c0d8936bff5e5d24ff6/digits.py

@@ -0,0 +1,77 @@
+"""
+
+Digits Dataset
+================
+
+something something something
+
+
+
+.. image:: ../../../examples/images/digits-tooltip.png
+
+
+
+
+
+"""
+
+
+
+import io
+import sys
+import base64
+
+import numpy as np
+import sklearn
+from sklearn import datasets
+import kmapper as km
+
+try:
+    from scipy.misc import imsave, toimage
+except ImportError as e:
+    print("imsave requires you to install pillow. Run `pip install pillow` and then try again.")
+    sys.exit()
+
+
+# Load digits dat
+data, labels = datasets.load_digits().data, datasets.load_digits().target
+
+# Create images for a custom tooltip array
+tooltip_s = []
+for image_data in data:
+    output = io.BytesIO()
+    img = toimage(image_data.reshape((8, 8)))  # Data was a flat row of 64 "pixels".
+    img.save(output, format="PNG")
+    contents = output.getvalue()
+    img_encoded = base64.b64encode(contents)
+    img_tag = """<img src="data:image/png;base64,{}">""".format(img_encoded.decode('utf-8'))
+    tooltip_s.append(img_tag)
+    output.close()
+
+tooltip_s = np.array(tooltip_s)  # need to make sure to feed it as a NumPy array, not a list
+
+# Initialize to use t-SNE with 2 components (reduces data to 2 dimensions). Also note high overlap_percentage.
+mapper = km.KeplerMapper(verbose=2)
+
+# Fit and transform data
+projected_data = mapper.fit_transform(data,
+                                      projection=sklearn.manifold.TSNE())
+
+# Create the graph (we cluster on the projected data and suffer projection loss)
+graph = mapper.map(projected_data,
+                   clusterer=sklearn.cluster.DBSCAN(eps=0.3, min_samples=15),
+                   cover=km.Cover(35, 0.4))
+
+# Create the visualizations (increased the graph_gravity for a tighter graph-look.)
+
+# Tooltips with image data for every cluster member
+mapper.visualize(graph,
+                 title="Handwritten digits Mapper",
+                 path_html="output/digits_custom_tooltips.html",
+                 color_function=labels,
+                 custom_tooltips=tooltip_s)
+# Tooltips with the target y-labels for every cluster member
+mapper.visualize(graph,
+                 title="Handwritten digits Mapper",
+                 path_html="output/digits_ylabel_tooltips.html",
+                 custom_tooltips=labels)