[ci skip] Initial commit 37431d8

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: f51136111eba58b7ed8810f833e65d91
+tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/084669b45b226af6f637ab8457c8c5aa/plot_classifier.py

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+"""
+============================
+Plotting Template Classifier
+============================
+
+An example plot of :class:`skltemplate.template.TemplateClassifier`
+"""
+
+# %%
+# Train our classifier on very simple dataset
+from skltemplate import TemplateClassifier
+
+X = [[0, 0], [1, 1]]
+y = [0, 1]
+clf = TemplateClassifier().fit(X, y)
+
+# %%
+# Create a test dataset
+import numpy as np
+
+rng = np.random.RandomState(13)
+X_test = rng.rand(500, 2)
+
+# %%
+# Use scikit-learn to display the decision boundary
+from sklearn.inspection import DecisionBoundaryDisplay
+
+disp = DecisionBoundaryDisplay.from_estimator(clf, X_test)
+disp.ax_.scatter(
+    X_test[:, 0],
+    X_test[:, 1],
+    c=clf.predict(X_test),
+    s=20,
+    edgecolors="k",
+    linewidths=0.5,
+)
+disp.ax_.set(
+    xlabel="Feature 1",
+    ylabel="Feature 2",
+    title="Template Classifier Decision Boundary",
+)

_downloads/2844e15e96683b9e78e43f57c6f47e9e/plot_template.py

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+"""
+===========================
+Plotting Template Estimator
+===========================
+
+An example plot of :class:`skltemplate.template.TemplateEstimator`
+"""
+import numpy as np
+from matplotlib import pyplot as plt
+
+from skltemplate import TemplateEstimator
+
+X = np.arange(100).reshape(100, 1)
+y = np.zeros((100,))
+estimator = TemplateEstimator()
+estimator.fit(X, y)
+plt.plot(estimator.predict(X))
+plt.show()

_downloads/33e3b8fda2687ae96879282d905830b7/plot_template.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plotting Template Estimator\n\nAn example plot of :class:`skltemplate.template.TemplateEstimator`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom skltemplate import TemplateEstimator\n\nX = np.arange(100).reshape(100, 1)\ny = np.zeros((100,))\nestimator = TemplateEstimator()\nestimator.fit(X, y)\nplt.plot(estimator.predict(X))\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": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/35939d2f12dbb3de2b921c8a33dd72c2/plot_transformer.py

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+"""
+=============================
+Plotting Template Transformer
+=============================
+
+An example plot of :class:`skltemplate.template.TemplateTransformer`
+"""
+import numpy as np
+from matplotlib import pyplot as plt
+
+from skltemplate import TemplateTransformer
+
+X = np.arange(50, dtype=np.float64).reshape(-1, 1)
+X /= 50
+estimator = TemplateTransformer()
+X_transformed = estimator.fit_transform(X)
+
+plt.plot(X.flatten(), label="Original Data")
+plt.plot(X_transformed.flatten(), label="Transformed Data")
+plt.title("Plots of original and transformed data")
+
+plt.legend(loc="best")
+plt.grid(True)
+plt.xlabel("Index")
+plt.ylabel("Value of Data")
+
+plt.show()

_downloads/5b2cebfab9d8cca4393d66b1e4b8a8e7/plot_transformer.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plotting Template Transformer\n\nAn example plot of :class:`skltemplate.template.TemplateTransformer`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom skltemplate import TemplateTransformer\n\nX = np.arange(50, dtype=np.float64).reshape(-1, 1)\nX /= 50\nestimator = TemplateTransformer()\nX_transformed = estimator.fit_transform(X)\n\nplt.plot(X.flatten(), label=\"Original Data\")\nplt.plot(X_transformed.flatten(), label=\"Transformed Data\")\nplt.title(\"Plots of original and transformed data\")\n\nplt.legend(loc=\"best\")\nplt.grid(True)\nplt.xlabel(\"Index\")\nplt.ylabel(\"Value of Data\")\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": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/fed3e85cac62fc93e088aa8cb008d467/plot_classifier.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Plotting Template Classifier\n\nAn example plot of :class:`skltemplate.template.TemplateClassifier`\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Train our classifier on very simple dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skltemplate import TemplateClassifier\n\nX = [[0, 0], [1, 1]]\ny = [0, 1]\nclf = TemplateClassifier().fit(X, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create a test dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nrng = np.random.RandomState(13)\nX_test = rng.rand(500, 2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use scikit-learn to display the decision boundary\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.inspection import DecisionBoundaryDisplay\n\ndisp = DecisionBoundaryDisplay.from_estimator(clf, X_test)\ndisp.ax_.scatter(\n    X_test[:, 0],\n    X_test[:, 1],\n    c=clf.predict(X_test),\n    s=20,\n    edgecolors=\"k\",\n    linewidths=0.5,\n)\ndisp.ax_.set(\n    xlabel=\"Feature 1\",\n    ylabel=\"Feature 2\",\n    title=\"Template Classifier Decision Boundary\",\n)"
+      ]
+    }
+  ],
+  "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": "3.12.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}