diff --git a/README.md b/README.md
index 3717b8371..708168d4e 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
[](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb)
-This repository contains the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks.
+This is where I say goodbye repository contnaoins the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks.
diff --git a/notebooks/03.07-Merge-and-Join.ipynb b/nb2/03.07-Merge-and-Join.ipynb
similarity index 100%
rename from notebooks/03.07-Merge-and-Join.ipynb
rename to nb2/03.07-Merge-and-Join.ipynb
diff --git a/notebooks/05.02-Introducing-Scikit-Learn.ipynb b/nb2/05.02-V2-Introducing-Scikit-Learn.ipynb
similarity index 65%
rename from notebooks/05.02-Introducing-Scikit-Learn.ipynb
rename to nb2/05.02-V2-Introducing-Scikit-Learn.ipynb
index e05db813d..526881760 100644
--- a/notebooks/05.02-Introducing-Scikit-Learn.ipynb
+++ b/nb2/05.02-V2-Introducing-Scikit-Learn.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
"![](\"figures/PDSH-cover-small.png\")
\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
"< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >"
@@ -34,10 +28,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n",
"One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n",
@@ -51,20 +42,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Representation in Scikit-Learn"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n",
"The best way to think about data within Scikit-Learn is in terms of tables of data."
@@ -72,10 +57,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Data as table\n",
"\n",
@@ -87,16 +69,25 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
+ "\n",
"
\n",
" \n",
" \n",
@@ -168,17 +159,15 @@
}
],
"source": [
- "import seaborn as sns\n",
- "iris = sns.load_dataset('iris')\n",
+ "import seaborn as sns_v2\n",
+ "import os\n",
+ "iris = sns_v2.load_dataset('iris')\n",
"iris.head()"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n",
"In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n",
@@ -189,10 +178,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### Features matrix\n",
"\n",
@@ -209,10 +195,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### Target array\n",
"\n",
@@ -229,18 +212,14 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"data": {
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1ICc4Ty+XT+7pp2qjJejc1eV0Y3d24+r28OQbR4lSyFidMnxDL0NOWnQqn5gO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3/+E4B169axaNGiVucvLS1l4cKFrFq1iiuuuKJFedpTTOZEdoXvf7PV0ehaSUUt2YU1JMXqCQkM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\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -250,15 +229,12 @@
"source": [
"%matplotlib inline\n",
"import seaborn as sns; sns.set()\n",
- "sns.pairplot(iris, hue='species', size=1.5);"
+ "sns.pairplot(iris, hue='species', size=4);"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):"
]
@@ -266,11 +242,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -291,11 +263,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -315,20 +283,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"To summarize, the expected layout of features and target values is visualized in the following diagram:"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
"[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
@@ -336,30 +298,21 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Scikit-Learn's Estimator API"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
"\n",
@@ -382,10 +335,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Basics of the API\n",
"\n",
@@ -405,10 +355,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Supervised learning example: Simple linear regression\n",
"\n",
@@ -419,11 +366,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -448,20 +391,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"With this data in place, we can use the recipe outlined earlier. Let's walk through the process: "
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### 1. Choose a class of model\n",
"\n",
@@ -473,9 +410,7 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -484,20 +419,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### 2. Choose model hyperparameters\n",
"\n",
@@ -523,11 +452,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -547,10 +472,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
"In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*."
@@ -558,10 +480,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### 3. Arrange data into a features matrix and target vector\n",
"\n",
@@ -573,11 +492,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -597,10 +512,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### 4. Fit the model to your data\n",
"\n",
@@ -611,11 +523,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -634,10 +542,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
"In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:"
@@ -646,11 +551,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -670,11 +571,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -693,10 +590,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"These two parameters represent the slope and intercept of the simple linear fit to the data.\n",
"Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
@@ -709,10 +603,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"#### 5. Predict labels for unknown data\n",
"\n",
@@ -725,9 +616,7 @@
"cell_type": "code",
"execution_count": 12,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -736,10 +625,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
]
@@ -747,11 +633,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"Xfit = xfit[:, np.newaxis]\n",
@@ -760,10 +642,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
]
@@ -771,11 +650,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -795,20 +670,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Supervised learning example: Iris classification\n",
"\n",
@@ -826,9 +695,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -839,10 +706,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"With the data arranged, we can follow our recipe to predict the labels:"
]
@@ -850,11 +714,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
@@ -865,10 +725,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
]
@@ -876,11 +733,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -900,20 +753,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Unsupervised learning example: Iris dimensionality\n",
"\n",
@@ -933,9 +780,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -947,10 +792,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:"
]
@@ -958,11 +800,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -983,10 +821,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
"This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before."
@@ -994,10 +829,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Unsupervised learning: Iris clustering\n",
"\n",
@@ -1012,11 +844,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from sklearn.mixture import GMM # 1. Choose the model class\n",
@@ -1028,10 +856,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:"
]
@@ -1039,11 +864,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1064,10 +885,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
"This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n",
@@ -1076,20 +894,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Application: Exploring Hand-written Digits"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n",
"In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
@@ -1097,10 +909,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Loading and visualizing the digits data\n",
"\n",
@@ -1110,11 +919,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1135,10 +940,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n",
"Let's visualize the first hundred of these:"
@@ -1147,11 +949,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1179,10 +977,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
"We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n",
@@ -1193,11 +988,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1218,11 +1009,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1242,20 +1029,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We see here that there are 1,797 samples and 64 features."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Unsupervised learning: Dimensionality reduction\n",
"\n",
@@ -1267,11 +1048,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1294,10 +1071,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We see that the projected data is now two-dimensional.\n",
"Let's plot this data to see if we can learn anything from its structure:"
@@ -1306,11 +1080,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1333,10 +1103,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
"Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
@@ -1348,10 +1115,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Classification on digits\n",
"\n",
@@ -1363,9 +1127,7 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1376,9 +1138,7 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1390,10 +1150,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
]
@@ -1401,11 +1158,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1425,10 +1178,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
"However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:"
@@ -1437,11 +1187,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1466,10 +1212,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n",
"Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n",
@@ -1479,11 +1222,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1512,10 +1251,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
"To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach."
@@ -1523,20 +1259,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Summary"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n",
"Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
@@ -1547,10 +1277,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
"< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >"
@@ -1574,9 +1301,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "version": "3.6.4"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/notebooks/05.04-Feature-Engineering.ipynb b/nb2/05.04-V2-Feature-Engineering.ipynb
similarity index 100%
rename from notebooks/05.04-Feature-Engineering.ipynb
rename to nb2/05.04-V2-Feature-Engineering.ipynb
diff --git a/nb2/05.08-V2-Random-Forests.ipynb b/nb2/05.08-V2-Random-Forests.ipynb
new file mode 100644
index 000000000..cebb236dc
--- /dev/null
+++ b/nb2/05.08-V2-Random-Forests.ipynb
@@ -0,0 +1,728 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
+ "\n",
+ "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# In-Depth: Decision Trees and Random Forests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
+ "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
+ "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
+ "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
+ "We will see examples of this in the following sections.\n",
+ "We begin with the standard imports:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plotting\n",
+ "import os\n",
+ "import seaborn as snsv2; sns.set()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Motivating Random Forests: Decision Trees"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Random forests are an example of an *ensemble learner* built on decision trees.\n",
+ "For this reason we'll start by discussing decision trees themselves.\n",
+ "\n",
+ "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
+ "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
+ "The trick, of course, comes in deciding which questions to ask at each step.\n",
+ "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
+ "Let's now look at an example of this."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating a decision tree\n",
+ "\n",
+ "Consider the following two-dimensional data, which has one of four class labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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oHpKlWtJ69KKoMAJEkH+ZSfbnX5DsMVsy+nAidf3+f/oz0qTJ+P7zL3xffFqrj5qdOlH0wquYo0bH3niS2JDqPZQ56lB8qxJnHdRGjKKoQ48m/5s3BE3+O2vmNPX1cXpw1NVtUgCUx2jtBRRI+ptvubTQNLTGsEMc221RJHTr7RX/9j/1hGPxXwBtzKFEfnNJnezxvP8OOZMnkXvqCbUWbgCze0/M0WPS7qoKXfs7jJ6947ZZ2dlELr+qWbvHXA5M6jrzfhz4byAQmAGowJ3BYDCFelouENt+73v9FcSdO7AyM9FOOgXtxJPTKhDihvWIG9djCwJCAteENmEi+gknAyDs3o0y+2fHMS2Pl+IpL8dcF7XE885UMv90K2Jxca0/W44tNoyAWoH+lDz7Av6/PYA6ZzZCJIwtihhDhxO+6rpYSKSLSzOjTuIdDAZLgf9Lsy0HBP6HH8Q35ZlqCa+8772NdvyJFD/3cp2EcX+EnTvJvvQClOXL4rbbqofwuedT9o/HARC3bkH9+AOEoiQbXkwD/HXYIGVZ+J5/tl7CDaDOn4f66UdoDVCWzRgxiuJ3P0Za+CvKogWY3XqgHzO+xb51ubR+3DuzEfG8MxX/U09UE24AwTTxfP4pGffemZbz+J98PKFwAxh9D6bsn/9G2J1P1hUX02bCOLLuuyvpuIJhoCQJq4uHMmMa8uKFtf5cjfNrGp733qn4t7RiOeqH7yGlYexyzOEjiFxyOfr4ia5wuzRrmtn2wKZH/exjPB+8i7R1K5bfj3HYEYSvuR47s/4rzp7330GIRhO3f/s1ZWVlkJGR+qC2jVBYEPvfNrkgCCi/OLs/5OAK5O+/IfMfD1Ur3JA0RBDwfPk52jnnpW4fIG7ZUrtdqw5I69ciz5mF/58Po8z+BTEcxvZ40EeNIXTjzQ0ST+7i0hxxxbsc2ybjrtvwvfxitTSonhk/on71BUUvv47dpWu9TiEnKF9WjrRpA8rcWejHpCZA3hefw/Phu8grloMgYAwcTOSs/0sahy2YJr7XXolbcScZ5Q+K2mAcMhLLn4EYSsOyiG6Qfe0V1TInCtEo6swZSGtWU/z0C80rTa+LSwPhvhfuw/Pu1BrCXY6yaAGZd91W73PYYgoBOanUWgS4/XYy77kDdfYsxOJixKIi1F9mknXnHyEaPxNfOVZmJuIOh7hmB+y27Wr9GXPgIPSxh9XpfPsjGEbClLfSzh34pzR+wWQXl6bAFe99eD78IGHhAQB15gzEfVVo6ooxLHGqWwB9wED0MWOTjiMGV8Kzz8a1V9A0xL3OW7n1sYcnjEBxwhZFonVcLCy76z6MBIWjzc5d0AcNQR86HH3IsIR7Qs2u3RHKnN8qlNk/x93KLm7cgPTrvPRlIXRxaWJc8d6HuGmDc3tREepPP9brHOGLfovVJjdumy2KRE87ExyK5Jbje+1/4BC5IYbDGL36xG0zDu5H6d1/xurUOTWjqxA94yy0U0+v9ecgVrWn6PV3CF9yOXpgAGa37miHjqX0zvvYO28JhT/MpPDb6RR+O53QdTdiVimyYAsC+vARlDz0D8c1AwChuBixuDKlr/rZx2SfOZm248bQ9oTx5B45mqzrr3Jzlbi0eFyfdzme5CXEEglvqhhHH0vpA38j4y/310i3aisK4vbtYJogObtXhL17kp5LP+wwIhf8BvXbr5G2b8PKyMAYezja4Ufhf/JfyPPnYpN4kdLMaQOSFKtt2bsv0RNOIvz7W+sVi2717kPpI487dxIEQvf/hfCNf8D75mtQVoY5YCDayaeCbWN27FStGHKNc3TpitlhX/rWt94i6w83VMvPLu3YgfTOVMR1ayh++0PsrOw6fx8Xl6bEFe996KNGoziEnOn9+qPt29BSr/OMPrRaHclyxGgU//9exPb7CP35bwk/L+TnIy9dkvQ8Vm5bwjfdQvimWyqOed5/h8w7bkba4zzrNLv3pPipKZiDBoOmxaJYGjlszm7bjvD1N9U4rh8zHsUh+7B21DGxaB3bhkcfTVhYQ50/D99TTxC64+50mezi0qi4bpN9hK/5XUJXg60oRM89P67o1hbfc88gbU2cMMr7yUcJo0WE/HxyLjgbZUXiGG4AKyeHyMWXVT8YDuP/x18TCrctCOi9+hC++LcUvvEOxqGHYWdmxRYokwi3tGQxmbf9geyLziPr6stQ35kae4NoAMruuAft8HFx27QRoyi7+88AKD98B/Odo2mUerrBXFyaEnfmvQ+rZ09KnppCxgP3ovw6D2Ff7USjZ28i511I+Hd/SDJCaiiLFji2S1s243n/XSIX/7ZGW8bf/5r087Ysx3zrvarn6vC++jLyusQLroJtox87nrKHH3Ucf398j/0D/5P/Qiyt3Hjk+eBdtHemUvzS67WLWU+FjAyK3ngH/xOPov44DWHPbuw2bdCOPJrwjX+IvSUA0qaNsYRiDiTdUeri0oxpneJtWaiffIi8cgV2RgaRCy5KKcTNGDWGoo++QJke2xFo57Qhcva54PenzzaHiJYK4i3KGUbSmaIty5T96R7CN/y+RptTZZyKPjviFwFOhPrZx2T861GESLjacQHwTPuezDtuofQ/z9ZqzJTw+wndcQ+hO+5J2MUYPAQ8nvjXch9W3kHpt83FpZFodeItT/+RzAfvRV68sCIczjflaSLnnEfonj8nX3ATBPSjj0VPY+GAqph9+jr61q2cHLTxE2uaVVyEmMRXjWGgnXxK3O9o+5PPgK0U+lTF+85bNYS7KuqXnyHs3p1ykeJ0YowaA4cdFsvHngB94qTGM8jFJc20Kp+3uCpI9h+uR1m0oFocs7RzB/6n/o3vkcQLgY1F5LwLsRxcCdqRR2HFiYe2M7Ows3Mcx7ZlmcwbribjztsQqxQbBoicf5FjtIwtimjHn+hs/H5IScqlSUVFqF9/Uasx08oDD2B26xa3KTrpBMJXXtvIBrm4pI9WJd7+F55NuPtOsG28H7wbq97ShOjHTiB0821YWTVzpWhjxlL6j3/F/6Cqoh1xpOPYomGgzpuL/4VnaXPmSSjff1PRZvXsSeTMsxNugLEzMzEC/VP9GgAIJcmzBDrV0KyGpuF94nFyJowjd+xwss86FfWdN+u38DluHEUvv0H4nPMxuvfAzDsIffghlN18W8wfn4YFaBeXpkKw67DTrrbk55fU+iR1qWDRZuI4lMXOs8HiZ14gelbTZ7OVFi/C9+rLiFs2gdeHdtQxRC682DElrLhuLTkXn5c0R0o5eqA/hd/9VDlmKETbkYMTRpxoo0ZT9MnXSePMy2k7fADStq2OfUJXXUfZXx527CP/MpOsa69A3m8sG9DHjKX4pdfrVCW+2j1kWaDrMT+4SwVNXSmmudPU1ycvLyuhn7dVzbzRktdBFtKRHCkNmEOHUfrI4xS/+R7FL71G5LdXJM3lbfXuQ9HTL2CmmF9ECa7E+/orFf/2vfqSY4y3Mn8e6ofvpfYFAKNfIGkfK8fZ1SPs3RNXuGFfdfs5s8i8+Xcp25QQUXSF26VV0arE2+odP067HDM3F+3o8Y1kTcPg/fIzpBR2WJYjra6szSgleSsRbBtlzqyUx4783/mOtenNtm2TlkzzPfdMXOGuijr9B6Qli1O2y8XlQKBViXfkzHOwHWZX+pFHY3Xv0YgWpR/lu1oWQ/BUmc2ntLM99e3v2hlnox1bMzIGYpt+ouech50kh0oqRRrEcBi1tt/bxaWV06rEWzv1dELX/Q7bW7NUlzbmUEr//lgTWJU6Qn4+/r89QPZlF5F11W/xvjClMk45HCbzpuuT+vSrYvn9RE49s+LfSQsSC0LtUrdKEsUvvUb4/IswO3aqPM/B/Si75XbKHngo9bGSIbSqW9XFpd60ujjv0J/uRTv6WLxT30DcthV8+xYDL/oteJMnn2oq1E8+IvPeP1XbOu/98D28b71B8TMvkHnvnXi+/apWY2oTT8A8ZETFvyMXX4b3zddQEuRG0UeNQTvtzLhtCfH7Kf33UwgFe1F+nIbt9caq2aToXzYCA/EkKa1mZWQQPSX9dStdXFoyrU68AYzDx1GaIP9Fc0TYtpXMu29H2r6tRpuyaAE5v70QKcUIEwCzXTu0icdT+sh+YYemSeia6/H/+3GU1ZXj2YKAPnI0JU9OqXMCKju3Ldrp8YXftiD4jsz6L2QiewXUHJve3ZcxatuDeBbOxUZAcPCea8edUG09I1IIi59T2RsUEUToONpk0MU6cvN9Nru4pJ1WKd4tDd9zz8QV7nKkVcGUiidYWVlEzzibsptvw+7cpbIhEiHj/rtQv/sWeeN6bFHE6NYDs1NHrMBAtLGHoZ15TsIQQXneHNQvPkOwrNjs/KTJKaeGtQz45hovaz+WqepP38hwtvBb/o9PEwq3LQhox4yn5PEnKz/3ncSMOzwUb6y0de1HCiunKkx8Oky7/g0f+nqgY9uxh/G6T2RKd4ioGTadDjMZfq2Gx82w22i44t0MkFc7z6pTEW69dx9Kn5wS2xZeFdMk+/KL8HxT6XIRLAt580akXTsom3hCwoLCQnERWTdchTptWsU2eFuS0EeMouTRJ7D6D0hq17zHVNZ+HH8zzGom8y0PcQKVaWttUcTs0hWz78GEbroF4/DKjUmRAphxu4fiTTUfMnuWSky72cuZn4Zd93gDYtvww+89rHxLAavyYbztZ5lN30ic8EqErM7uA7QxcG/z5kAqtS2TELnsyprCTaw2p/pNfF+5EI3ie/W/CVPQZt1wFZ4vv6iWv0QwTdS5s8m+7goIJ85rAjF3ycavnecHazgRk0pxFywL/bjjKX7rg2rCDbD4eTWucJez81eJtZ/U/1qmkzUfS3x+kZfXxvh5fayfr670svnH5mVjbVj2v9hbTlXhLid/sczMu91Y+sbCFe9mgF5lUTEels/nmA/F6N07tiAbB/XrLx2D/6TNm/G++r8ax+W5s1AdkjopS5fge+l5h5EhWgxFG53dK3vpSxHV84+IO3fG7xtMcrtaAjvmNR9hnP8vhe9/52PDVwrFGySK1kms/Ujh6yu9rHi9Zb70rv9cAjvx33TrTInizXWvtuSSOq54NwMiV16D3i9xXhH92AmEbroVy1czNa3ZPo/Q7XeDr2Z4JMRqOiZDiFNtRt1vxh0PZd5cx3ZJjf3nhEwUhVC1Y1ZmzbwvkJqbvbm4TArXCix8SsUI1zQ6Wigy91EVrTTOB5s5xZucL3C0QGT7nObzAG3NNJNb/cDGzsqm5Mln0YePqLZ0Z3u9RE84kZL/PEv497dQ8swLRE46BaNvPxg4kPA551P8vzeInnF2wrGtvA5Jzx8v856QSkKoJLnJFT90GOE8ThdmkUVlHnFbktAmnxK3b4eRzmOJqk2PiSnkS28Elr6kEC1K/PMq3SKx5L9JnmzNkOQRPTa+ds5FMFzSQ53f3QKBwJ+AUwEVeDoYDL6YNqsOQMzhIyj84jvU999FWbwAZJnoCSdjHFq5aUY7aXIs0oNYwpzSFBLmRM88G+9H7yHo8fO+6P0CRP/v/JrHR47CFkUEh2o0qeQ2GfxbnR1zJCIFNYVMpYiRPFftmHb8iWiT4qemHXypTvBthT3L4s/suhxh0nVc8xCO0M7k86LSrS3PvdBxjMme5Yln1u0HW83mb9DaqdPMOxAIHAMcDhwBHA3ET5rsUjskCe2ccyl78GHK7vtLNeGuK/qE4wif/xvsOPHbZtt2hG/+Y9yEWNrk09BHjko4rtm5C+Grrkt6/u7jTY58KEK7gSZUea9o224nEzo+xmDeAcDo0ZPQFVdT/NzLCf0jsg8mPhWmw0gDhMqxRNWm27EGE55ydvM0Jkpm8oiLsp2w7RcRq2HKfTYIw67RyOkV32BBsgmcq6Vj/d0lBeqUEjYQCDxE7Jc4CMgG/hgMBucl6m8Ypi3L7l+0ybBtePJJeO89WLUqlsd6zBi49loY75Coa8kS+M1vYPF+SaG6doUnnoAzzkjZBMuApVOhcCNkdYahF4Ik6DBrViwFwGGHpVzv0rZg2TuwZVbMx33wSdB7QsqmNAqrv4Cpp4GVPNElHQ+B4ZfCoTc2uFlpYcss+OZ22DQD9g/RzxsEEx+GfpObxLTWSMLXs7qK9/NAD2Ay0Av4GOgfDAZuyy/YAAAgAElEQVTjDtZY+bwPJOp8fQwjtosy1Z2UZWX4Xn4BZf48MAyMg/sRvuo67A7JfelNTVPeQ7YNX13uZd2nqRV8ED02o/8YZeSNKah9GqnrNfrpHpXFU+KHBWZ0spj8Zoh2A1t+vHdT65BTPu+6+rz3ACuDwaAGBAOBQATIA3bVcTyXxkKu5Z88I4Pw9TfRfBwSLQNBgOOejTDzXpsN30iUbnZ+87SiAiteVRh6hY6SxnrXDYFWCus+S3wflW0XWfqSytGPJC7+7FJ/6hpt8hNwQiAQEAKBQGcgg5igu7i0SrRS2PC1xOYfRcwUJ8eSCkc9HOX8H0Oc+EqIvOHOkTDFGyWCbzX/0mxrPpIp3eL8MNq10HWTNjR1mnkHg8FPA4HAUcAcYg+A64PBYAtadnFxSQ0jAj/d42HTd1KFYOUGTPqeZjDqFi2l2HMlE3qdYLLw6eR9o4VNG4FStlNg4dMK22dLGGGBrC4WvU42GHCBUfFdTS25jVbziNhs1dQ5VDAYDN6WTkNcXJobtgVfX+Flw9fVZ8MFQYm5j4hEiwSOfDB114C/QxIfsGDT5uCmmwPtDQp8dYWPgmDlrHnvComN38vsnK9zzKNRBAF6Hmcw52GLaJzwz3Jy+7nhgg2Nu0nHpcVj27DlJ5FFUxRWvS+n7NZIxtpPJDZ8m2B+YwusekempBax2n1P1xHkxAJ+0DCT3ic1nXjPvM9bTbgrsARWvKGwcmrsWmR1tel2dGI7lQyb/uc27sLrgYgr3i4tmu2zRT483cen5/mZeY+Xb6/x8fZ4P0v+W//cIRu+ip+AqZzIXpEVr6Xuo+59ksmAC3QQawp4RieLMXdqTba9P3+JwPZZDn5qS2Ddp5XX9KhHInQ50mD/WEFPjsWoW6N0H+96URualpkdx+WAIloCC59W2TZTQisRyOho0fN4k46HGnx3vbdGpsGCoMQvf/aS0w6616MATyq5R7Sy1McTBDj6kSjtB1us+0xizwoJvQywwdRsFvxHoXiDyKBL9FTTpaeNHXNljJDzSUu2Vj5ZvDlwytthVr0rs+l7GSMCmZ0tBl+q09bNqd4ouOLt0qwp2ynwxcVedi2ovFX3LJPY9J2M7FcxQvGnqkZY4NfnoNspdU9WldExuQhldamdUAlCbJu/mmXx010SRlnMOCMEW2dIbPtZpmC1yLi/Nm6Ynbdt8u+h+Kr3EWXof55B//Pc1cmmwHWbuDRrZt7rqSbclQgJhbucbfNhx7y63+IDf6MhZyQWtexesfJrtcWMwrxHPUT21rTNNgWWv6Kw5afG/Wn2Ptkgt7+zq6Pjoa4rpDnhirdLsyWUL7D1p3rEC1sQ3lN3/0PeUJvh12hInpoC7m1rMfpWDTl+Jl5HVrypULgm8fcyowKr3m7ceG9JgYEX6ohxvitA2wEmw69zFyGbE67bxKXZkr9IIJxf9/mFPw86jKhfyNqY2zXa9DVZ9b5CwSoRUYK84RaDLtLpckTdZqKlW5I/UHYvj0XPZHeP+fcbYyFz2NUxcV7xhsLeFbGHi5xh03msyeF/jpBxkOvLbk60KvGO6KuIGCuxrGJARpU641NHIompJTxq7dh2bJfg7iUSksem/3kG/rzm+4P0tY9lqrPNus2ee46HjGSx1SnQ7yyTfmelz2WgplCkd/diid2LZRBs8oZaHHJ9lL6nN7zbYtjVOoMv01n/hUxkj0DHMQbtBzXfe+RAptWId0lkGmF9IVA50zKsbUTN9eR4JyNL7ZrOuGbAjnkiM+/zsOtXqUIMF02x6Hu6wZEPRJtNBZqq5A2zyOpuUby+9q6Tgw4xOPFfcrPMyTLwIo0lLyqUbXe66PseWLZA/iKJ6bd7kTMi9Dyu4QVcUqDvqe4iZHOnGf5ka09EX1VDuMsxrT2URH9ofKOaEaXbBb67wcvOuXK1WWx4l8iS5xR+/nPzrOgiCJBRy0rkGZ1NRt0a5ZR3w2R2bCDD6ok3FwZcqCMoqX+3SIHI0v82/7wnLo1H6xBvI0g84S5HN7ehGdsbz6BmxqJnFYrWJZq9Cqz9REZr5KyXoXyBonUCRpKpcds+qfuss3qYnPV5mDG3aXjil8FsNoy5TePwe6O0H2pWFpaIs3mnKjvmSUT2NoJxLi2CVuE2sayiJD0MdHMbqtypUexpbuyc7+x2KN0iEXxHYchlDR9NsPF7iSXPK+yYK6GVCmR3s+h6tMlh90bxxPEF5x1iwivJx203yOSIB6Nk1nKm3hBECmDHHAnbttm1SCZaKODJthl0qU5mp5h90WI4+EyDIZfr7F4qUrYdvrnWjxFKPK4REtDLhJRisl1aP61CvFP5GoLQPF0DjUEqGd7MRtgTsu5ziWm3eInsqXzhK94osfwViYKgyOSpYZT91pb7n2uw7CWD/MXx/8aethZj74wy4AIDcb8u+Svgx0dU9i6XsG1oO8Bi6OVag+0AjBbDT3d52DxN3lfD0qZqIZRlryh0PsxALxPYNlvCigooGTa5AYsJz0TI7mFVRHnEI7u7lTy5lcsBQ6twm6hSZ8d2UWiDT+nfSNY0P7J7Ov/g5QybLkc07AKVbcOCJ9Vqwl2V7bNlFj5d8wErynDsvyO0G1xzoa5tf5OTXw8z6OKawr32E4lXJsCylzxsny2zY47M8v+pfHS2nzUfpj/XtBmFLy72EXxLrVJ8uHqUTGSPyLpPFTb/oGCGRGxTQCsW2TlXZuoRGWR0cnYRRYrglwdVyna0vMLFLumnVcy8fepIouZ6TCtePQgBnzIAQUjfYo9h7iGkL8QwdwE2stgOrzIEVXZ+iDQV/c/V2fBl4twVXQ4zyRvasDO6LTNEdi1wFs0t0yVG/7Hm8faDbM7+IsTyVxV2LpDAhrzhsd2Nsrdmf70Ufn7AQ2mcZY7wLpGfH/TQbXworpumriz7n8K2n1P5OcX/G1hRge1zJDodarB9dvxxIrslFk+R2DxN5vgXwrQNuLPwA5lWMfOWxAxyvJNRpB5UfR6JQhsy1LH41bFpO1dUX0dh6D0i+iIMazuGtYOIsYzC8AeEtMXJB2gCuh9rMuJGDdlf88eeN8xg3N8jDW5D0Toxaby2UyECyQNDrtCZ+FSEiU9HGHZVfOEGWPxflZKNiR8UpZslFj+fXjfa5un1n80bpSK5AZMj/hIht59Jjeq++ygISvx8f4Iv73LA0Cpm3gCy1I5c/1loxnZ0cxuC4MGnBNI647Ztg1JtOhbx0s1FKYvORJV6Ikt1m9LZtkXEWEHUWIdta4iCH6/cH1XuiVDPNHOjbtboMs5gxWsKJZtFZK9Np8Mshlym1fAzNwRt+1uIqo3lUIXF2y49M8ni9cmvVfGG2s9bts8VWf6KQuFqEUTIG2oy5Aqd3L42Wkl6XBmFaySO+WeYTd9LFKxK/EDYNktiz3KhWpHf0u0Cq9+XKVwvkNvX4uAzzLRsUnJpnrQa8S5HlTs1WFRJWF+CaSWO1bIJE9YXkCUdXeuxbdugKPwJmrm+2vGoEcSnDCbTM6HeAt5ptEWn0U1TFLbToRYdRphsn5X4lut2THo2oEjxi5pX76PWTtSWvCQz+28etKJK0d85T2b9lzLHPBZNm0gK+/S6bKvzw8UoE9gxV6LdQAMzCj/+0cOaj+Vqybpm/cWmx0SDox+J4M9Li3kuzYhW4TZpLEyrIHkfu7hOY5dEf6wh3DEswvrifZuQWi6CAKNu0RIuynU72mD4tVpaztXzeAMcKtYIsk3PSakv0BZtFJj7j+rCXU7ZNolvr/XS43jnKjmp0n5I7AEWz8VVHRvfvtQG393gYeXUmulxLU1g/ecKn1/si+UNd2lVuOJdK1IISazDy4xt62jGBsc+UX11rcdtbnQ72uT4F8L0OVXH39FCzbFoO8Bk2HVRTnw1nNKMORXyDjFRHVK5HjTSpMek1Gf5S15QEkbJAEQLROY/5mHA+fGr5KRKVg+TYVfFYu07JUm/2m5gLGHVrgUi6792dg3umi+n3cfv0vS0OrdJQ+JV+u+bASeetXnkHrUe1zDzsWznjUamVYBtWwjNMQlJHDRzBxFtKZZdhiAoeOQ+eOR+dBxt0XF0BCMMRhjUHBDTHLk3/59q3FlyOb5cu1aVakq3JL/mhaslBl2ic9TDUdZ9JsUWaImFOpZsFbA15zHaDjA58i+Vm4yGX6+zaZpMwcqaF0fy2gy8WEOUYM3HMmY4+ZfZOlNk5O+TdnNpQbjiXQsU6SA8cl+ixsoE7d3wyNXjyS1bw7LLEAUfopAgQkBI4c8giCQKM2tulEZnEtJ+BSp3bEaNlajScnJ8pyAIMrKPOuXCToZtw5bpztdz+yyJ8G4BX/vUZslSioEdm7+XmDw1wuBLq+9UXfW+xMy7vYR3Vxdw2WvR+QiTXieZ9D9PR6oygc7oYHP8C2F+vt/L9lkSeqkA2LQbZDHwIo0hl8UmEGaKniatNLV7xzJiD1Ulo+4ViFwaB1e8a0m293hKIgpRYy025XuZPXik7mR6J1bMjA2zkDLtZ3RjMxZlCHhR5K741dGoUvUFVVnMQxY7YFg7E55XljrUe8GyMQjrKwhp84Car/2auZ6SyA9keY9jw1cSO+ZKiBL0OtHgoEPql3e7HEtLXoAhWihStCF18e52tMHq92SSPTwjCUIdDxpq0flwgy0zZIywgKTatB9iMu6hKO0cdnu27Wcz+Y0we1bEFif9B9n0OM6s9qbSpm9q3yGjk3O/wjUC8/9TWSfUn2fT9SiD0bdreHNq9tdDsPQlhb0rRUQZuowzOfh0wxX8RsQV71oiCBLZvuMwrSOI6iuxsfHIvZGl3Io+pllMUeRjTGt3xTGbCJqxBsPcSbZvcjUBFwQBrzKQ0mg+8RJsCXjxycNqbatt20T0ZUSN1TH3BSqK3A2/OhKxgdIFRPWVxBPuciKRDUy7RGTrDF+V1LQq3Y41mPBkBDWzfucXVVCzbcL5ifsomTaZtag92e8cgwVPmRQEnX8u8XKObJ8r8t11XoqrxJ2bEYHtswSWvWQz7uFoUhdOuwE27QbEd9UNvEBnyYsKhQ5hhYg2fU9J7Orbs1Lgq8t81ar7RAugYJXEroUSk6eGq21o2jxdYsYdnmr9V7xhs+xlk+OmRCryt7g0LO5zso5Ioh+/ZwQZnpHVhBugTJtdTbirYtklhLTZNY771UPIUMciCtXT4UliLpne8XiU2vnSbdumOPIFJdGv0cz1GNYudGsLIe0XCkPvYloOGZDqgeEQSgmAXIIpb662YccIx6Iivruh0j9h2wZl0dkUlL3D3rJXKQi9R0j7Fdt2XsgTBJJWuOl8mFErgTHC0OVoE0F0fjvosV+ubduGXx7wVBPuijZTYNmrCus+q5/DX/LAEQ9E8bVP8J0FmwEX6PQ9PbF4z/2HJ2FZtp3zZOY+UvmgD++BH2+J098W2D5L5vub3M1DjYU7804ztm2jmVsc+2jGFiyrDHG/Cj8ZnrH41EMI64uxrSiimIVPGYwg1P4HHtbnJ/TNG9YOSiPfk+OfXOtxk5PctWNG4t92m6fJ7FwgkjcsTGHkI4z9rqNubkQzNpLjO9Xxmoz8vcbOeRJ7ltfsk9ElttsUYPsckSUvqmyZHvMpC7JNRmeLwNkmw6/VkL0QKYTPf+Njxxznn0rP43UGXVTd1715msTOXxPbaRsCi59X8LSx6TjKSrhjNBk9xpuc+XmYOX9X2DJdRisREWWb3IMt+p9nMOhSPeHsvmwXSeuEbp0uA7Frtug5Ne7DqJztv0hs+Umk65HpcYO5JKZe4h0IBA4C5gPHBYPB+EpxwGFg28m2m2uYVmkN8QYQBQ8Z6uh6WxHV1zq3m2swzCJkKY5Dsx4oUkeiRmHC9qJV7cmf3StumxESWPeZjG/AjBrCXY5mrqdM+4VMz5EJz5HV1ebEl8Ms/Fcm6761CO0SUXMsOh9mcsgNGp3GWKz5WGL6HV4iVRcRowJFq0XmPCSz6l2ZDiNNds6XKFydWKw8uRYDf6Mz5natRnKs3UtFbN35Ybb9F4WPz1Ro08ek5wkGY+/W6hR9k9PT5rhnNMpFNlX2ro6tATgR3i1g6rEKO3uWO/c1owKbp8l0PTI9MfsuiamzeAcCAQWYAs2y0lQTIiOKPkwr8WUR8CKJacyKFIfkm4UsSqLfk+s/I63n9SlD0YwN2NR8gNkWbP5sELbuNBu10IyNjufQjPXY6hGOC7jZPW3Oeh22rC2jbJuIr71dsUBpRmHO3z3VhXs/Clc7i3Y53rY2h/6ppnADeNok/Xjl+dZKLHwqFgUz4T+Ntws2u0dsDUB3iEZRc+yK75fKknkLWFdvFdTH5/1P4FlgW5psaRUIgoAidXfso8jdEMUGiJOrRvLnsm7uwrbTW4BBlbuS6R2PKFRXLgEfhQtGs/JJh9QBok27Q0pSiHkvxk5xhunJhpzeFmW7BArWCth2rDp6KsKcCmXbRSJ746tV4Gyd7F612/K/5iOFHfMabykqpyt0Osx5t2nnI8wKQW43yNkdInltuo936182BnWaeQcCgUuB/GAw+FUgEPhTsv65uX5kufY/lry8Zl7Lqgq2bVMaXktR6VIkqQTR9GJZNWefqtyObh0m4k1DnS6n6xOxu1Jcmmw7fxnZOSZeT9t621Kd0VjWcApKfkUzCpEED7lZIwh7s5nTGUoSPO67jhEYc4mPNVsUx4eKJCnk5bVBTBIfbxmw8J9ZrPoEdq+I5Q3pPBKUeka0VMWbLdCpZyZqguReo66EaffHQhhTwYwIbP4sgyEnps3EpEy4V+G9lVC8uWbbQUNg0l9UcvJii5bj74L1H8PeNfHH6nmMwLBTGyHTWSPSXHWorm6TywA7EAhMBIYDrwQCgVODweCOeJ0LCmof2ZCXl0V+fiMXVqwjtm1TEv2GiL6cmqF+sfhgUfChSl3xq2MoKfZRQv2+W7LrI5mDgaUkSitablthoY4kNsx1Ns0emJYXW/BRYICglHDYnyVm3uOlbEf12WXbASaHPxSmqBBksTO6mdh1Igqd2LPb2Vtn2zD9piyWTa1yzIStc9i3hT097/YHjdYpCkUgwS3e/wqIaAor31Icq+RUpShfJz8/gl4Gqz6QMcoEuh5l0G5A+kPw8vKy8AVKmPCMyIInVbb9IqEVifgPsugyzuTQ26NoHpv8KqGX4x6RmH6Hh4Jgle8j2nQ53OTIRyLk57eeUMGm1iGnB4dg2/W70IFAYBpwjdOCZX5+Sa1P0tQXrTaURedSps1I2O5TRpDpGVenqJFEpHJ99pa9hWFtTdiuSN3J9Z9dq/NqxhYi+hJ0K1aIQhLb4pUH41V6V/QxzWJKo9PRzM3YhAEBWeyITxmGTx1I6bZYlEXBahFRgg4jLIZcrlXMiKP6GooiX0Mcv7mAnxzfKahyF0c7N3wt8cWlfuxEb/CCDXb9BDyzs8WkF8J0HJU8ssIyYOO3Estfk9n4tXOM/bDro0gyrP5ApmRT7J5Rsmw6jzUY93CU7G6p/5xKtgqs/0wGAXqfYpDZsfpn97+PQvkCkT0CGZ0tx2IVRgSWv6qwZ4WIIEG3Ywx6n2S2On93U+tQXl5WwivqhgpWwTRLCOm/YlixaYYstsenHOIYkWHbNlHDOWmUZm4EjkqnqSmR6TmcwvCnxF9TVvEpQ4FYYqyIvhobA4/UC0mKPe1Ns4Sw/itGRYUiEc3cClQuqJnWXjRjI6Z1GBmeUZhWmMLIx5jWrirnsjGs7ZREdwMWmZ0Hc/h9if0IHqUvWRiEtXkYFePEHgAZ6qFJhRtg7adyYuGGfcJdxxm4ENumfsQD0ZSEG2I5TnqdYNJhhMVb42XCu+L7tb3tLPRSWPSKWu3hopcIbPxGIbRL4PQPa9b63B+9DKb90cPmH+SKpFrzHrNiKWL/EU2YmsCfZ+PPS/5wkL0w9MqGL1jtkph6i3cwGDwmDXY0OVF9AyXRb7Dsyqesbm4iqq8i0zser9I37udsoklTxZrWXiy7GEmoRfhBGlDlbmR7jyOkza629V4S8/ArI/DIB1MamU7EWF2xSFiGD1XujiL1IaTNqHY9EqMT0ubgkfsSMZbsJ9zV+4X1hXiVQUm3+vuU/njlAFFjLaZVjCy2RZV7pJwiQCtK3u+gUSZl2wTKtlV9I0pB0G2BcL5Qp1mm/yCbEb/TmPOwB72s+gCy36bniTqr31MSvhXkL5JZ8oLKiJsSP/xsG76+2svG/bINRvaIBN9S0UoETny54asnuTQs7syb2MyzVPshrlBZlFIanYYqd0MU4uUsFUketCMC6XGZ2LZOSF+Inh8mErHxKP1RpQ4J+3uVvnjkPvtEsABJzMYjH4wgiJREviOsL6o+PmGiRpCosQanbe417CJCWF+IbsZd9qjAsHahGevwKH2SjhlLGxD/oZmMVPKW9DnJYOhVOpuni+z6VULOAG+uxfL/qexa4PzTCO0UWfSsknQ3ZzyGXa2T1c1i5VsKe5bG7p2c3haRAoFVb6lYSWLDt/0iMuKmxO2bvpfY9ENi+zd+K7N1pkiXI9yNNC0ZV7yBsL7YcfZs2cWEtQVkeGrWwhQFFUXqgGZuSPh5WeyAKNQ9xMG2bXRjMyF9EZq5GYhQFi23fREe+WCyvZMS+tTjiaBhFhPRgw5nrb0omXYpNsljlE2r4X2IgfN0Vr2rYiRY18zsZjLoUh1JhZ4TLXpOrBSy/v8XZsXrMj/d68EMJ34wb5slEdkL3joE6/Q+yaT3SbFrbBrw8Zk+di9O7edoRJzFff0XsuPmIEsTWPupTJcj3I00LRlXvAHDTJKPAzAcxN2rDN7nC47nA5TwpeAmSGzbHkqi36GbW4kfOWIQNVZQECoi1382QpLwOcPcS0hfiKavi7uRpj6IKNhCJiZ7HHpJyA5vCumi02iLkVfBnCftGoWPlSybQ67XaiTBsm2TkDaPqLGeg04vYcJQD/mze7Dq+cMp21xTofVSgWixEDchVW1Y/a7M9lmpv5lldXWeMScTd4jlk3Fp2bjiDaSyV0lwcHt4lX5YdoiQNg+rys5GUcjaF2ExpE5WWbZGceQzjARJrqpiWNvYXfoKOf4TUKXOcfuE9ZWURqZVSWWbTgRUuQ+WXeIY5qdInVHlTmjGdiJ61WINvfDI/dNabOL4x0HtGGXtxzIFa0QEEfKGmQy4QK+Y9ZZj2xZF4U/RzH1pBUTI7ltCdt/d5I3dwKzfnU1xsGO1z2R2ttJSu3LLjOTpZsuRfTaB85wXCrO7J3eHZPdsPeF8ByqueAMeuRcRYwnx0rGWoyapkONXh+NTBu1zwcSKL/jUoQn85KkR1n5NSbjLsSmkJPI1bf0XIgjVF6tMK0RZdHoDCTeoUi88ch/ARjO2opmravQRhRwyPEfuK9Ywn6oViaJGEFVasS/plHNZr1QRBBhyuc6Qy3WMcGyTjpQgSi+kza8U7v3I7rOHIX/8jplXXFjteNejzLQUlLBSDNqQvDbDr9OSJn0aeqXGijfk/RZiK8nqZjL0Ctdl0tJp1SlhdXMXpZEZlER+JKyvwLbj3/Sq3AtV6pZwHEXqikful/R8gqDgV0eS5T2KDM/oegk3gG7WPvOAae0ltN8iJEBY/xXLLq2XPZVUioJIBl55MDm+kxEEAUEQyfGdRIY6DlnsjChkVcSCt/GdjmkV7SvWUDOOTzM3UhL5Pk02Vkf2JRZuiOVLcaL9qE1k9ancqXLQSIPD7k1PDpLsnslnyu2HGBz/fJgxtycXXW8uHHqXhrddzXF9eRZj74nWO2+6S9PTKmfetq1TFP5qXzX2fdMaHULir2R5j0GVqscJC4JAlvdkSiNfEzU3Vn4GGVXqTpZ3UpNUsUmWuzoRhlkzVM+wnPOFpIaCKnUj0zMew9qGbVuoci+k/fK0CIJIhmc0GZ6a2RFLozNwWgyNxYyHa4zZ0JhJHmxKlkaHIzbjy86ly5EmI27SHDex1IahV+oE35Ip2x5/pty2v8lZX4QdHz770/8cg7b9LJa9HNtIA7GixYMv1cgbWt1lsmuxwLKX1Fg/AdoPivVrP9h1rTRnWqV4F0e+jfvablo7KQ5/Ra7/PCTRX61NEr3k+E9FN/OJ6mswrUIEwYcqd0EUGldIKm3KRbfiJJxIgm5sJqQtwqcMrXjoOPnskyGLXVDlbqhyb1Qp5veVpex9G5TWUxpdiWWVIYgqqtTTMQd55Yaf+FiUopubkcTkbzrpRBRULAetsnQBvagNp72ffINMbfHn2Rx+f5Sf7vHW2LyT1T1WmLg2wl3OQcMsDnrc+e1g8Wvwxe/9FRt5AHbNhw1fyhz51wh9T6vbBMKl4Wl14m2YBUSNdQnbLbuQsD6fTM+4+O1WGZq5AcPaAdhEjF+RxTx8yih86oAGsjo+PnUoESMIKYTfVcWijNLodxjmTrK8xyEIAqrcjaixvE52CAhkeg6vdsy2bUqj3xHWl1KxVmCBZqwlaqyhTQLftSCIzulWIGnETEOgSF2r7OasScHSzmz8qBdLhkYZ8bv07yw8+AyTvKEhlryosHelBKJN3hCLoVfrNba0p4tIAXz7J6oJdzmhXSK/POih+4RQWl0s22eLLHtFYc8yCRtoP9BiwIV6neLlD3RanXhHjVUkEzs9jlsBQDO3Uhz5CpuyascNK5+S6PcIgoxXOThdpiZFkQ7CpwwjrM+nLnHXEWMpitEFnzIIrzyAiLR0X8hh7YiXfjWsLyKsL47bXzc3UhL5gWzfpBptstjBMaZeEtuiSrUr+Qaw9WeR1e8paMUC3nYxQcgbn/rn/epo9m7cgq9zzXtDK/Kw+qWxgEDh2kqhs22DsL4E0yqK3RvyQGSp7hka2/SxGfe3xltIXPy8SolD0aeSTRJLXlQZ6bCbszasfEvm53s9RAoqr+He5RIbvpE47J4ogy5OnkpWL4vlhVcy3bzhrU68nSJGKkiwcBnWFtQQ7kqihPVFjSbephWmNPItUXMTlcItIpCBKvVGEPOJ6MkXNA1rs/gAACAASURBVKP66n1x5iLZ3smURL5BMzdRuWAoE2/xsCqWFaYkMm3fbs6Y2yT2kEyMZm7EsqM1Fm19ynA0Y1PCqBevHKhVAi/bgh/+4GH1+wpmtPLXvPItla3XwfA/pjaOJGaw5e0z8fX7kbyxG/C0DWNqInsXdWHtK2PY8vngWL99LxMRfTWl0Z+w7MoHUUhbhFfpR5ZnQsohj5ZtM9OIst2y6CJKHC6rSddXNpoGH+lh8m0LHwKHySpHyx7EWqpZyabkNhZtSI9CRothzsNqNeEuRysSmfsPD71OMPEfFP8tY/X7EiunKuQvkbBNyO1n0ec0g6FXJC7x1tppdeKtSN2AOTjNVKU4syPbNpNGd+jmVkyrBEls2Py+tm1SHPk4zizZwqYEQbTo3fUK1mx6B81c4ThW1QgTScygjf90NHMHurEBEFClvpRGv0V3yD5oEUtQFdYX45H7kOWZhGEm8V3bJejmTjxy9cIUqtyZTO94QtrPmFWKFQv48CoD8Ks1d7E6MfcRlZVvKuwfJ22UCfzyOCh5MoMuTa04QMcRWXx9xTmo7UrIHbiDcH4mRcs6VYwtyDY9Jhpo5g5KIt/FeQBFiehLEFDI8h6T9Hzf6xGmamFWW0ZFRpWAKHOh6mecUvnQ2/mryMq3FKJ7BeaeXMTMo4sJVfnlfm1E6S2EeMSfQ7ta1FCTPKkkoEqPy2bJCyqlWxPbFtolsvgFhbF31pzlL3haYe7fPdU2Fu2YI7JzvkTxepFxf2u8ykPNiVYn3qrcFUXqgm5uitsu4MOvDK9x3MbEdkxDB2Bi2VqaspQkJqIvc3RvRPVVaEYBHqVnUvGO9ydWpY4VM2iADOFIisNfJq1gE9vNGcSy9ZR2Z4rEX2XzKf3wyr0J60sxrWJEQcUrD6rIZpgqlgHrPk+8wcU2YPWHSsri3fskky5Hmmz5MZsdu2qGknQ90qTHJJOSyCLHePmosYZM+wjHePVZepTHI9WzutvASsvg0UgJfkFghKQy4w4PK6cqGGGBrceWMevQYsw4v9p1tsmFZXs5UfFyvScTOYXpaM/jDVa8qSbMviiqNn0mp6cqTvHm5PaUbqs5K48UwOJn1bg7Qm1TYMXrCv3O0ukw8sDL09Iq47yzPJOQxY41jgv4yfQchSy1i9OmJK0rKQrZyA1cexLY5ypJjE2UwpJFeOV+SKKzj1WVu2LbJqZVgpWgMLIqdaGN73S88hAksR0kEN1yYjsonWdkstjBcRu8IMj41eH7YuLH1lq4IfZKv3el8y28Nyiip7gvSRBg0nNhep2oo2RWfj8l06bXSTrHPR9GENiXyzwxll1MRHdOE/y+Hk5YjqMQmw/0MAufVFj6slIhXBvPLMF0WDyMAB/oEf4eSVa/NEaP40z6Hu/QPtFIW/IqxVe3Wf7Sl9QahTuqYoQFVk5Nz6aulkarm3lDLIwt138uYX1JLJGTbSGJbfbl5o4vvrGIjF4YWuIfpir1qNfuP9MsQTM3IwoqqtwrsW836RtALJZdFCR8yhBKoz8Rz00k0h7LCrM39GpsUQ0FReqETzkEj9KzWl9rX2y79P/tnXmcXFWZ97/nnrtV9Z59YwshFQiQRMIOAgIKyBIYFhXnfcXdGWd0HMcZGR3FQWd0dFxnRhkHfV1QBMRRRARZBWQNEALkhpCEEEKSztrdtdz1vH9UJemltu5Urznfzycf6K7t9K2q333uc57n94gWYmo189TePLXNI4a9Nl4YxX/VyuH33Adg96uCdXeaKAVzL4hoK9Mi7nbA+f+vwLYXBOt/b4GAw84L+02xqS1Eqso+wq4k4YW4esXKyjDk2N+Yfaxhd8+rr8rlj1HAqjhgQZn6wk1xxM1hnufjkIJSzP5GDxm7HfGgg7+jeKBSUxIOPrvo+90ojrgs4sWf2MQVfFcMWzG3TJSfr6PBOL/9wEx6T0jxBhBCkrYXk2ZgiqQSTfZJRHEnQTyw1NCUs2lyhjZQobj5eB9BvKE0WQYM0UHKOoq0fcIAkUvqEG/L7CAJIG0fBxgUwpV7h0iAg2XMQVEotf0XUcQE8TrC+A1aOAfXKtZSZ/0nyAaPU95Ya2ikrAUNe65KtB2qmHJsTGcV+9Ypx8SoGO7+UHEwgb+rKFDLv5Fw0JkRZ/27X3am5ZSFiikLy1dZmMYk4iq2BYJ0ySqgPPk6vBfFVoOdq/qe3I06g+AC8IfQHyDea+KQ6/JdvNZrw36TkefJr+Y5e4vLRfd2YAjB3IsHTtzZX2YsTTjs/JA1t5e/qjvknIiDzhh4FnYqz0HZd5/2A7OZaMKK91AQQtKWuph8+BxBtK5omoSLbR5M2n7TkKJupSK68v9LmPTdDE3UTrLBIyTKp8Xdd1II481VR5fte959Qpu2l5CyFhHE60mSPJachR+9QjZ4qPxjKZALnsQxjyCI1jdcuA3RSpIE+NEKDOHgmPMAgyjuRBFiyWkN8S8RBhyxLGLb8xIVDYy+rDRkrgy5+4Mur97T9/X8XQZrfmUTZgUX/KQwqIoF1zwKP+rVvdsP2zwEaVTu5JkiDGYIg40Vqp4ApkiD/gUr7Ssddi+o733yy+jZ9/xsH+Huzb3TCyy8ppvL7OFrSDv72z5OW3FE3R7flaYZCQefE3H6l/yy78HC94S88COLfGf51IlhK+b/2YE50UeLdz+EMEjbS0jbS/b+Loq30e3/kSTZBUgscw5p69g+AhTFXRSilSgVII02UtYxCGEWBycklatY8uEzpO2lSCNdHCGW+xX1pCXiuNBnw0IIA8fcN0ey219f9fFRsoUgWkshWsVghdsQ7SRqV8XblYKd+ZvZU28vSAESVUrHGKIV2zyUFufM/W7IWfThkFxncePK71WGlp6WcOrfGdhtsOGByq+x4QGTDfdKDjmn/jp6x5pLU3Jy6aTXN4a25CG0uGdXfbwlBCeYNhvDypu+r00OOezIAu1P7xPTw29qYfOZOfwptUPw6UZfsXs5Dnm+Rqrm0cgfVvGWNpzxFZ+TPgNrf2uikmL6yu2o/Jim6YqF/ydk+bdtkqC/uiuOuDQ8YIdKaPGuQS54lqz/SJ8L3SB+BT9cRat7IdJopcd/gEL4Up8KjFzwDE3OKQRh9c1HiOkq3E1Hehn5YHndrn+m2UJSpXdCqerT1QGiZFefcr16sOQhNNvn0OP/rsJJyUTRd8NM9ZuhWdzQW0GSdNGWWrZfNrBCwCn/FHDMe4sRmr9bkJ6qOPq9AQdnWrj1muqDCVQoWPd7c1DiDdDkLMUxDyMfPkesehDssbXN1JXr/6DTzKtJzNMVBLUgYM2yHpascJBh8fhMeS7FkuumsPITO+g5rHJqbbowuLSfCK+Kw5r1QZ1VrgQaidMKR76z/iqWE/4+IDUtYfUtFp0rinXekxYkHHZ+xPGfbGxTU6IUvwzzPBoFbE1imv3dHKkMrrLSzJDDXWc2OLR4VyGIN9PjP0K5js0o2UK3/wcsOYN8+MyA2xO1i57C/ag6DnFc8vsoTsmpjSHa6WhZwvbtlQVa1OHHYhrt+HUUPkpjOpYxBds8rDRCTdBmXEpX4felCUK9v4j1fymDeD2F6CVS1sK6H1OJljlqQI3w1hdh40O1/754iPtyppxMixxEG2cvXCH4l1Qbt4d5nogCtsQxr5P02Qpd+65u3K2SeT9txd1R/BwdfGczhz/nsuZLO3j6tJ4BR7sNwXvtNE39TohThGTPyOVKpIcyjHmEOOaaiKPfE9HzuiCJoOUgxSBK2usiUYrrC13cF/X6HAUBq4Cn44DPuW0cLseOZI6dlYxBCsEKqrXah/HrRHHlVu9ixDkYR6H6oj9bHo5hVH/rbPPQirXuUCzls825+Ht9XMpjiBY60ldgiL5/R5RsKTU17V8dsB+tbYh492fzUwb3fRR2r639DS9XdTIS2EJwlZ3mKjvNV/PdbIwGxsYvfnwXa9/RzeE/beFNPS5HT5EcfU1I0/QUT4eSO6MCa5MIAcwzTC62XI42B37mTjRtjjBMVieV369Fsvo+xNo4wotDphuSJdIacadNIYon6eHiV2Ghr3D3YkOScIPfw5fTxSHiOZVwR1igWyVME5LzLBdrhI+HFu8qVBt9VrrHgBTBQGq/oaYxufTf6X2mvFdCGrWdgtLWEvxwPVFSTsAFlixOYk9bS0qbs+X/Dsc8YoBwA+TCZxoy2EGp4emOe/xfHHZV9ifbS/OcmGM/MPqDCXZWSVkUZsS88Le7OMpKcaK7770/zrI5zqovODCEYJnl8m2/h3LXa/MMyVUV8t1r4pAb/CwrSqkXg2In6GV2inMtt67XHw88GtVwYIxD1sYRj0U+/xsW2NLrPbslzHG1leZtw7hn0J8J2aTTKEQDDo/ARZCuep9iNcaesr/aVKtk2Pu6QmIalXaCFH74IlG8DVNOosV5K1JM7bfuFClrEc3OGQMenaiAMBr8oIhyGKLB/qoUo+7NT9aOuFPTEk75nI89vG4HddFUR9RWz32qcYGd4q+dZo40zL0hRathcLq0+We3lY4yeYiNccR1+S6e6JUzT4CXkohvFLq5t8qm63ijs4Z/fh64yc/ywyDXR7ihGJl/x+/hyXDkWvV15F0Fy5xBGFTOQwtSKARUiUAtOZmUdSy7C7+jXArGMefjltIGpuzANA4jSipPdTHEJByztjlWnOQJovJjvaDom50LnqU1dQ6OdTC2eTWFaBVxvB0hrKrt6koFqIaUFhq4ZuPrwTtXyIrNIHtw2hMuuTXHpAVjo0b4dNPmD5Ff0VatA8HFDYhyz7dTnGe5PB+HbFcJp05tx95VWYB/EeQrlhdmgduDPG8xnVEZVtJoUhjUMrbzkqiMx2aRbuA3UYHjrf2boFUvWryrkLKOoxC+XLEsbk8jRiFaWeEZJK61AMeaS7tYRj58ljDehFIR0mjDMecOaNJpS53P9uzPgXJVIJK0vagu1z0/Wk1So1Oyt3+1EAYp6yioo/zaECmk0TroSpX+pKyjsc1D9+s5ytE8KwGh+nQn9qdjfjKqwr02jvhNmGenSkgjOFM6nCZtHooHSoMELrBdJjVoh04IwbGlvPhUy6KzSh3KihoDNlclEavjiIw5/lvUj5UWq6rsCRyMwes1KnJeiiNipZAjcDLT4l0FaaRpTZ1PT+H+fpt6xcaTYj1vQpLvKVVd9KY4z9ItdRra5mxsc3ZpjmZcsUlFGi6T05fR4z9IEL+2t/zQNKbhWseQthfVt/i6yr6GVhomhMSWh5CvIt6GaEUIE4GJIVpQKiJWO0AppNGOY80nZS0alojtkHNjphydsO35ymI365TRM/+/0c9ya5Drc712d+RzmrT4M9Pl8Thgo0oQwBGGydmmw5UjmEvtTU5VP8FFwPYRKjEcbq6yUzwVB6xNBn42XOAM0+HHUfUS3EgpEhh28zrQ4l0TW86kI/1O/Gh1cTakkLjmkZhyTz5Z0pa6FD/y8KO1Jc+RJlL2sVhy2oDnK9Y0V8+lS9lKW/oioriLKNmEEGlsedCghM425yKCP1V1/6tlalWNZuc0omQbYZnyRkseRHtq2YATlCoJwXBfYhsSjnl/wCOfSRGUcX+aujhi8V+Ozibl78M8PwtyA5JOEfBAHHKFZfI/TZN4JY6wgbnSHLRPdyOZJgy2VhHnVgRH1Kh8Gi9MNiTXua181+/h2TgkS7HcYH6piuccy+X+rF+1M3aOIUes6mRIRz2TyVjAjcChgANc73nerxu4rjGFEALXyoCVqXL7gr1RdqMwZWtFI63aj23Dlgfjl5nlWcQiZR495LUJYdGeupRcsJwgWk9CAQMX2zyEtH1c2c7JkcyLHvnOiPbJ8KdvRWxZXmyfT01NmH1azKlf8HHr8MyohyjeRS58mjDeWDpxt5TSYceVbT76Q+hX3S14NPJ5v9PEUWMkDXGSabMyqJxKWCwtppZpXskrxUNhgQDFSdIpe5+xyEHS5HOpNrbGES8mMXM7mpjbE+09gZ5YpTNWAG+2HJRSPBYFPBj5ZJWiXRicbzkcVaaEc38Y6inz3cB2z/P+PJPJTAaeASaseI9Xmt1zSAr5AdGxwKXJORHbOrjCI+tDCJMm5wSanBP263mGi2Ovhhnn5ulcYZDfBlOPVaSnNi7PHcad7M7f0WeaTqK6iILXCePNtKXe3kfAlVKsL3NJ3pvXVcKqOGLRGBHvd9hpVsURD5fJxc8zJB9x+lYLJUpxg5/lwcjnjVKE2kaOpabN3zhNNDe6s6ZBBErxoyDLE1HI1iQmJQTHSIvjZSuG2PeefchpZlOS8Kd+x0MC51suy0yXLxS6eCgK+nRt3B0VWGal+IjbuIGgQtXIaZUjk8k0A8LzvO6SeD/ped7cSvePoliZ5th80yY6SiXs6nmebG4NiYqwzFY6Wo/HtaeM9tLGPevf+AnZfLGiZwNt3MkCVjONAMk0enhLqokPztiX11dKce6GDbwRVxfwm2bOZFFq/3LcgVI8kc+TKMXSVIq0MfSy10gpfrx7Nw/mcnRGEU2GwVLX5X3t7Uw2+8Z/13d28rPu8k7lx7sup6VSrA9DTCE4NZXinKamUa9UCZTirzZv5uH8wHz2bNPkK1OnsrjX+xErxR09PTyYzdKlFJOl5IKmJt6cTvO1HTv4we7yQ00k8JnJk7mybVCXfRUPzpDEew+ZTKaFYsT9357n3VTpfp2d3YN+kalTW+jsrGRXr9HHpzbDeYyiuJMduZ8BES8yjf/gFDrpW1opUJxnunzKbdkrUJ/J7+bhCl18AHMNyQ3pjrom4ZQjUYr/CbI8FPp7S/xmCINTTJu/KDNhp5HHaHMc8YHcLrrr8DuH4s7PYmnxWbelbI35SPETP8v3g8rlvkulxVdLnZXVCJTi/dkdbKiSE18iLb5ex3PtYerUloofhCGfjjOZzEHA/cCPqwn3REUpVaoc0RyIhPFmih228DOWDBBuAIXgrsjnnlLn3pYkZqmwKrZsCeAs0xkgsLFSdKuEoI5A6+t+Dz/tV5u9WSX8MixwfaGL/QnWanFn5Nct3FCsdVoeh3ylMLpByBNVTqYAz8chL9dwZARYH0dVhRuKw6OjBr0HQ92wnA7cDXzU87x7G7KScUIhXFMafLC1WPYmJ+OaC0jZQ9/820Oc5ClEL4GKseRsbHNWA1asGQ6M0ji855jJGgaO1duDAm4Ncvw2yPNSqcGjHYHq57XYArzVcnm3vU/aC0pxo5/liThgWxLjCoNjpckVdpojy/iQrIsj7qvS8fhwFPB0FLK0jpb6tXHE8ijAEoKzLYfmOpwf/SGK0jNxWHbyz+txxO1hnjeSBEfAm6TNeZY75KuSSlSzJoBia93LccQRNbxfpKCm+ZchRMPsv4a6YXkt0AF8NpPJfLb0u/M9z6vtQzqOyQXPlEaO7TsLJ3GWMN5IrHbR7Jw2pOdVStHjP4gfeiRkS781seRsWpyzMMtMu9eMLrY8GNOYzmtJO3GNqt6Xk7jPF3pX6ac5wuA4w6JFGrzddJnZy7HOV4pP53fxTLyv0qNHJdwXBayMI651W1jcr3rhrrCw99NTjgh4IPKrindnHPN1v5tn43BvHfpPgxxnmA4fdpqqNp/MGWLqowD8KQr7iPevghw/8HPs7nXk7osC7o58Pu+2NKxhCSiemKoIuARm1/F6hxlmTfOv+YbZsAaeIYm353kfAz7WkBWME+IkTy54kvKDCxJywTM4ZgZLTi1ze3n8cB35aCVB9CoMaLqNCONX6Sr8lvb0lRhiZFpuNfUhhCBlLabFr+1+VSkS26gS3mFaXFhqwNkcR9xWijQ3JhHrKwjKVpXwoyA3QLzzdUS+uSqNWb5S/FOhi5f6ic9WlXBLmCdC8TG3shHMeZbLL8M862pU1NRiZRTwfT9btj94RRzy9UIP/5xuUK0nsNi0eKlKOeQCw+TYGlE3FKPqcyyHV/yorD9oE/D2Bhp5aWOqOimEK0hUtXbzkHz4fJXb+5ILnmF34Q6C6GUGCvc+oqSTXLC8/oVqRoyUvZC32Yczu+bA5sr8qZRv/W2Q5yO5XdwSFng4DioK9x5WxiGr++Vhp9RRUTK5Svrj12F+gHD35v7QZ1uVShlLCN5vNzF5kIkBFzi5V2nkHWGh6hFdHgesj/fPirg377RSLKgQWbcbBlfa6borYq6wUlxlp2jvdwymCYMPOE2c2kDfk4nRGjUCJEntL2ii6rNIjeMsWb/+uZFR3BgHP83gKSjFT4IsT0UhO1RCsxAslhZX22kmG5I25yiWkeW/gyzBELKZPSrh5Tjie36WrkFs9gXAK3HE/F4R4aWWy2/CQsWJOC3AMqtyCeKzUfXP4y4Ud0UF3i0rO0GeajlMNyS/CvKsSkKCJEEh2EJSMURZIq0+KZMNNSL3LPB4FHBogwYjtBqSL6Xa+K6f5ekoYDsKGzhGWlwzZRJH5+ovTBBC8EGnmcvMFHdEBXqUYrIwuMR2Se/HxKhyaPGuE2HUPmMadQ5eyEeD88JOalhVAigV40cvEyc5LDkd25xd9/NrylMok3feqmBtErMiDvmi28Z0KbnCacIWgjtDn9VJsQKlHcFMIXlJVY8Q2wyDXwf5QQk3FL+4B/drS28xJH9up7nB7xkQuToUG25mVxE8v4411HOfedLkb9xmvuP38EDis7NCqkZQFO5P9UvF1CNxjS4snGRIrk210qMSXotjOoRghjSZ2tREZ27w1TBTpOQ9VU5yjUCLd5245jHkg+f6zLLsS/32pklSbVtpIKZRPb+XC5aTD1f0cvmTWHIWTc7p2HLGoF5Ls48f+Nk+wt2bNUnMDX6Wz6aLVScXWSkuNF1eSCJ2K8VCadIqDD6Q21kxBywomh39Ohj8Pv9RhslRZYT4YjvFDCG5M8rzchyRAHMNk3MthzNr5FtnCEm1q0FReq56+C+/h9srVL40AacYNqfbDgsMk5uCHCvjCB/FDCGpFSZJwEtCVscR8xs8lqxZGBxpjo9sshbvOjFlG651NPnw6bK3O+b8+tvNKzgKlsfa6/ddjlzwLD3+H+k7Qi0mjF+jK38n7alLe5loaepFKcVTZVrCe/N07PMjv4cno5A3kgRLwFHS4rJe9q1/bqf5RqGnbGR9tmnzFtPhjkGKdweCd1bJw55g2ZxQ54Sd3lxoO9wbFcpO2gHIGJIzzNpXoN1JzANh5WOXpRiZzjZMPlnYzYZkX2S+lhgDSEHFdcTAPVHAU1HIX7nNvGUCTfMZDFq8B0Gz82YMkcKPPKKkEwBDdOCah9M0iDJB18xQCFdSe/6jRZN9IrY5p+ytSiUUwuepNPsyUUXTpFZ5Tt1r0xQJgO1J9VznLuDG3sKr4I3I59ko4JNuCydbDm+xXFoR/Coq8FIUEaKYZUhONW3eVRLg+dLimSobhXtqh9PAImlzueVy3DAY/i+QNu+20/wkyA0QzlnC4C+c5rocDu+OfLbVsBt+MQ5ZnUR9hHsPCcXywakIOqukaXZS9FE5yXRIT4BhEINFi/cgEELQ5JxA2l5KFHcCCaacWtZBrxq2ORvHnIcfrSr/Org45mG41kJss3I0H8Qb9p5EKhHGbwxqbZoiNpAWgl1DaDzZjuKHQY4TTRtDCJZaDksth0ApIhQpRJ+o+XIrxR8jn00VNhrPlg7vcdM0Y9C+Hx4l9XC100TGsLgrKrAxiTGBBdLkcivFjDpTFEEdh6wrSXi9isAr4AhpcbhSPFZlIMRmlXB7kONqZ3jzy2MRLd5DQAgDy5y+X8/R6p5Hj5/Gj9bsHf5riFYccx7NzpvL2on2J0lqb3oq1YhxZQceQggWSYtNNYbSVmJ1EvFw5DPTkKyKQ6YakhOkjV3mfZ0qJX/vtvDtQg9rem1Op4CTTZtPuS3YIxhZLrXsurowK7HENHGCckP/9mEZgqBGEcc2FXOIIWvODHmjxhXSREWL9yghhEGLeybN6tRSkw7Y5iEVJ+yUw5KzKNYRVP6aSGNofuCaYnXG83FY1nzfpHrSSwHf87NsVwkFiqmPeYbJZZbL+WWm4iwybb7b1MEfogJr45h8ktAlihay12R3MN2QnGbaLLNSGEIQKcVdYYHlcUCgYIZhcJmVYlaDN/CGwgJps0haPFHBDyQFHCdtVifVc/1bk6SuQQ/2gZcxAbR4jzpCWDjWvCE91pTt2HIOQVx50PCeyfQHAkolFKKXiOItgEEqtxClpgzZcvQQafL5VCs3+jmeiwOyFNMpR0uLrErw6vDm3rs24OUk4lt+DwbwtjICbgrBeVaKl4yQ6/NdfR7/epywPA5ZFUd8yE5zXaGn73zJGO4Jfd7npLnYrmR9NXJ8zGnm8/kuXu5X5poC3m2nucJO80jkVzVy2oXCpFhdUulI28CZdWyiTkS0eI9zWpyz2JXvJlZbB9zmmkeRsuqceTnOCeMtdBXuIe41VPnVzc9iyYNpdc9HGkPzx54nLb6UbmNTHLE+iZlpSA6TJrcEOTx/cCWfUKyg+N+wwFstt+JJ5QY/W3HQ7T2RzytxxCtlav93lzbw5hlmw6e2DJbZ0uSbTR3cHuRYEYcESjHTMHm75bCwtLYTpc2GqLKRFsCmJOZ4afFYhSj+ZGnvHaZ8oKHFe5wjZSvt6T8jHz5NEG0ESqO4rHm45sJRN7ofCRIV0FW4izjZ3v8Wwng93YW7aE9ful+vMUuafVISl1gpHol8nh1Cm/aqJOLFOGJhmWk5L0QBL1SxH1XAuipNWz3Ab0J/xMV7z+ivVUmEBZxrukyXkqudJq6u8Jg5UtYsuOpE8V+pdv690M0TUbDXqKoDwUmmzcereK1MdLR4TwCkkSo6Gh6YV4/kg2fLCPc+gngDQbSxYsnlULCF4ItuK//hZ3kyDulUCQZFE6ONSVy1YzIBdlSIrF+MoypON/seX43VNfypG82aOOSbhR5eTPYZMv0gyNGB4ELL5UonXbY1fJKo3SfpIkgJwT+mWtkaxzwc+cXmJstpqLPgeESLt2bcEyabrZZurQAAEDxJREFUa9wjxo/WNVS8AZoMyadSrWRL/iTNQnC4YfKx3C5WVKnbbkGQqbAR93yNxqB6WEfCP+R28QGnmcOHeQOzK4m5vtA9YDZnDGxD8cMwz8NRwBdSrQM2U0827arNOFCsL9/DNCm5TI5+Pn+sMD76QDWaatRViz18E2SahMFi02aetBBCcEqNDbQl0mJamWnqSqm6yt5qXWAlwGNxyHX53WxqoPteOW4NCzWHKq9RMd/yBxq7rUuimtZsauJn/YaMFm/NuMeUlSfZFBFYcuSMuq6wU5why+ec5wnJXzjlJ4j3oHijDhOypXU2hW1QCTeHwzsf5cU6xoMBPFdmlNhTUVizx3jrAVrDXQ86baIZ96SsN1EIXyJR5d3fLGMWjjl3WF77hSjgqThEAKeZDnOlyaak5EqHQRcJApgqDE4yHa6yUxWH7doI7BqDtGzgo6kW0kGOhyK/aiMMFEVzOAnr7EDNU7Sc7T1KrJ6M9YGd1a6OFm/NuEcaaVqcs+n279vbrboH05hOi3tuw6tutiUx/1YojgvbI6A/D3LMFSabVcy2fgJsqIRZhqw6Jd0RgqNMq+p0+YXSYqY0+cdUKwv8HN8Oqpcr1jNdZ3+YY5g8VyW/3xu333twruXwsyDHzionqwV1TLA5UNFpE82EwLHm0pF+N2n7JGxzPo65gJmTL6Aj/Y6GzwCNleIL+W4e7yXcADlgpYoGCDdAN0WL2c01ctDLTJe2CkMd0sDFvRz0TjedMjPr+zK1wQMA+nOR7VCPq8gMYXB2P/e/DkNyWpU2/JnC4PIqwyMOdLR4ayYM0nBpdk6hPXUhbakLmNR2PKKOcrTBck/k9+1urJOdKH5ZZbo7wFLL4WNuM4f3i9APNYq58rN6CeA0KVlSIbe+h5OGud57gbS5xmmiWg2IAN5iDXT+i5TiEtPldGnTfxdgvpD8g9tSdmNXU0SnTTSaQbJ8P+qot1TYkMwrxa/DPNuT4qi1L7utPJdEvJHETDEk55gOZpnUz4edJjYWYtaWqfg4Vdq8o1er/CtxxG1BnheTYsfjLENyYZfiDMV+pZUut9MskhY/CnIsj4I+E+ynYnC25fABe198HivFD4IsD0cB65MYARyCwQJpMM+wOFyavMV0GjZlfaKixVszKKJ4J360GlBY8hBsc+ZoL2nEifaj7NAukxL5TZDnp0GOzb0ad24P8lxou7yvQmXKHmZJk6+k2rg5yPFcHJFVCVNKm6NX2Km9gv90GPAVv5stvV5jU5zw1LZtzC6lJy60U1hDFMwjpMU/p9qKQyyikFVJSEoI3mY6tPS7ivhyoZu7e7k1KmA9Ca/FCfOlxbkH6HCFwaLFW1MXSoV0Fe7Gj9azz8XwCWw5hxbnrUhZXWQmEnMME2r2QZbn+H5pjD+FPt/1e+i/7bgTxU1BnlZhcEUNo6kphuQvq7SJK6X476Cnj3D35nWV8M0gy11Rgc+4rRy0H409QgiOt2yOrzDP9eko4P4KNrsx8NugwCVmiuk6XVITnfPW1MXu/F34kUdf+9mIIF5PV+EOVBV3uInG5ZbLtCFsBC6RJmf3a+D5bVQYINx7iClucj4aVM+T1+LRyK/pgAjgJTFfKXSjhrFC5b7Qr9qYsxvFb6LhrU2fKGjx1tQkiDYRxOsq3h4mmyiEL47gikaX1tLm4eQyKZAZCE6SFh29bmsGzpA217mtffK4sVJ4NapPcsAX/W4eqLHRWY11SVx3oueF0hCJ4aKnpjMLZJPhLW+cKOi0iaYmfvQytezfgngDKY4emQWNAc60HDKG5LYwz7qkODT3iNK4sEmGZEcSc1/ok1Cs+Di4TCpCURTwWmSBm4M8Z5jOkDYWmwdxlZAAK+OI04epvLqjjrV0DPOot4nCkMQ7k8kYwH8CiyheR7/f87w1jVyYZixRO1pSFe3yJy4zpclHZflc8yRDcrlTPVdtCsHBhmRHHU0uq5KIx6OAk4YwePg8y+UXQa7ijMz+DGeNxwWWy+/DyhPqp2CwTG9Y1sVQI+9lgOt53smZTOYk4GvAJY1blmYsYRq1vEPAFG0jsJKJx1mWy/N+T81Tn6I4mGAouELwdsvlB0GuppeIDZxYozbcV4rvF3pYmYQ4CM4wHS7qVdlSjfnS4mIrxa1hfsDfnAKudFK09qpOWRkF3Bn6bE5ibCFYbFpcaqVwdBnhkMX7NOAuAM/zHstkMksbtyTNWMO1FpIPnyVKtpW93aCJlPWmEV7VxOBiy+W1JOa2MF81L20Ch+5HFcjVThO2EPwuyLO2SgS+WFosqSLet/g5/ifI0jsD/2wQ8dMgx7WpVt5UR1PQR9xmZhuS+0qOhAZFUT/PcjizV9T9Yz/LTUGuT5T+WBzwYOjzBbeVqQd4RYoYys5yJpP5PnCb53m/K/28AZjreV7ZE3sUxco0D+wDPd7J5tfzeuevCaOdfX4vZRMzJr2V9pZjR2llE4PrOzv5WXd5Yy2AJY7Dj2fN2m+PllgpPr91K3dks32KHQWw2HH41vTpTDLLnyTu6O7m2s7OilcJkw2DW+fMYVqFx5fDTxKEENj9/q4/ZrN8fMsWKm3TnpVO850ZM+p+nXFMxTd8qKfyLuhjq2BUEm6AnTtzg36BqVNb6Oys/GE+0Bn54zOZVvsK8mI5YbIVlELKSaStxYSFyXQWxt57NZ4+Q+9VDq/IQtmJ65MRvFM4bNs20BN7sNwfFrinkB1Qpa6AHUHIpm09xBUi2h9md1ZN72xPEr73Ricfdve/5v+W/O6Kwg3weC7H45t3MneYh02M9mdo6tTK9ftD/csfAS4CflHKeT8/xOfRjCOkbKZZvnm0lzEhsYTg+lQbP/CzPB4HbE1iXAwWmiaX2ymOqeFhUg9KKW4L8lSSoldVws1hjr8uswm7PYlZXc/GaoMsaDfW8PHOUWz4GW7xHssM9S+/HTg3k8k8SjGsv6ZxS9JoDkxsIfiQ28wHlKIHhYOoe2OuO4m5LSywLokwKFrHXtRvY29VHLGqhgA/W0F8A+qpOaqv9LEe6hGm/qmWA40hibfneQnw4QavRaPRAIYQtA6iYO/x0Ocbfg9v9NqIvD8KuCsocG2qdW902qmSmtUmuQriO1UYzBBGn9coR6NmZi6QJi9UOdFMEwbnDqFsciKhq+E1mnFMdxIPEO49rFExXyt0k5QE+QhD1vTenlKhicYUxZLAarjAZTV8WOrlCivVZ/hwf84ynbIT6Q8kDuy/XqMZIRKluCcscF1+N3+X28WX8l08Hvr77SNyW1ioGg2/mEQ8WGp3nylNFteYTFPN//v9ThPHV3i8DXzCaS7bSToUZkiTT7stLDBkn2uQDgSXWS4fcuoZATGxOXCz/RrNCBEoxefzXTwa963xuC/yOc9y+VunecglgOtq5LAV8EIccVZJcz/kNPN6oavsxPdT+vl/98cUgn9NtXFPWOCXYZ7OJMEEMtLkE04zk0rCrZTiocjnV0GeTSrBBk42bT5kNyEH0fp+jGnzX7KDP0Y+ryQRDgbnWQ6TqoySO5DQ4q3RDDPf83sGCDcU3WJ+GxY4xJA1bV8rUY/kG73afw6WJl922/h5mGNFHFIwBJOSYlflO+x0zS5JKQTn2SnOs8uPJ4uV4vO5Xfyx30nltbDAXaHPt1JtHGrWb5wihODNlouucRqIFm+NZhjxleLxKpN3FPBQ6A9ZvI+SFg9UeX4LOLFfrnq6lHysVA7Y6DrmH/nZAcK9hy4Un8jv5ubmyUMe+qDZh855azTDyIY4YmONCo2NSUwwxNz3JVaKw6vM6VwiLY4b5jmWeyjm9avbye5AcUeg/bobgRZvjWYYsYWgVobWFGLIX0RHCK5NtXKUYfZJoZjA8dLi01Um7DSa3UqxtY5q8Kcb1MhzoKPTJhrNMHKwIZlvmLxUZWNxgTTrcuSrxOHS5Dvpdu6PfF6Ki006J0ibpdbIRNx7sER9OXg9aqExaPHWaIYRIQTnWw6v+FHZqZctwEXm/vtXG0JwtuVy9jANUaiHZmEwRxisq5Emmn8At7Q3Ep020WiGmYvtNP/XTjO9X1PJQcLgo04zJ0ygTsH32E1Vo+92BJdXqFTRDA59CtRoRoCrnSYutVPcERbYnSRMNyTnWe6E8+c4w3Z5VxJxc5gf0IrfiuDTbgtNB3hnZKPQ4q3RjBBpYXBlg9rHxzIfcJu5wk7zb4VuNiQRthCcKG2uslO06QabhqHFW6MZ48RKEVD0DtnfYQwjRbth8MW0Ho03nGjx1mjGKK/GETcFxU7IrFJMNgyOkxbvtZtI6wnrBzxavDWaMcgrccjn8l19Gny6kph1SczqJOZfU22kx0kUrhke9OlboxmD3BjkKnZmrohDfuRnR3hFmrGGFm+NZoyxJY54topfCcBy3aV4wKPFW6MZY2xQMbXi6h0q2W8vcM34Rou3RjPGmCkktdp2moUYN5UnmuFBi7dGM8aYI02OqTHxZkmN2zUTHy3eGs0Y5F12mskVGs3nGZKrD4BmH011tHhrNGOQN5k2n021cpK02SPTkxCcIx2+6LYxRXcqHvDoOm+NZoyy2LRZbNpsTxJ2JjHTpaRF+4JoSmjx1mjGOJMNg8m6o1LTD/2J0Gg0mnGIFm+NRqMZh2jx1mg0mnGIFm+NRqMZhwjdYqvRaDTjDx15azQazThEi7dGo9GMQ7R4azQazThEi7dGo9GMQ7R4azQazThEi7dGo9GMQ7R4azQazThkTBtTZTKZBcDjwHTP8wqjvZ6xRCaTaQN+ArQCNvAJz/P+NLqrGhtkMhkD+E9gEeAD7/c8b83ormrskMlkLOBG4FDAAa73PO/Xo7qoMUomk5kGPA2c63neqtFeT2/GbOSdyWRaga9R/PJpBvIJ4F7P884A3gP8x+guZ0yxDHA9zzsZ+AeKnyPNPt4NbPc873TgfOA7o7yeMUnpJPc9ID/aaynHmBTvTCYjgBuAa4HcKC9nrPJ1ih8sKF5B6SuTfZwG3AXged5jwNLRXc6Y4xbgs71+jkZrIWOcrwLfBTaN9kLKMeppk0wm8z7gb/r9+lXg557nPZfJZEZhVWOLCsfoGs/znsxkMjMopk8+PvIrG7O0Art7/RxnMhnT8zwtUoDneT0AmUymBbgV+Mzormjskclk3gN0ep73+0wm8+nRXk85xqS3SSaTWQNsLP14EvCE53lvHsUljUkymcwxwM+BT3qe97vRXs9YIZPJ/DvwmOd5vyj9vNHzvDmjvKwxRSaTOQi4HfhPz/NuHO31jDUymcxDgCr9WwysBi72PG/zqC6sF6MeeZfD87x5e/4/k8msB946aosZo2QymaMoXv5e5Xnec6O9njHGI8BFwC8ymcxJwPOjvJ4xRSaTmQ7cDXzU87x7R3s9Y5HewWImk3kA+PBYEm4Yo+KtqYt/AVzgm6XU0m7P8y4Z3SWNGW4Hzs1kMo8CArhmlNcz1rgW6AA+m8lk9uS+z/c8b0xuzGnKMybTJhqNRqOpzpisNtFoNBpNdbR4azQazThEi7dGo9GMQ7R4azQazThEi7dGo9GMQ7R4azQazThEi7dGo9GMQ/4/6Ued1rV8jbIAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.datasets import make_blobs\n",
+ "\n",
+ "X, y = make_blobs(n_samples=200, centers=4,\n",
+ " random_state=0, cluster_std=1.0)\n",
+ "plt.scatter(X[:, 0], X[:, 1], c=y, s=80, cmap='rainbow');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
+ "This figure presents a visualization of the first four levels of a decision tree classifier for this data:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
+ "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "tree = DecisionTreeClassifier().fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's write a quick utility function to help us visualize the output of the classifier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
+ " ax = ax or plt.gca()\n",
+ " \n",
+ " # Plot the training points\n",
+ " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
+ " clim=(y.min(), y.max()), zorder=3)\n",
+ " ax.axis('tight')\n",
+ " ax.axis('off')\n",
+ " xlim = ax.get_xlim()\n",
+ " ylim = ax.get_ylim()\n",
+ " \n",
+ " # fit the estimator\n",
+ " model.fit(X, y)\n",
+ " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
+ " np.linspace(*ylim, num=200))\n",
+ " Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
+ "\n",
+ " # Create a color plot with the results\n",
+ " n_classes = len(np.unique(y))\n",
+ " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
+ " levels=np.arange(n_classes + 1) - 0.5,\n",
+ " cmap=cmap, clim=(y.min(), y.max()),\n",
+ " zorder=1)\n",
+ "\n",
+ " ax.set(xlim=xlim, ylim=ylim)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can examine what the decision tree classification looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/anaconda3/lib/python3.6/site-packages/matplotlib/contour.py:967: UserWarning: The following kwargs were not used by contour: 'clim'\n",
+ " s)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "visualize_classifier(DecisionTreeClassifier(), X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'helpers_05_08'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# helpers_05_08 is found in the online appendix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mhelpers_05_08\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mhelpers_05_08\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_tree_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'helpers_05_08'"
+ ]
+ }
+ ],
+ "source": [
+ "# helpers_05_08 is found in the online appendix\n",
+ "import helpers_05_08\n",
+ "helpers_05_08.plot_tree_interactive(X, y);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
+ "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
+ "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Decision trees and over-fitting\n",
+ "\n",
+ "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
+ "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
+ "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "SyntaxError",
+ "evalue": "invalid syntax (, line 2)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m import ../notebooks/helpers_05_08\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ ]
+ }
+ ],
+ "source": [
+ "# helpers_05_08 is found in the online appendix\n",
+ "import ../notebooks/helpers_05_08\n",
+ "helpers_05_08.randomized_tree_interactive(X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ensembles of Estimators: Random Forests\n",
+ "\n",
+ "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
+ "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
+ "An ensemble of randomized decision trees is known as a *random forest*.\n",
+ "\n",
+ "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'visualize_classifier' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mbag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mvisualize_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'visualize_classifier' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import BaggingClassifier\n",
+ "\n",
+ "tree = DecisionTreeClassifier()\n",
+ "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
+ " random_state=1)\n",
+ "\n",
+ "bag.fit(X, y)\n",
+ "visualize_classifier(bag, X, y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
+ "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n",
+ "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
+ "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
+ "\n",
+ "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
+ "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'visualize_classifier' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mvisualize_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'visualize_classifier' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "\n",
+ "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
+ "visualize_classifier(model, X, y);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Random Forest Regression\n",
+ "\n",
+ "In the previous section we considered random forests within the context of classification.\n",
+ "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
+ "\n",
+ "Consider the following data, drawn from the combination of a fast and slow oscillation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7\nwTRSlanVbclEryzjPGu9tck6BWseizipMer8BlWJbvB139pQFvWuX6IbpoUL5s3ZBlTFvZJD3e0p\nyCzX6t1m7FvBXbWiD9P52ZYu2oaBObs8ybxbldtaPT8KiciCx1LGOU9/2W/w+3oyyHSktCy6Ub1y\nRJYb2nq1FUQ3TKdnS66/pwJPAdk0Tdxzzz3YuHEjbr75Zrzzzjt+t6sl1cF53cqlePbQUc8XbavX\nUjqTZV0qmdIvnXEb0tGp7m8jxzI0uEqJQvOtnBe3OU8ZLrAqsm8nqWMwnjk9O2fliCw3tPXWGjsl\nsl61om9O79jp91TgKSA/88wzOH36NPbv349t27Zh586dfrfLN61c6Oy9FgBKDAW6rdXTaWE9j6XM\nbatJGS6wFCxr/XWzTs04j6BEfXPu9n22lpvZt/IVJXoB8ub5OPEUkA8dOoTVq1cDAD7+8Y/jl7/8\npa+N8lMrFzpVhwLd1hXrtLCex1JWL6kx6gsstS6Itc+iKl1R39B6mcN3W/oqY56PiKcshVwuh2w2\ne/ZF2tpQKpWQSLjH92SyfBvX05N1fZ5X1a9v/fd5v5fFW8cna577kd5s3Xa8e0I8FBjUMXiRTBrl\nSW6Uj/26P8yis7Md3/7B/8Vs0cSyczqx4aqP4opL+9DZ2Y6hfYdqXuOGqy/Eoz8aAQA8ctdnQm2/\nVzdc/THhsVjn56XXjmLiVB7Foolk0sCC9raWz10Q576RYxGxzrfT7wPlC6xM39dGNdtmv68vTtcT\n+3+HRfSeL712tFIU5L5Hf175O29FI9fGINmvX0D58ja/vRyuKufAMJBMGujpyQqv80u6OnDdH14Q\navtb4SkgZzIZnDp19i6qkWAMnL0jGxub8vK2Tb2+9d9Xf/IjjoXnr/7kR+q249zF4vXNT714RJo5\n5GLRhLXOxTqmi/oWYuGCctGSu2/5ROVnF/UtxJbrL8bep0Yqdb9vva4fF/UtDPz8+M1+LFYN44v6\nFmJsbKp2Z5iiiclThZbOXU9PNpDPp96xNPL7yYThuFb2nMULlDmnFi+f8/1bVgLw7/tr/T089eIR\nnHh/BsWSidvufwYfzMwinUqG+plW/21aPeX1a86f8/1+6/gkhvYdwuTkTN3vt9s0RiPXxqBVX7+s\ninM113fTRLFoYmxsSnid//wV/yPSY2n2xsbTkPVll12GF198EQDw+uuvY/ny5V5eJhROVajs2dGi\n4SC3oUCNLKjUAAAauUlEQVSVhwGthBVryzZZbiy8sCffVB+LaglsbscC1B+2FA3pqTRkJ5t8oeha\nRjNKXr/f9htVy6JOdbdbbeQ6rwJPAXnt2rWYN28eNm7ciPvvvx9f+cpX/G6Xr7wGoIH+XmskuEbU\n8yxUn05JX41Ip5JzluapelGSiShfJDddiHyZkNfvt3C71bSc5XIbrdWtQ0fD05C1YRi49957/W5L\nJLbvfhXjUzPCpTEfXrLAcdhalsSh6mVZ89tTLW1rqUvpQ4uopKos5y4I1vIcazmLdfFV8eIkA7dy\nmdYyISCaz7crMw8np/I1jy/MzHP9PR1uVIcGV0m1wYdfIi2dqQKZd0KyL8t66/hk7Ja5WDdUTmQ+\nd0HJF4rKVyuSSSPlMqWbAqnTm3RbVkTRYkCuw74IHYA0OyG1Mkc6PjWj/ZZtThWXdB/CFQ2xShc0\nfBDGNoiNlMuMqmc5kTvt+Pj7p5wft9RbViTb9pJxolVx1qCGXK3dlKr/LQMdhp6CYF1MhgZXzdnF\nyutOWCoRDbHG/TvhVTqVxI1rl1ey351ENQXidUrGuiG1htuTCQOlkinNdS3OQu0hDw2uCm2eMqj3\nCntLOzc6FcYgf4iGWPmd8M5KFrKPlFmimgJpZUpmoL8XiYRRSYASJa9SuLTqIbdifGoG23e/Kk2w\nbcS6lcscly+4/UFyKEpvHek2x5q+Ks+bV494hMn+fvbesrVWPKopEHtPN+r2BKF6edlELj9nCqGR\n70NU3x2vOIfcIPNM6r1Mu6LY50iXndOp/RwpubOWPlniMG8epnprxaNoj1XLWob2+MmeoFgsmchN\nF6S49gaFPeQG5AvFOYmLUS93qFY9R/r3t1/ZcFUaawN76wZj3cplkR9Ls9wyrOPMynnozrbHYt6c\n9GL1akUJit/70a8ARH/tDUJsAnK+UJzTw20mALllrsr8pRAN08h8g0FEc1m1BoolE3c/Mox8odhy\nAtbwyChKZ5LUJnL5yg26TEqCJLpCseR6vZIpz6dZsRiyHh4ZbWltpm6Zq3FaGhMnYSZNUjjstQas\n0p2tTJ3V1HgvmVJuK9uWdM800/F6FYsecqs1jd2K9qtItxsMle+I7UTHYe8lqTjFQM0TXbuAsx0L\nA2gqS1qVbWVn6xRKUPV65SYWPWS39brWUHbJLA/dON1x3npdv+Pvq5q5yqUxanHqJelSfYtFKNy5\n7fNraXa0WfSabmVCo/DhJe7XI6frleqjRLEIyKL1ugsXzKvJ4nO60NmrdameucpdgdTS7AjP0OAq\nYW12GVlLDqlWEGUuRa/ZSJnQMLnttlf+uX7Xq1gEZNGJPT1bcnzc6UKXTiWRMCDNcodW6LI0xp6o\np0OP0QkrssVXvaDk52s2UiZUBipvE1lPLAKyU03jq1b0ORZQAJwvdKr1OtzkC8XKfFEyYShZTKCZ\nRD3Vh7FYkS2+7NcuJ5mOVFPXJus1LYs6y5W6ctMF3L7rFYxPzUhxk6vaNpF+iEVABmoX9L/x9rjw\nuTpf6KzF9tZ8kWiYXnaiP9Ynnj+CiVxeiguKX+K4a5WdfVjbSnIL6zxHeVNXfe1yGtnysgRqoL8X\nH+ruQKYjhZOTZ5c9nZzKVzadiTpXIY4jQ7EJyHZuyRI6X+h0WfIkOn8np/LaJT/FadeqRpK8dE5y\nq8fvqbNGMqujujbEcWQotgFZdLIXZdNaXuiA8oVMlyVPzSS7qHaz4US2ko1hsXrC1asgWl3G2CrZ\npkBaaU8jmdVRXRviODIU24Dsliyh4522vRiAnWp3nc0ku6h2s+EH2YKGF/aesDW9csxhy0Egnue5\nVY1kVkd1bbCvbkkmjKb2ordPa3zxuy9Ln82vRlpdAKyT+tC/Hp5TNu7kVF65MpKNXHjdCgwA6t11\nDvT34vGnf1NJ7OrryeCDmQJOTuVrnqvazQaVib6zbckECsXaFRI8z80T7Q5WLcxrg70ATrWuTLqp\n16nugDjtGy2j2PaQAWunFOc7RB2GOau5zZmrOB85PDJakym+4coLHJ+r2s0GlYkLWDgvV+R5bp59\nCeSibBpWpzmZMEK9NojKhHqhSjUyu1gHZEC/MpIibsUAVAzGTkOZAGKT/BQHou/suUsyPM8+qk4U\n++Zffgrd2XYkjHKPNMzPtN4oXjNUqUZmF/uAHJcykqoXA6jmltQT1+QnHbkl9fA866demdBmNr9Q\npRqZXewDclzKSNqXziQTBgyg5W3cohDH9Ylx5PSdZU9YX/VWTuSmCw0HZVU7ILEPyOlUEtX3TG7D\nX6pnrlb3Kroy6aZ2iJFJHNcn6mh4ZBTjUzOV8qe373ql5oJrfWejGEKlcDWycqLROWCntfuZjpT0\nHZDYB2SgvHWZLnWq4yCO6xN1Y+UBlGwrHJrpBVFwSma5OlqY7CU9/Xi96mkN2YMxwIAca6ruI8yh\nTPW5JfDIngkbhaHBVVi/5vyaIim6Ka98Ef9c91EwuQfUfaZi8CFnA/29OPDC7zA+NcOhTAW5JfDI\nnglbj1V8ws/rjX1dbfXKAq/f/Ufu+gzGxqakLJZhwHmfZ91HwdhDJqLQuSXw2DNh47LNppuoy4WG\nzTAQy1EwBmSUh2512VqR9KV6UmE1twSeYsnE//nm8xgeGW1qm02dhbmywJrDL5mI9AYojgl9DMhE\nFDorD0A0X/jW8UnsOXgYTzx/xPHnuvYMRcJaWSDjDZAfuS5hb9fpVewDsr3X0cj2b0TUuoH+3kpV\nKFHBBqfa5ED81pwHtbLAvpuWjjdA+UJRme06Y5XURWfpMvSpaqY4zdVsItc5ixfEKhvbGq7d+9QI\niiUTfT2ZSsUyr1567WhNopiON0Bue8DLNgzOgEzaYYBWTzJhOAblRZ1pnJysDRKtBiMVWSsLAOC+\nzZe3/HpPPPvbhp+r8nIjlfYriP2QNRFFT1TScMOaC+bsRqTSRhKyT3+9PTrV8HNVXm6k0n4FDMhE\nFLl0KjmnStOyczorgbd6NyJW0vPPeb1Zx8cXZc/uO6zSDZCISvsVMCCfodOSEjdxOU5Sj1WlKWEA\nf3/7lRjo7630MFVZlrh996uhl5z0asNVH3V+/MoLKuch7Bug4ZFRlMyzS65aLaM6NLgKD25d7VjX\n2hr+lwkDMhFRDF1xaZ/jvtIA5gTFsLKR7dXIjo6d8q22uaiutWzTCkzqImWxp08yaaZ3HER5TS/s\niWJOQbHVEp1uqj8HUTWyOGXTs4dMRFIpmcDmr/0k6mZ4Yp7pWZ6YnMFELq/czlVRlugUVSNTvbZ5\nMxiQiYh8kC8U52yIUCyZyE0XpCxAIRJmiU47UTUyUZa0jhiQiYh84FaAQhVhleh0IqpGdut1/YG/\ntywYkIkoMkODq5TJoK5HpQIUIkGV6GyEfZ9zHZZcNYtJXVWsuq7Fkom7HxnGupXLYvVlIIpC1IlN\nfhFVG5OxAIWIdb2zErn8KNHZ7Pv7WY1MNS31kJ9++mls27bNr7ZEysouVKEAORHJR6UCFG6q14Oz\nEEu4PPeQv/71r+OVV17BRRdd5Gd7IuOWXcgvJBHVk04lcWq6UEnsSiYMdKTbfL1+6DKaQM48B+TL\nLrsMa9euxT/90z/52Z7IRJldSETuVAlEhgEYUKeyWLNkWT/t1fjUjFSFQOzqBuQDBw7g0UcfnfPY\nzp07ce211+JnP/tZU2/W0+NcO1UG5/1eFm8dn6x5/CO9WanbbadSW1XFzzg4S7o68N775eIaPT1Z\nJJNG5b9ll0wa5YgMoFAs4tTMLIpFE/c9+nNsuOqjuOLSvjnPfe/9GXx5z0/xyF2fiarJ4s/YcP7c\n/T4fTq8X1Dm3zo/1+tVk+X7VDcjr16/H+vXrfXmzsbHGdxcJ29Wf/MicCjXVj8vc7mo9PVll2qoq\nfsbBGR4ZxYn3Z1AqmTgxOYOnXjyCYrE8AKzCZ/7BzCxKZ3JQJk8VKo+/dXwSQ/sOYXJypjJ8XSya\ngGmiWDQjOzbru+z0GXdn0jWPAfD9fDi9XlDn3PrMP5iZxXR+FsWSWZlWCOocNBvouezpDKbcE0XH\nnlRZLJrYc/AwxqdmcGJyJtSayl4Mj4wiN11wfY6s65HjsuHM0OAqzG9PITddOPs9O1O85YvffTni\n1pUxIFexFyBnMCYKhyip0lpFJPuqhyeeP1L3OVY+irW8smQCE7m8tMfkN/tGDtbnEOYNl6h4iywV\n1Vpah3z55Zfj8svjt1aMiPwlSqq0k3HVw/DIKE5O5es+75zFC2o2byiWzEA3b5CV2yYWQXKriy3D\neWAPmYgiJyrZaCfjqgdR795u3cqlkW7eIJOoPod6dbGjPg8MyEQUOVHJRjsZq17V691X56NweWVZ\nVJ+DqHhLWO9fD0tnElHkBvp78dC/HoZZZ6c9GatenbtkPo6O1V7IE0Z5PXJ1CUjRc2W80QiS2+cQ\nZMnMdCoJAMIEPC/nwc+12ewhE5EUPrxEfDGUedWDqHc/vz3V8HNlu9EIOvM6ys8hnUoi01F7bsJ6\nfzcMyEQkBdFFOtORknrVg33JJFBus9Ubc3tuMmFIe6MRpKiXmVpBWbbzwIBMRFIY6O+d03Pp68kI\nA5tsrCWT1qYMbm2ufm5XJh15EIhK1MtM06mkdOeBAZmIpJFOJZEwgA91d+C+zZcrEYzturPtWhba\n+OJ3X8aJSTUKtTipXvc8kcsjXyhG3aQaTOoiIiJX9kpk1euGZehZ1uO0/rteZbUosIdMRBSyfKGI\nkgllepuqr58WtV9UuSsq7CHb6DjURETyULG3qfr6aVH7rcpd1o3RupXLWKmLiCguVOxtiiqpqbJ+\nupFKcDLUS2dAJiIKkYq9zVbXDUexkUS1RivBAdHeGDEgExGFSMXeptOStEbX7dq31oyiJ+q0/lsk\nyhsjziETEQVAlI+ybuUyx52Noq4SVU86lcQHM4WacqD1yDJEP9DfiwMv/K7y74lc3nH3J6cbIz/L\nY7phD5mIpDE0uArd2faom+FZI2uQq4MCIHdZUD/IOEQ/NLgKt17X7/izZm6M/B6KZw+ZiKSl+6oH\nq0qVztw2kohy2dFAfy8ef/o3lYz3vp4M1q1c2vCNkduezl5vrhiQiYhCVmdbXq24DdFHPSrw4NbV\nleHoZm+M3IbiGZCJiCKke2/eq1Z7orIKYiieAZmIpDI0uAo9PVmMjU1F3RQ6w5orLZnlZKjhkdGm\nAqrXhDCZBbG3NZO6iIhCVjLL/1OBfdlSsWRiz8HD+OJ3X/b8mtt3v1oZKpaZWzuD2NOZPWQiIhJS\npQ502KwRgr1PjaBYMn0ZimdAJiIioXp1oOOsem2zH0PxHLImIiIhUWUxt2pX5A0DMhERCYnmSjvS\njQ2wbt/9KsanZnxskb44ZE1EFKJ8oVj5by8Zy2Gzz5UmEwY60m1Ip5IRt6x5si9NYw+ZiCgk9r2Q\nrYzlKLf8a8RAfy+6Mmks7mzHw3dc2VQwzheKlaxy6wZEJflCMbSdqhiQiYhCIstGC2ER3YBUjxLI\nLF8oIjddCG2nKgZkIqKQHHMoJAHIvRdyK1RfMiVqZ1A3UJxDJiIKwfDIKEQLhWTeC7kVKiyZcptX\nFrUzqBso9pCJiEIg6i0C8u+F7JXqS6ZE7QzqBooBmYgoBKLeYsLwvl2f7FpdMhU1UTuDuoFiQCYi\nCoGot9iVTYfcktZYG000knU80N+LTEeq8u++ngy2XH+xMkum0qkkMh2pSk/Zan9QN1AMyEREIRD1\nFk9OqrMUKF8oztloopGs43QqiYQBLO48u9NTWMuIvNq++1V88bsvYyKXr2SJZzpSuG/z5YGOZjAg\nExGFYKC/F4sEvWFVlj21mnVs3zkq6GVEXtmXOxVLJnLTBcd2Dg2u8q3gCAMyEVFIJnKnHR9XZdlT\nq1nHqqzDDnu5k4UBmYgoJKJ5ZFWWPbWadSxKbJPthiTs5U4WBmQiopAEsal9WIYGV+HW6/odfyZq\nv5UAZpXN7MrMc3yebDckYS93sjAgExGFRJR1rMqyp4H+Xmy5/uKGso7t88XFkomTU3nH15XthiTs\n5U4WNRaDERFpIp1KIjddQMLwZ1P7sA309+LAC78D4N5+0Xzxos403s+dRrFkoq8ng3Url0p3Q2It\ny5rOz1Z2uCqVTBx44XeBtpUBmYiIfCeaL34/dxpdmXK2ucw3JOlUcs566TD2dOaQNRER+U71BLYo\nMCATEZHvVE5giwoDMhER+c6eAJZMGEolsFUbn5pBGBtUeQrIuVwOX/jCF7Bp0yZs3LgRr7/+ut/t\nIiLSVsIAurPtUTcjcAP9vejKpJEwgK5MWslgHCZPSV3/+I//iFWrVuHmm2/Gm2++iW3btuHJJ5/0\nu21ERKSB7my7b+Ulg2atnbayqzvSbejOtoeS1OUpIP/5n/855s0rL/CenZ1FOq3WbiVERFEZGlyF\n7btfjboZ5MBaO22xaliHpW5APnDgAB599NE5j+3cuROXXHIJxsbGcMcdd+DOO+8MrIFERERhEK2d\nFtW29lvdgLx+/XqsX7++5vE33ngDt99+O3bs2IFPfOITDb1ZT0+2+RZSU/gZB4+fcTh0/pyTyXKi\nU9TH6PX9m2m/03NlOX67d084r50ulkwkEgaSSSPQNnsasj5y5Aj++q//Gt/5zndw4YUXNvx7Y2NT\nXt6OGtTTk+VnHDB+xuHQ/XMuFsspu1EeYyufcTPtd3quDMfv5NzF83F0rHYDiWTCgGmaKBbNmjZb\n0w9Oc+TNBm9PAflb3/oWTp8+ja9//eswTROdnZ3YtWuXl5ciIoodVRKcRFRvv8i6lcvmzCFbOtJt\n+GAm+LlkTwF59+7dfreDiIhiQtaAbi3L2vvUyJxa248//RuUTODE5AzufmQY61YuC2QJFwuDEBER\nnWGtnV7c2V6ptV2daX107BT2HDyM4ZFR39+bAZmIiEhAlHn9o5/+t+/vxYBMREQkINq16viJ2uSv\nVnH7RSIiCoys88WNOneJc+Z1ELtWsYdMREQkcOF53Y6PB7FrFQMyERGRg+GRUTx76GjN41et6GOW\nNRERUVhECV1vvD0RyPsxIBMRETkIM6ELYEAmIiJydO6S+Y6PB5HQBTAgExEROVq3cpngcf8TugAG\nZCIiIkcD/b3Ycv3FSCbKu1MlEwa2XH9xJaFreGQUE7l8paRmq9W7GJCJiIgErFKaCQPoyqTnBOM9\nBw+jWCrvXOVHSU0WBiEiIqrSSDETt5KaXpdEsYdMRETUpCAysBmQiYiImhREBjYDMhERUZOCyMDm\nHDIREVGTrHnivU+NoFgy0deTwbqVS1sqqcmATERE5MFAfy8OvPA7AMB9my9v+fU4ZE1ERFRHd7Y9\n8K0kGZCJiIgkwIBMREQkAQZkIiIiCTCpi4iIyEXQc8cW9pCJiIgkwIBMREQkAQZkIiIiCTAgExER\nSYABmYiISALMsiYiIvLIzwxs9pCJiIgkwIBMREQkAQZkIiIiCTAgExERSYABmYiISAIMyERERBJg\nQCYiIpIAAzIREZEEGJCJiIgkwIBMREQkAQZkIiIiCTAgExERSYABmYiISAIMyERERBLwtP3i9PQ0\ntm3bhsnJScybNw/3338/PvShD/ndNiIiotjw1EP+53/+Z1xyySXYt28f/viP/xgPP/yw3+0iIiKK\nFU895FtuuQWmaQIA3n33XSxcuNDXRhEREcVN3YB84MABPProo3Me27lzJy655BLccsst+O1vf4vv\nfe97gTWQiIgoDgzT6up69F//9V/YsmULnn76ab/aREREFDue5pAfeugh/PCHPwQAzJ8/H8lk0tdG\nERERxY2nHvKJEyewY8cO5PN5mKaJbdu24dJLLw2ifURERLHQ8pA1ERERtY6FQYiIiCTAgExERCQB\nBmQiIiIJMCATERFJINCAbJom7rnnHmzcuBE333wz3nnnnSDfLpZmZ2dxxx134MYbb8Sf/umf4rnn\nnou6SVo7ceIE1qxZgzfffDPqpmjpoYcewsaNG/H5z38e//Iv/xJ1c7QzOzuLbdu2YePGjbjpppv4\nPQ7AL37xC2zatAkA8Pbbb+PP/uzPcNNNN+Hee++t+7uBBuRnnnkGp0+fxv79+7Ft2zbs3LkzyLeL\npYMHD6K7uxuPP/44Hn74Yfzd3/1d1E3S1uzsLO655x60t7dH3RQt/exnP8Nrr72G/fv347HHHsPx\n48ejbpJ2XnzxRZRKJezfvx+Dg4P49re/HXWTtLJ3717cddddKBQKAMpVLf/mb/4G+/btQ6lUwjPP\nPOP6+4EG5EOHDmH16tUAgI9//OP45S9/GeTbxdK1116LrVu3AgBKpRLa2jyVJ6cGPPDAA7jhhhu4\ns1lA/uM//gPLly/H4OAgbrvtNlx55ZVRN0k7y5YtQ7FYhGmamJqaQiqVirpJWlm6dCl27dpV+ffh\nw4fxiU98AgBwxRVX4Kc//anr7wd69c7lcshms2ffrK0NpVIJiQSnrv3S0dEBoPxZb926FV/60pci\nbpGennzySSxevBif+tSn8A//8A9RN0dL4+PjePfdd7Fnzx688847uO222/Dv//7vUTdLKwsWLMDR\no0dxzTXXYGJiAnv27Im6SVpZu3Ytjh07Vvl3dZmPBQsWYGpqyvX3A42MmUwGp06dqvybwTgYx48f\nxy233ILPfe5z+OxnPxt1c7T05JNP4pVXXsGmTZvw61//Gjt27MCJEyeibpZWurq6sHr1arS1teH3\nf//3kU6ncfLkyaibpZXvf//7WL16NX784x/j4MGD2LFjB06fPh11s7RVHe9OnTqFzs5O9+cH2ZjL\nLrsML774IgDg9ddfx/Lly4N8u1h67733sHnzZmzfvh2f+9znom6Otvbt24fHHnsMjz32GD72sY/h\ngQcewOLFi6NullZWrFiBl19+GQAwOjqKmZkZdHd3R9wqvSxcuBCZTAYAkM1mMTs7i1KpFHGr9NXf\n34///M//BAC89NJLWLFihevzAx2yXrt2LV555RVs3LgRAJjUFYA9e/ZgcnISu3fvxq5du2AYBvbu\n3Yt58+ZF3TRtGYYRdRO0tGbNGvz85z/H+vXrKys0+Fn765ZbbsFXv/pV3HjjjZWMayYpBmfHjh34\n27/9WxQKBZx//vm45pprXJ/PWtZEREQS4IQuERGRBBiQiYiIJMCATEREJAEGZCIiIgkwIBMREUmA\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "rng = np.random.RandomState(42)\n",
+ "x = 10 * rng.rand(200)\n",
+ "\n",
+ "def model(x, sigma=0.3):\n",
+ " fast_oscillation = np.sin(5 * x)\n",
+ " slow_oscillation = np.sin(0.5 * x)\n",
+ " noise = sigma * rng.randn(len(x))\n",
+ "\n",
+ " return slow_oscillation + fast_oscillation + noise\n",
+ "\n",
+ "y = model(x)\n",
+ "plt.errorbar(x, y, 0.3, fmt='o');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Using the random forest regressor, we can find the best fit curve as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "forest = RandomForestRegressor(200)\n",
+ "forest.fit(x[:, None], y)\n",
+ "\n",
+ "xfit = np.linspace(0, 10, 1000)\n",
+ "yfit = forest.predict(xfit[:, None])\n",
+ "ytrue = model(xfit, sigma=0)\n",
+ "\n",
+ "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
+ "plt.plot(xfit, yfit, '-r');\n",
+ "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
+ "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Example: Random Forest for Classifying Digits\n",
+ "\n",
+ "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
+ "Let's use that again here to see how the random forest classifier can be used in this context."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.datasets import load_digits\n",
+ "digits = load_digits()\n",
+ "digits.keys()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To remind us what we're looking at, we'll visualize the first few data points:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# set up the figure\n",
+ "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n",
+ "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
+ "\n",
+ "# plot the digits: each image is 8x8 pixels\n",
+ "for i in range(64):\n",
+ " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
+ " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
+ " \n",
+ " # label the image with the target value\n",
+ " ax.text(0, 7, str(digits.target[i]))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can quickly classify the digits using a random forest as follows:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'RandomForestClassifier' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n\u001b[1;32m 4\u001b[0m random_state=0)\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mypred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.cross_validation import train_test_split\n",
+ "\n",
+ "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
+ " random_state=0)\n",
+ "model = RandomForestClassifier(n_estimators=1000)\n",
+ "model.fit(Xtrain, ytrain)\n",
+ "ypred = model.predict(Xtest)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can take a look at the classification report for this classifier:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'ypred' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mypred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m: name 'ypred' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn import metrics\n",
+ "print(metrics.classification_report(ypred, ytest))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And for good measure, plot the confusion matrix:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2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\nCfL/ADUiXKng20C6AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "mat = confusion_matrix(ytest, ypred)\n",
+ "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
+ "plt.xlabel('true label')\n",
+ "plt.ylabel('predicted label');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summary of Random Forests\n",
+ "\n",
+ "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
+ "Random forests are a powerful method with several advantages:\n",
+ "\n",
+ "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
+ "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
+ "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
+ "\n",
+ "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >"
+ ]
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb
index 177fa9342..3ab2a4cf8 100644
--- a/notebooks/02.00-Introduction-to-NumPy.ipynb
+++ b/notebooks/02.00-Introduction-to-NumPy.ipynb
@@ -2,13 +2,10 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
- "\n",
+ "\n",
"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
"\n",
"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
@@ -16,35 +13,28 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
- "< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"
+ "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
- "# Introduction to NumPy"
+ "# Introduction to NumPy & Pandas"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
- "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
- "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n",
+ "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else. Let us look at following data formats:\n",
+ "\n",
+ "* Illustration of main Pandas methods\n",
+ "* Plotting DataFrame\n",
"\n",
"For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n",
"Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n",
@@ -67,11 +57,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -91,10 +77,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"For the pieces of the package discussed here, I'd recommend NumPy version 1.8 or later.\n",
"By convention, you'll find that most people in the SciPy/PyData world will import NumPy using ``np`` as an alias:"
@@ -103,11 +86,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np"
@@ -115,20 +94,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Throughout this chapter, and indeed the rest of the book, you'll find that this is the way we will import and use NumPy."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Reminder about Built In Documentation\n",
"\n",
@@ -151,10 +124,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"\n",
"< [More IPython Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >"
@@ -178,9 +148,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "version": "3.6.4"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
index c8bde4cb7..39f6becb8 100644
--- a/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
+++ b/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
@@ -63,17 +63,15 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
- "import numpy as np\n",
+ "import numpy as np2\n",
"np.random.seed(0) # seed for reproducibility\n",
"\n",
- "x1 = np.random.randint(10, size=6) # One-dimensional array\n",
- "x2 = np.random.randint(10, size=(3, 4)) # Two-dimensional array\n",
- "x3 = np.random.randint(10, size=(3, 4, 5)) # Three-dimensional array"
+ "x1 = np2.random.randint(10, size=6) # One-dimensional array\n",
+ "x2 = np2.random.randint(10, size=(3, 4)) # Two-dimensional array\n",
+ "x3 = np2.random.randint(10, size=(3, 4, 5)) # Three-dimensional array"
]
},
{
@@ -86,9 +84,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -116,9 +112,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -142,9 +136,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -185,9 +177,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -207,9 +197,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -229,9 +217,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -258,9 +244,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -280,9 +264,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -309,9 +291,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -333,9 +313,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -355,9 +333,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -377,9 +353,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -406,9 +380,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -439,9 +411,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -489,9 +459,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -512,9 +480,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -534,9 +500,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -556,9 +520,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -578,9 +540,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -600,9 +560,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -631,9 +589,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -653,9 +609,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -685,9 +639,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -709,9 +661,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -732,9 +682,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -763,9 +711,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -797,9 +743,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -816,9 +760,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -842,9 +784,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -872,9 +812,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -900,9 +838,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -928,9 +864,7 @@
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -949,9 +883,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -986,9 +918,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1014,9 +944,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1035,9 +963,7 @@
{
"cell_type": "code",
"execution_count": 37,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1067,9 +993,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1100,9 +1024,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1125,9 +1047,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1148,9 +1068,7 @@
{
"cell_type": "code",
"execution_count": 41,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1173,9 +1091,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1224,9 +1140,7 @@
{
"cell_type": "code",
"execution_count": 43,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1255,9 +1169,7 @@
{
"cell_type": "code",
"execution_count": 44,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1282,9 +1194,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"grid = np.array([[1, 2, 3],\n",
@@ -1294,9 +1204,7 @@
{
"cell_type": "code",
"execution_count": 46,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1320,9 +1228,7 @@
{
"cell_type": "code",
"execution_count": 47,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1351,9 +1257,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1380,9 +1284,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1422,9 +1324,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1451,9 +1351,7 @@
{
"cell_type": "code",
"execution_count": 51,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1477,9 +1375,7 @@
{
"cell_type": "code",
"execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1501,9 +1397,7 @@
{
"cell_type": "code",
"execution_count": 53,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1559,9 +1453,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "version": "3.6.4"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/notebooks/02.08-Sorting.ipynb b/notebooks/02.08-Sorting.ipynb
index 1a82f745a..fdb02d56c 100644
--- a/notebooks/02.08-Sorting.ipynb
+++ b/notebooks/02.08-Sorting.ipynb
@@ -41,9 +41,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
@@ -58,9 +56,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -92,9 +88,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def bogosort(x):\n",
@@ -106,9 +100,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -140,9 +132,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Fast Sorting in NumPy: ``np.sort`` and ``np.argsort``\n",
+ "## Fast Sorting in NumPy: ```np.sort``` and ``np.argsort``\n",
"\n",
- "Although Python has built-in ``sort`` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n",
+ "Although Python has built-in `sort` and ``sorted`` functions to work with lists, we won't discuss them here because NumPy's ``np.sort`` function turns out to be much more efficient and useful for our purposes.\n",
"By default ``np.sort`` uses an $\\mathcal{O}[N\\log N]$, *quicksort* algorithm, though *mergesort* and *heapsort* are also available. For most applications, the default quicksort is more than sufficient.\n",
"\n",
"To return a sorted version of the array without modifying the input, you can use ``np.sort``:"
@@ -150,20 +142,19 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 1,
+ "metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "array([1, 2, 3, 4, 5])"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "NameError",
+ "evalue": "name 'np' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+ ]
}
],
"source": [
@@ -181,9 +172,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -208,9 +197,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -237,9 +224,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -273,9 +258,7 @@
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -297,9 +280,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -323,9 +304,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -365,9 +344,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -398,9 +375,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -444,9 +419,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"X = rand.rand(10, 2)"
@@ -462,9 +435,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -496,9 +467,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"dist_sq = np.sum((X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2, axis=-1)"
@@ -514,9 +483,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -538,9 +505,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -562,9 +527,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -593,9 +556,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -623,9 +584,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -661,9 +620,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"K = 2\n",
@@ -679,20 +636,19 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 2,
+ "metadata": {},
"outputs": [
{
- "data": {
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RaLCxqUVMzFoSEp4dvjE3P4eDw358fJywsLDI1mfqU//5TR7rKYQeUalUfPbZ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\nYBGCGwAAixDcAABYhOAGAMAi/weoi5+ciElD0QAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "NameError",
+ "evalue": "name 'plt' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# draw lines from each point to its two nearest neighbors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mK\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+ ]
}
],
"source": [
@@ -773,9 +729,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.1"
+ "version": "3.6.4"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/notebooks/05.08-Random-Forests.ipynb b/notebooks/05.08-Random-Forests.ipynb
deleted file mode 100644
index 74da6a281..000000000
--- a/notebooks/05.08-Random-Forests.ipynb
+++ /dev/null
@@ -1,751 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "\n",
- "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
- "\n",
- "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# In-Depth: Decision Trees and Random Forests"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Previously we have looked in depth at a simple generative classifier (naive Bayes; see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb)) and a powerful discriminative classifier (support vector machines; see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)).\n",
- "Here we'll take a look at motivating another powerful algorithm—a non-parametric algorithm called *random forests*.\n",
- "Random forests are an example of an *ensemble* method, meaning that it relies on aggregating the results of an ensemble of simpler estimators.\n",
- "The somewhat surprising result with such ensemble methods is that the sum can be greater than the parts: that is, a majority vote among a number of estimators can end up being better than any of the individual estimators doing the voting!\n",
- "We will see examples of this in the following sections.\n",
- "We begin with the standard imports:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns; sns.set()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Motivating Random Forests: Decision Trees"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Random forests are an example of an *ensemble learner* built on decision trees.\n",
- "For this reason we'll start by discussing decision trees themselves.\n",
- "\n",
- "Decision trees are extremely intuitive ways to classify or label objects: you simply ask a series of questions designed to zero-in on the classification.\n",
- "For example, if you wanted to build a decision tree to classify an animal you come across while on a hike, you might construct the one shown here:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "\n",
- "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Example)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The binary splitting makes this extremely efficient: in a well-constructed tree, each question will cut the number of options by approximately half, very quickly narrowing the options even among a large number of classes.\n",
- "The trick, of course, comes in deciding which questions to ask at each step.\n",
- "In machine learning implementations of decision trees, the questions generally take the form of axis-aligned splits in the data: that is, each node in the tree splits the data into two groups using a cutoff value within one of the features.\n",
- "Let's now look at an example of this."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Creating a decision tree\n",
- "\n",
- "Consider the following two-dimensional data, which has one of four class labels:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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jQJ0sHpMKry8RiF9j3b6ayX4jI4z27sYpLJQoVzfSe/Si+xczSrprerOzs+di\n1eqgI+9zAPAsbLWLjWHd2lWU//rbl7Z5YvkSxuYIwgBmwMiDPhzauA7jY0cwJev9cl7UeSTteMa8\nZx+ifY/imOsR9HEHB8qNf4cHc2fjkZ7+vD3gaJv29G/T7qX91+XC7p3YfPYRI2Ois8tOHdhPsIkJ\no3NM02qYmkL1E35s//RznvzyOyqliv7d2hMTk6Kr2SLRst9A9v7zN6MvXdQoTwSURXj3n1arNty4\nplV+1cycSl26FdlxBOFVE4H4NSaTyej62VeoPvmc5OQkallaFflyh8VNJpOR0qkLVy9dpF6OoBYN\nxAHP0oPIAHmqnsHkbKBW1i/ImtKVedIf46cDvYyBJCD3s4ObpqaUe8l7zbajx7H75nXqbVpP88RE\nVMDBcuXI/PRL+owai7+5GWfWraHy3TtE29gQ3rY9nebMBbJWfvL771+Ue3ZiFB1NesWKOIwYTZM8\n1mOWJIm4hfPpniMIQ9aAvSE6tjcHjI4dpfZnXwMUy1zlnORyOTV/n8/Kzz+hy8XzuKlU+Ds4ENyn\nP70+/LTIjlN96nscOn+Wro8eZpelAKd692VA46Z57ygIpZwIxGWAXC7Xejz7ukhNTcV8724S1Gq2\nkhUcM4FYsu6odpOVmcoEsPDUb86yOo81gQFC79zCrrw7BN/Fm6xMZF2BZ6shXzEx4eKkKfRo2fqF\nx5DJZPSePZcHY99m3d5dyExNaT5idHZmsbYTJ6MaP4mIiHBqWdvQLMf764Ozf6Dr3/NweZZa8/ZN\nrgae5kxqKp460lI+evSQ2rnuNiHrPOU1RNIwj8f9xaVy/YZ47D3EhWNH8Hv8kPpdulOnvHuRHqNK\nw8bcW7aaNYv/wfzWDTItLVF16Ezf9z4s0uMIwqsmArFQogJWL2fAzes6A8o2oM/T/75pYEDQfd3r\nAyuVSmQyGeEP7nPl73mEnD+r851lOND0UhChFpYcNzWlvULBWLKWKQwArlWtRse/FtGjmafe/feo\nWQuPmroHxsnlcsqVK69RlpgQj+Pm9c+D8FP1kpK4suI/pCHDtKY3mZiYkGJkrLVwRyXgFqArRUdq\n9Ro6SouXTCajaTFn76rSoBFVFogEQkLZIgLxGygxMYHAZUuQR4QjVaxEq7Fv61xN6FWQHj/K+64u\nx3/XUqt5sGIpiRMmYW2dlQjz9vnznP3mOyyDzhOjUmOcksLolGTUZN3pdgdcn+4fBhwBRgGylGR8\ngT9r16WrA0+GAAAgAElEQVRCehpKK2vS23di4udfF3u61svHjtI1LExnnfutG8TFxWJv76BR7urq\nxsHmLWjpd0yjvCHwg709n8fGarwPP+nsQsWJmmk7BUEovUQgfsPcCjhNxEdTGRIcjBFZi9xv27iO\nGouWZc+vLYw7ly7yYPNG5OkKTFq0onHPPgSsXo7swnlUxibY9OxFM+9e2Xd9sgoVUaD7EWvuhRu7\nhIWxZftWOo+dQERoCI+GDWPk3ayVmrbyPLmGATAG8Ccr73YKUB9oTtb6wEZkPdZVm5vR9vjpQn/m\n/LAt50akkRGWmZladYlW1lQy031BVOWrb9ga+ph+wXcxJCt5yJ5KlfH6dR4b/Xwx9TuGYVISqTVq\nUWHSFOp4tSneDyIIQpGRSVIxrLqeB5GJ5eWKM2ONJEkc7NOdUWcCtOoWeLZkyO4Dhcr6dGTer9Sc\n/weNn04liQb+srPjo7i47NWgHhkZcWT8RHrP+hmAtLQ0Tnh3YkSu0bDXgVSgWY6yVGDfX4to/9YI\nfGZ+yahFC7ITV+xCew3gZ3YC5cgK7DnfMgfK5UTNW0CLt0YU9CPnmyRJ7O/rzdhAzQsACVg56C16\n/bMkz30T4uM489+/GIaGkOniSrOJk7F30Lx7Djp8gMjNGzGOiSa9QgWqjZtIyy7txN+eHkS2KP2J\nc6UffTNriUBcyhTnL/ity5dw6tGJmjruxnyBG42aUO/72dRq9eKBSro8vHMbZY9OtEzUHCSUAewD\n+ucou2ZmTszWndRu1gKA+9eucuuHb6gRGIBNWir+1jZUT4inQ65jbKtaDU/f05iYmOA7ZjhDfPZm\n1+3IdYxnJGAdWYO9dM0KXl7endZH/LUeBxen+1cucevDafS7chlrIEwuZ08rLzouXVWoBTr8ly6m\n9k/fUzvHHGY/F1csVyynQlOvIuh52SaCi/7EudKPvoG4eOc1CKWKIjlJ5yNRyJra0zvoAhEfTyO5\nAMkRbm3aoBWEAZ2ZkOqmpRKye1f2z5Xr1sN743ZMT50j1i8Qz0PHedigIc9yNUnAMScnTD/9InvV\nokw7zXWPDcl6BJ2br4MD1xo0wiOPfncMDeGIVzMO5rH0YXGoXL8hnX2OcfjPhaz79AsuLV9D/627\nCxWEFQoFBv8t0gjCAO0iwrk/d25huywIQjES74jfIHWat+B0jVoMun1Tq+4+0BhwDQ5my7KldHn/\no3y1baDMyLNO18NuXWXlypUnMSGe0//8jbF7Rf6Wy5HZ2eNSuy71xk6gXo6sWm5Dh3Ntz07qPp0T\n3IOsJSA9gVpkBe8jTs4k/9+X9G/fgVSv5lqjjiHrXXH7mBgc/pqHfzl32o6doP+HLgQjIyPaDh9V\nZO1dPHqY9sG6R5U7nT9PfHwctrZ2OusFQShZIhC/QYyMjJC//Q7Xf5hJnRx3ThfJeocqI2sgkyzi\nSb7bdunSjbtLF1MtRzYpyAqIucPfTVNTyvXqQ24Rjx9xaexwhl+9kp168qaZOUEtW2ultqzn1Zag\nn37i3q+/0e3RQ1SAZZWqHOndl4syAyQTExqOGouzqxsAB9u0A9+jWscMIuuRtUypJGPPTnhFgbio\nmViYkyqTaaQ7fSbDxARDw+IdDS4IQsGJQPyG8Ro/kaDy7vh8/jHVQ0PIAKrzfBBTKmBQuarGPjER\nEZyZ9ytml4OQDA3JaNmKdh/+n8Zi9/W92rGt/yAcN67LHpglAf+gOYgqTC7n5PDR9PVsqdW3C7/9\njzFXr2iU1UpLJWzJP0QNH42Ti4tGXdf33uNBnyHs2LkNuaEhLfoOyHMVsCpfzGDr44f0Dw5GTlbK\nyUNABeAgkA4khIbkfeJe4NyuHcRt2YBpZCRpbuVwHj6KRt28C9RWQTVq24Ej9Rsy4nKQVt0FNzei\nRgzGNCwMhYsLhn360f7dacW6HKMgCPoTg7VKmVc1COLaCX8sJo6heWyMRvnq+g3puO9w9rvY+NhY\nAob2Y9TlS9mPk5XA0o6d6bd2c/a61ABqtZrFb4+mwd7dGJD12LcFWXedauCBqSke8/+hZb+BOoOA\nb9sWDLl1Q6tcDWyY+QNdp2tmUHrRuUpIiCclJQVXV7fsFI/xcbFs/fQDKu/eiRFgSdaFR3eypk/5\nGxpyd8wEes+Zq3eQ8l+6iHqzvqdGjvSbV62teTDrZ1oMG6lXG0XlytFDKD79AO+QEAzIurj4zdmF\nkfFxVMp4/uogUi7n0Aef0O01ykn+KogBSPoT50o/YrCW8EJ127Ql5pffWefZggBzC47YO7DKuxf1\nFi/LDsIAAQvmMzJHEIasxyhvHTvC6c0bNNo0MDCg8wefUs3cnL5kzet1B3qTdVfs1KgJrfoPKtid\nmJ77RIaFsnfCKB60bEJmy8Yc79GJgHWrAbC1s2f8kpUktm5DR7IuKPqTtSCEDGinVNJ/+RJ8lyzS\n61gZGRlIK5ZpBGGAeomJpPy3GLWONYmLU/1OXans48v6jz9j4+jxbJ7xLdVs7TSCMICzSoX1lo2k\npBTfQhCCIOhPBOI3WNO+A+iy+yCWgRepGHCBHqvW416tusY2ptcu6/wlsQMyzwZqlddo1JjTHTtr\nvRe+a2qG5UsGJ6U1baaz/JiTM42HaOdgzk2lUhEwaRzj9uyiS0w0zRQKhl68QJUZX3Bx324g62Kh\n/dJVzKtXH13r9dhLEqpDPi89FsCtS0E01zHwDaD2tas8yrE4wavi6OxMty9m0Om3P6nZdyC1793V\nuV2zRw+5E3ThFfdOEARdRCB+w8lkMlxcXPMcUas21bWOURZVHu9juy9cytqxE9juUZmjtnZsaNyU\na7Pm0PIlgbjpZ1+xun5DjSB+zdyc6MnTsHNw4OSWjRz+eRYnt25ClStXM0DA9i3013FxUDc5iZh1\na7J/tnN0pHbHLnkOkDBOTHhhP5+xsLUlPo9zkGhmhoWFpc66V8XW1paIPBYDCbWwxKGIF2UQBKFg\nxGAt4YUMO3clZv8eHHINJbhsbkGFgboW4QMzMzN6zp1HZmYmaWmp1LOy1utxtHO58njt2MvGxQsx\nunUDpZUVroOGUqucO/t7d6Xf+XPYAzHArqWL6bZpA8ZWTtn7p92+RV5pOYxzDcSybNyEMLJGi+f2\n6AWrN+VUpXoNfJq3oIH/ca26ey1bU9PJScder46trR0n2rRD2rlNa7rYlVZe9M41El0QhJIhArHw\nQm1HjWX7xfO03bKRmoqsFBuB1tYET55O15esUmRkZISRkU2+jmdlZU23T7/QKNs7Ygjjz5/L/tkB\nGH/+LOs/+IAuy9Y9P557BZLJGoSVW4aTs8bP5nb2bJPJmCJJ5Ay7/oBBRt5zonMzGjSUuZcvUT0h\nHhVQFzjToBH1v5+tdxvFqc3/fmN1ahLtjh/HIyODULmcA54t8fz5t5LumiAIT4lALLyQTCaj3+9/\ncX3YSM777EWSG1FzyFt0raFr8b2i9yQslJqnT+qs8zh+nIiIcFxcstZYajVsJNtXLGV0rilQj01M\nsBgwSKMs/OJ5xksS28iaO/1slHctoGJ8vF59O712FTW+n8GwhKztJWCXnT0Vvv1R6117SbF1cGD0\ngQMc2bqb01cuYVu9Fr27dRdTlwShFBGBWNBLHc+W1NEx97e4xcfGUD5X2sZnHJOSiIyNzQ7ExsbG\n1PprEatmfEGzs4E4ZmQQULkKacNH0ynX+2nbatVJMjBgiI6RzVf1eKSckZGBcuF8muQI2jKgX1ws\naxf8Sb227fPxKYuXTCajYfuO0L5jSXdFEAQdRCAWSrWqNWpxukYtquoYnXy9Xj2a57rz9KhbH4/t\ne7lz9QoPoyJo1NJLI/HIM82692RvM0/G51qJKlYmQ9KR9Su380cO0eHObZ11LhcvkJiYkL1usiAI\nwouIUdNCqWZsbIw0cgz3c41OvmdqitnEiRgZ6U7dWL1efZp17KIzCEPWXWLjPxeyol0HrpqaEg8c\ncCvHninv0SlX4hBd5EaGaI/bzqI2MMhOIiIIgvAy4o5YKPU6TJlOgIMDAVs3YfzkCRlublgNHkbv\nqRMLld2nfNVqlN+yiztXr+D3+BF1WrWmiZ4LIzTp0Bnf2nUZkmsdZYDIps1pbKlfRh1BEASR4rKU\nEanj9FfS5+r8zm2Yff0Z7SIjkQEqYEfVargvXkaVBo1KrF+5lfR5el2I86Q/ca70o2+KS3FHLOjt\n4c0b3Fy8ANM7t1FaWWHYrQftxr39xo7AbdpvII/q1GPdqmUYx8SQXrEinpOmYuegezZzZmYm+zee\n4MmlDAwtlXQeVR+PqhULfHxJkji+P4CQ64nYu5vQbXAbjdzfgiC8HsQdcSlTWq80710OIm7CGLwf\nPcguizEwYOe4ifT5368l0qfiOldqtZr09HRMTU2L7CIjMTGR38fsx/rUUEyxRkIiyu4kzb5OoPeY\ndvluLyY6lr8mHcIioBcWKjcUJBDfYBej/mxAjbpVNLZ1crIiIiKB7cuO8MBXhSpNjm3tdPpNb4GL\na8kmHSlNCvP7lJGRwf71J4gJzsTMSaLP+DZYWpZsZrXiVFq/p0obfe+IRSAuZUrrL/jBd8YzcsdW\nrfJAGxsM9x+lYgnMmy3qc5WRkcHhH2diceQQlvHxxFfywHT4KLzGjC9024u+2IVy2XAMco2PDC+3\nn49962FrqzsVZV7+eHcHxttHIcuVMyu29Rpm7OinUebkZMVXw5chbRiEKVkjuSUkImtvZvLaJri5\nay4vqYskSfjuC+Dm4QSQoGo7S7r0b12mBqUV9Pcp9OETFk06hX3QYEywREkGUVV2M+iPCjRqVbsY\nelrySuv3VGkjVl8SipTZ1cs6yz0TEri5Z9cr7k3x2P/hNIYtXsjgu3fwjo5i2PmzNJjxOadWryh0\n2xGBplpBGMA5rBsH1gXo2CNvycnJxJ5y0grCALKzzbkapLmUZKB/EGnb22QHYQAZMlxuDGXH/DMv\nPZ4kSfz54RbOTayHtHYI0rohXJ7SjF/f1Z3z+0XtBB6/yNo/fDiwxS9f+5Zm678/g1vQOEye5nQz\nxBi3e4PY+cMdXuF9jvAaE4FY0Isqx9KIOWUCBhYWr7YzxeDhnds0OLif3J+yikJB6oa1OveJjohg\n/3cz8B39FoemTuK8z94821dn6n7ELcOAzPT8fVmnpqZikKz7Dto004WosFiNsrN7H2KbXkPn9rFX\ndC9akdPRXafI2NgDC9XzzNzmkhOGO4eyd62fXn2Oj49n1vCNHBtZgYQ5Q7g6tTXfe+/m9rV7eu1f\nWiUnJ5EQ6KyzzjioNRfP6L6AFYScChyI//33X4YNG8agQYPYulX7kaVQtqS28tI5b9anYqWXrqr0\nOrjl70vzxESdddYP7pORK/90yN07XB3Uh9EL5zPkwH5GbNlIjUnjOPSLdo7pgG2bKZf8P+zoSAYT\nSSIouy7GJpA2fevmq69OTk4Y1nyksy6xwmmat2ugUWZgJCGhO9gbGL18zeRbR5KwVLtplZtiwwP/\nF+flPrT5NLP77+P9BuuxO/o21hlZC02YYYfrpdFs+OLya33XqFCkI1PovhA1VtmSEKs7K5wg5FSg\nQHzmzBkuXrzIhg0bWL16NU+ePCnqfgmlTLuvvmVJ+45EPx28pAYOurhi+MU3ZWJQinO1GjzKIzlI\nqp29VuKQa7//wpDbNzUeDldJT8dtxX9ERYRnlx2aO4eGH0xlRthZ3seXn/iPtvQlET9S5RG4DL+b\n75HTMpmMZmOtibfQfASdahhO1SFJWOaaw9xrfDOi7LTzdavIpFzr3CtHa5NUeQ9YU7/g6fKhrac5\n+1kFLE8NwF5RV+ejeaMLbbgQ8PreNTo4OGBSN0RnXWJlf1p2aPKKeyS8jgoUiE+cOEGNGjWYOnUq\nU6ZMoWNHkcO2rLO0tGTAxu2c/Oc/NrwzlfWffE75Q8dpPnhonvtIkkRoaAiRkZGvsKcF07Bte47q\nWE1KAaR17qY1etos6ILOdtpHRxG0ZSMA8XGxOK5eQcX0dI1t+vEYW+ePqPPHOd75vm+B+ttjuBet\n/wwjuctGImvuINFrE9VmnWH0595a21aq4k7d96OItnn+PjhVFkV8l2UM+6jLS49VycuYNOK0yjNJ\no7xn3kH67JpYbFNqoyQNY51rYoF5phsRj2Ne2ofSSiaT4TXJiVj7cxrlSab3qTtGlWdmN0HIqUCT\nDuPi4ggLC2Px4sU8fvyYKVOm4OPjU9R9E0oZAwMDWg8cDAMHv3TboH27iVown5pXLpFiZMSF5i2o\n8dXMUpXoIieZTEbj3/9i+Sfv0/FsIBUzMzljY8vV7j3pOeNbre0lue41iyVA9rTu3M7tDA3X/bSo\ngcETWgzxKtT0qA59PemgZxwfMq0LdzoF47dpI+pUOQ1bWtK53zC9Rj33eKsdlw9sRL5/FMZkPYbN\nREF8h9VMmTBI5z6SJJFy3xRbwARrUonWuV2sy0ladGqo34copTr0aY6V3VX8V28gLcQEE8cMmg2w\no3P/l1/kCAIUMBDb2tpStWpVDA0NqVy5MiYmJsTGxmJvb//C/fQdyv2me93P0/WAAGw++4iuz+6E\nFQq8jh5m25NQagcEYG1tXWTHKspz5eTUmIYn/Lhw/DhXb92iYZcutKxaVee2UhsvpFs3tcYtHy1X\njh7TJ2NrZ4VjOSfS0L0+MmamODtbv7LpP05OVjg5NaJ1u4JdCM3d9TbrFx7g3vFMUMuo2tqAEe+P\nxSSPQXwAZk4ShGWN0DbHiShu4kSt7HqFLI5qI2KpWasTKSkpmJubl/h0qIL+PnkPaIX3gFaoVCoO\nbPcnJjyJ9LQk3CuWe/nOgEqlYtdaXx6cTcHIUkWfd5pTqbJ7gfryqrzu31OlSYHmEfv6+rJ69Wr+\n++8/IiIiGDNmDD4+Pi+9uhfzzl6uLMzPO/jBFEau1x5pnAls/vIbun70f0VynJI8V9ER4QSOHsbw\noAvZI63PW1vz4POvaTdpCpA1L/lUJy+G3L6lsa8ErBoyjJ4L/n0lfS2p8/Tfj7tJ/msARmQ9nn3M\naZIIQzJOo1xDE6p6GyCXG3B9eyaZIXbIHRKo2C2TcV/3QJ7HE4fiVNjzFHT6BttnBGN9pTum2BFt\nfxKn/veYMqf/C78bk5OT+W3cHiz9BmGGfdYcbwc/WsxIpefINgXuT3EqC99Tr0Kxprjs0KED586d\nY/DgwUiSxLfffvvGpjkUtJmEheksNwLkIY9fbWeKiaOLKx137GPrf4uR37pJppUVld8aSbtGjbO3\nMTY2xubLb9j/1Wd0fxKGAZAMbGrajFYzfyixvr8qY7/swd+RG4jyqYNjgif2BpUxaXKXEXOzsn9t\n/OsQD2a1xEn5dER2DCTdTmFR0lamzR1Qsp3PJ4VCwdbP7uN2a3h2mVNsGxTL67LJ/TBvTe+a577r\n5hzDwe/t7MFsMmS4xLTn9C/7aN0rPt/JXoTXT4ET03766adF2Q+hDEl31D2vUgVkOr88i9Prwtzc\nnK7vffTCbRr36kt0sxasX7EEo7g4DGrVofuI0RgbG7+iXpYcQ0NDPvxrMA/uPuSc7xZcKtrRpms/\nZDIZKpWKm1uk50H4KWMsiNxXgYtDgji/N4TMRCMcakKfse1K9cAnnw0ncbilvY61qWRH8AEVTM97\n3/BAExx1JXt50g2fddsYNrVHUXZVKIVEhnihyLkNH8WlQ/tpmGte7p5KHjSfOLmEelVyHF1c6Pb5\njJLuRonxqFYJj2qVNMqio6NRP9T9DtQ6qgn/DFtLw+QpyJDxhDTmbN/ItJXtSm1u7OQoJcaY66zL\njNc9Le4ZdYbup4lyDFEqCt014TUgMmsJRa5+u/Y8/H4Om2vX5TFwy9CQtc09sf1jAfZ5rEwkvB5u\nXLrNkq/38c+HB9ix4ohWohN92djYIDlE6ayL5Q4Vk7tnp/A0wgyXi2PZNPt0gftdGGq1mqBzl7l8\n4Spqte4EKFWaOJJo9FBnnWWVF0dT+3q6z2G09Vla9aqls04oW0QgFopFq5GjaXPEn9u7DxDlc4yu\new5Rp03bku6WUAhbFh5hywADMpe8hbRuMPc/68LsodtJSsr/oB1TU1OcO8ShQjOhiIREFNexR3O0\nugwZkWdf/aNp311n+LH7Afb1Kseeni7M8vbhxP7zWtu16tSEzHYHUOfKPxdrd4624yq88Bi93qtL\nVJU9GmVpBtE4DblJ1ZqVC/8hhFJPrL5UyojRiPoT50o/8THRbJkfgCrFCNcGRvQc3lYrU9jLhIdF\n8E/nEFxiOmiUq1Fj9M563p2V/8QkaWlpLHh/L6lHG2Cf1JgE09uEu+/G/e4YrNAeSxBWcTuzzhXf\n3Nzcv0+3rt5l49BUHKO9NLaLcvFl7A4nrYxoKSkprPjuMOEnzFAlG2FTO402E1zx8n55dq2H90LY\nt+giibfNMLRUUr2bCX1Gdyi1g2DF355+inXUtCAIpY9arcZ332kiHyZRy7MCjZrXZfcqPy7Otscx\nNms07z2SmLVlIx+v9sbGxuYlLT53eMMFnGOGaJUbYEDU2bznEr+ImZkZny4ZzL3bD7gUuI0OdSvi\nWmEIf3W8ilWkdiB2aJSuo5Xic3TVTRyjh2uVO0a059DyDUyapRmILSwsmDa3H5IkoVar8zUFq1IV\nd6b8UrrnDQvFRwRiQSgDgm88YNXHF7G64I255Mw+01vs8lpD2lU3KsS2yt7OBCucAiawfvYGJv+s\nPco3L2olOpddBJCUhXvDVaWGB1VqeGT/XH3cWUL/fIBVelaZhERU5X0Me79moY6TXxnRJuh6biBD\nhiIq74sPmUxWIvOghdeXCMSC8JpIS0tj6yJfIs8bgAG4t4I6DcwJXbOSgwcrUjVpbva2toqaqI9U\n4xqbyf2G0gADIs7k7y62Ze/qbFx0EYfkxlp1Do0KP7Q3MiKabb8HEHXRBJnMgPS228DYHnmaNZZV\n0nn73cZU8Chf6OPkh5lbOplIWhcgEhLmbq/27lxfqampnD56AQtrUzzbNCnxTGWCfkQgFoTXgEKh\n4OeRO3E4MQ7Tp/dpYT6p+MnforLqIQ7M0trHADlGmKMiE3mue7u8pszkpUadajiN2EHyf26Yq1yB\nrIAUUXsTkz9sXsBPlSUhIYG/R57A5fIoHJ4GPQmJSM/lfLmlLaamL14zOTU1lY1/HCU80AhJLcOh\nYTqDP/LCwfHFKXdfptuEBqzcewSnMM330pEVfJg8qVmh2n5GqVRyYMsJnlxRYGyjpsc4T5ycHQvU\n1paFR7iyQo7Ng/ZkypLwabiPXl9Xonn7+kXSV6H4iEAslFlqtZr9B9aRqriHgVyFpLShaePeVK5c\no6S7prfT69eQsnkDymu3qBZXhYekYMsHANxiFw6q7wghkip46NzfCDMySdMKxAV53/ruj/040MCf\nmwdSUKbIsa2VwXtTW+HkXLgpaTv+OYHz5REad54yZNifGc6u5XsZOqV7nvtmZmYyd8xO7P0mYPP0\n60x5RmLemVV8uqVzvt6D5+ZRtSI9/0zg8LyNKC5UBpka06YP6P9JZdzKuxa43Wfi4+KZN/4AVqcG\nYYYdCtT8tfYgnX68T4e++bu48d0TSPDP9XFJyxptbipZYxU0nF2f7KT6YZGdq7QTgVgos9Zv/BWv\njirMLZ4vu3Dm1ErU0iiqVqldgj3Tj//SRTT+YSaVFc8e/UYQzhl+Jopo2lGVrpjjQDKRPOY0HrTT\naiPDJRhldD1QZS20ISERVXUXY97P/12STCbDe2g7vHOtfBnyKIzDq4NQphjg3ticrgO98vWONO6G\nMcZob2+EGVFXX3znvm+9P9Z+w5Hn+CqTIcPl0ii2/7OZcV/00rsfujRvX5/m7evz5EkYBgYGuLjU\nLVR7Oa358ThOp97OvgAxwADXJ94cnbOdlt0UL30SkNPFbTFYpWmPKHd51Js9/21l1CciO1dpJgKx\nUCZdv3GZKjWTMLew0yj3bO3A8UN7S30gVqlUqNatzhGEs7iiwpN1bKEu5mTdiVrizAOOoSARU56v\nbJVgfoueX3hgbX+TM5v8USbLsa6ewaTJTSlfUTO1ZEHtW3uCwFlmOMcMRYaMq0QTuHETn67si4WF\nhV5tyM1VedeZKfOsAwg7n4kJ2lNEDJATezV/U7RexM1Nv1WU8iMy0AxXHQPgHIK9ObTtAH1GdNa7\nrYwYY3S99TdAjiJaDBwr7UQgFsqku8HnadneTmedTK69yH1p8+RJGNXu3tFZ15kHrEezrg5DuM0e\n1CiRDGJxb21FsxH2dB3shZOTFV499JvzmZ6eTkREOI6OTpib607Z+Ex8fDwBc8E1x9xicxwx9Xub\ntf/byDs/6jcqu15PG87seoxVpuawsnizG9RqCIv+by9pEUaYl8ugy/i6GkkuDExzp9BAo640Uyt0\nB0hDTElNzF/GMjN33QPmMkjFtaoYsFXaiUAslElyuTFKpQpDQ+0vu/D7ScybcBD7mkr6TfbS+z3i\n48cPuHI1EDtbJ1q06FCsI1JtbGy4a20DCu0v2EeYIaH5zs8AA2rRl1SisZu+krdnvJWv46nValbO\n3seDvebIHlVC7XIOl86xTJzVI881hw+sDcA5bKBWuQFywk/rPyq7Q++W+Pb4h9s+llTK6IIDNYiy\nPYVhh7NcneOJY3RrTMhaNGTNPl+6/ZFAq85Z6yo37+/O/g3XsUuro9FmqkEUtTvr/2i3JNjWSwMd\ni5FFOfgzqN/Lk4Dk1H5cVXb5nsYhqpVGeWyDrUwZLR5Ll3biUkkok9q07kXgKe1cxpkZSiJ2N8F4\nzyASfxvMbwOPEx4W+cK2VCoV6zf+yu1H/9LUKwQ7t1Os3zyDO3evFVf3sbKyJtSrLbrS3m2hJnKs\nucZWjXI1KuJbb2Lcl2/n+3gr5+wnbn4PXIL74ZzZCNeQXqhWvsWi/9uT5z7K9Kygq4u+o7LDHofz\n46BtmO1/i6YZ01AZpnKt+myG7TRBdb8cjtGtNbZ3Cu/AkXmhPEsI2KRVfSpMvUK01bnsbeJMb2I8\ncrdERvEAACAASURBVA89h7XXqw8lpdu0akS7H9EoSzUMx31YKK5u+VulrGHz2nT6I5P4Nht4YuPL\nE2cfUnuv4Z2lnnleSAmlh0hxWcqI1HH6e9m5OnnKh5iEY3i2dsLAwICwx4lsnyXH4fCPGD59oyYh\nIRu9gam/9c6znR27l9LIMxpzc82lCw/tj2LYoFnFlrwhPjaWY++Op/upE1TKzOQJMpbTijhWYkk1\n4rjPEy6glimxrabEo5OMkV900no3q+s8KZVKfDb5E303A1MHFZdXg/s97bvoCIfjTD3mjour9tKW\nwbfus66HSufcYuWQ9by/IO9z+sxPb23D7thYjTIVSuJ6LETu0xM7qZrWPpEmF3j7lAkVKjzPbHX3\n5j1ObLmJpJbRuEdFGjXP/6Cqkvjbu3U1mENLb5AcbIqhjZJaPczoNaJdoVJbxsfHYWRkrPc7+oIQ\n31P6ESkuhTeeV2tvYmKac8p/N6f3HsdgtykmGbVQEIclWdNPZMiIOv/iO4b0zPuYm2tP0fFsbcmp\nU4do29a7QP2TJImMjAyMjY11fvHa2tvTf9MOgo4d5eSVSwScicP60BwsyVr8wI7K2FGZuA4r+Xqj\n9iPivDwJieCfSf7Ynh+IKTYkoCCWLZhwAyc0B7FZxdTn1uVzOgNx1ZqVsR+yjbSVFTBTP5/7Glll\nN6On13tpP25evYP6dFOtcjmGpF50x1SWiq5HAhIqrYufarWqUG1GlZces7SpWa8qNedVffmG+WBr\nq3tshFB6iUAslGl2dnaoDp9j+q7dNMzMQA3s4W+OMRNbpmZt9JKbD7mB7oEzNjZm3E6OLlC/tv97\njCtb00gPscLIMZXK3iqGfdwZnw0nCb+ciaGlko4j61KlhgeNO3WGTp3pkJnJwk82EnWgOnZxzUgy\nvQetzvD2H23ydex13wbg+v/snXdgFNe1h7/ZqlXvEkKFpgKidyEBQvQOBmywAdfYxLHj5CV+yYsd\nO3HixHYc23HiEndjeu+igyREFU1ICJCQUO+9bZ15f8hIrHdVANHs/f5Cd2bu3B1259x77jm/c/rJ\n5r+V2NGfxaSwFk/CzPJ565wv0yMs0Fo3ADz/1ly29T5Mxn4dhho5zqFanlw2iKCeTbrJkiRhMBgQ\nRZF9mxJpqNYTOX0A/oF+5GUV4ai1XpFLVdsFXd9jkNzf4ph6WAZ+fu2vtm3YeFCwGWIbP2rivvqM\nBauWN4c2yYBZFGPHa+xhEk70xGtI2xKNosm6e+lqeiU9uk+86TGt/2g/WX8bjqfhe8nGUqhIq+O5\nVe8woPhl1DhhAr5bc5TB/xfHzCea9jqVSiUvfTiPvJwCzh7dRY8wf8IHtr8SliSJgzuOkpFQi1HS\nkhmvxZrj1o+hFJOMLwOaPjciDtGX8A+c32rfgiAw+4lx8IR5u9Fo5Js3d5N7QElDoZJqXTEO2gB6\nMZsvPkiky8OnGLMwjD12CXTXTrPoV94jn8m/C2Pfb/fhVTgBAaFJyStgF3N/a33lq9VqWf7mPgoS\nVJjqFTiHaol5thtDxnRe7q8NG3cCmyG28aNGOrAfa5pCEylnF59RPLAvL75sKYRxI0GBUVy9EkfP\nkJboaoPBRHqamqWP3ZwwhslkInWDCW+DuW6yGkc8i6Mx0bL69qocxan3dxM1uxI3txZ3o3+gH/6B\nbee16vV6Dh3eSkNjEQlfNuB78hc4ik25w56kcJFN9MHciGtwJ8c5FseaLjS4pOMYnc6y91pXtWqL\nj367FVY9gg8tNYQruUYWB+lROZ7az0N5d/snqLQh6KhDTYvoSrXsGn0fVhI5cTBB2wvY8+VaGkuU\nqH20hHiZSNrWyPlD15iwZGDzc5Akifd+tgWnPU/iff21lg2x544i/zyNgRH3d964jZ82NkNs477j\nQkoSOTlpODi4ERU5BYXi1r+m8vp6q+0C4Db0AkvX/RpHR0er51xnxLAYjp0wcXBPIkpVLUajApnk\nzyPzf3nT4ykrK4Us667eLgymmGQzhSzvwonsXbOJR37e8X3okpIiYve9z9gJbhxcXUPg8bdQ0RK4\n40NfZCgoIhlfWly/NUFHeG3zeHIzLhAUHIBfV0u3cEfIyymgKjYErxuMMIAb3SjkDHmcokbMQVnQ\nCzlKTvIfPAhGgR166qjgMlMGNKlE+Qf68fSf/aipqeG9pbE4H12AHS7okfh85WFG/jGLqYsiORl3\nDvmhiWYKWwDuJaM4+MVqmyG2cV9jM8Q27hsaGxtZt/EdwgeIDBvtQm1tLms3HmHY4MWEBLcf/GO1\nz9594HiiRXuxIDDouYXtGuHrRIyYSMSIiZhMJmQy2S1HtTo7uyC6X4YGy2PVZOOE+UpXQIZRd3OJ\nDYfilzNlpjeCIFB03B9XLKNnvQgjjS3NhrhOnU3vxQb8/f3x97+9urhJcRfwqJxr9ZgKB0QM9GFe\nc5ueBtLYRBhzkCHnkriN4xtyGTSyxduw8s3DeB59Ctn3GZcCAj5l4zj2jx2MnlnLlZMlOOmtezbq\nrmqsttuwcb9gyyO2cd+wM/ZzJk63J7BbkwvYycmOidO8OXl6JaJ4aypJA5//JZuDzevYGoBNMRMY\nMXPOTfcnl8tvK7VEo9HgNbYCE5bSjSWk4IF5uk6Zy3GiZnd8EmIymZArS5vHKBlan2uLQVepHLSF\nhonrGPR+Ggtfurn97vLycjIzMzAYDGbtQSFdqFVds3pNPaX4M8KsTYU9XvShiqZrJESMtebjLjlu\n12yEb8QrbzKxq46icRUwYr2QhcLJYLXdho37BduK2MZ9gSRJiEI+CoWlkMHQkQ6cOHGYiIiYm+7X\nNygI0/LVrPjP+2iSkxHVarQRUcx4+ff3rFbr029O5j81y9EfGoJbXX9qVJnU9d+LU6k9UnZL/dt6\nRSFdHs0kqPusDvctiiIyWcukxaVvLmKCaGHE6mXFPPTHcMbPirzp8ZcUl/Ht/yVQeyQQebUXUuhh\n+i6UMf/5Jm3kgcP7sWPYZkg0T8sxoqeWfKsiIH4M5hLbMKLFhQDcQjPMjpv01v+vZCgwNIrM+dlY\n/v7VLrpkmq/EtdSQln+ctZ+amPPkBJu4hY37EpshtnFfYDKZUCisC/y7uWvITLNUyeooXXv2ouv7\nH93y9Z2Nvb09//vlAi6nZnDhxAYi+vgxeORiCnKL2PHpGqqvqFA4meg31ZEpCzpuhKEpstqobwns\nmvqshi+PvY3fud81G2M9DZimbWXcjJuTwYSmCdMnyw7hmfgkDgg0UknZpRrOvilh75LAtMdGIwgC\nj783gm9+/S2qU9E4GQIpVJ4g1+4ADg3eWBOHbqSSOgoBcO1fzZxnzQseuPfXwlXL68pcjjNzdj/s\n7e2Z8Tdvdry2Do8r01HhQC5HKeMKI/L+QeFrjbz+3Qae+mwgIeEPXr6xjR83NmWt+4yfsmLNuo1/\nJnqSZapQ8tlS+vR6Hj8/86IAP+Vn1RYpKUnklWxm8LAmEZLaGi1r364mc3svZCYHHAYU8taGF5pX\nh6IosuWrg1yLNyLqZLj30zHvhTG4uFpqcB/Ze4qjj4dib/LlIhuxxwMfBlBNNoXuB3g7/mE8v69P\nLEkSJ4+cJTe9hMFRYfQI6cbnr2+n8ZP5KDBXKTsp/xC3QCU9I5yZ/78j8fUzFxC5kpLJqqfz8M5q\nKWvYIC/G6Zl9PPeX2c1tVVVV/GbUCmrLDPRnMe6YG92qMSv4w4bZWMP2feo4tmfVMTqqrGUzxPcZ\nP+Uv+IlTBzAKcQSHtiQc1dfpOHHEjoULfm1x/k/5WbVHxtU0zifvoSArk8aLgdQfH4FPSVO+rp56\n7Jdt5dk3ZiJJEv/8xTpkG5qikaEpf7hk4Hf8avU43D3MVZpWfrCb6r8tII3N9GCiWdqRhETV+G95\nZfU8WkOv1/PB81sw7IvArbEPWmqo6rOLeW/1ZMDIsDY/07WMXHb99xy16XYonEyETrGUg9y+aj8X\nfjWEGnIJxNLtXmp3jqWHZHTv2d3imO371HFsz6pj2CQubTxwjBg2nqQkOXH7jiDIahBFNRpVTx6e\nt/ReD+2Bo1fP3ri5evPhq5n4lMZwY2y4CgfyY51p+H0DZ49dxLh1Is60rH5lyPA5t5TN/17D038y\nV7DqGuJCvlAAkmBmhKEpkpnEEaSevUT4IOtGVaVS8b9fPMyF02mkJG7AxUfNxIcmoVS2Xzu4W68A\nnv9HQJvnGA0mDDSgxnpFLaXWnaqKHOhcVUkbNm4LmyG2cV8xdGg0Q4dG3+th/Ci4euka9qXW82fl\ned0oLi4i7VApzoZxlHCRGnLxph/O+CEgUHZeZXHd2Kkj2TdkFeqkYKv9umhDuHJhU6uG+Dr9hvSm\n35DOz+2d8FAEp98/Q1WBAR8so811wUmED2hSKquqqiQvp4Cg7gE4OTl3+lhs2OgotvQlGzbuIVqt\nlpqa6jvSd/eQIBrcr1g9ZuySjbe3D7WN1SSzEgEZ3YimmmySWYUJAzKl5a6VIAj8/ONx1DtmWu23\n0vE8/UeEWj0GTfvGCXtO8sUfdvPFq7tITrp4ax/uBnQ6HRkZ6VRWVuDk5MzAZ02g1FHKJbPzqtXp\n9F8qx2Qy8a8XN/F+ZDpbJ/jwj9HJfPz7LRiNLcGCoiiyY+VhPnx2Dx88tYd1H+9Bp7OeHnUdrVbL\nycTTpF/KaPM8GzZ+iG1FbMPGPeD82bMs/+NhhCthqEUPHMJLiXrWhzHTh3baPby8PHGNicO0IdJM\nccqIDp8JFTg4OFCSaqA/jzUfCyACXwZxkY1ERlifp/t38yP6fzzI+WsxDmJLupkJI3Yx5+kZal2b\n2mQy8c/n18P2qTgam0RDtn+XStLPtvPUqzNv+vNJksSq9/ZyZaMCeUYYRo9MXEYf5um3Y/APy2LL\nhzu4kL4XleSEV285EYt9GT83hvd/sRHF+sX4XH8mBd0xfNXIh85bWPKHyS375hvno6Fpj7xwh5a3\nDi3nd9/Nxc7OzmIs6z7aT+oKGZqrwzCoy5EP38b8N/raIrRtdAibIbbxo0eSJFJTz9HQUMuAASPu\neS7plm1fcugflYRc/lNLpaNjEH85CY3TBYaNuTn96rZY9s9pfK1ZR/EuP+zKe6H1voTn5AKefXMG\n55NScUodb3GNEjtk9o3MWzbdSo9QmF8ECgP1076l9kpPZNndMXkU4TW2jF/83bKAw3W2fn0I5eaF\nqGkJYHFrDKfwMzmnY5IZMurmJDU3fHqAwn+Oxsf4/WSgPAxpi8RHtV/zyuoFjIyxrJNcUlxK9YEe\nLXrUzZ9ZQ+ZWexp/3cix/Wdh88xmI9x03A63uMfZ/Nl2Fv3SXH97z/pEst4ahI+uW1ODzg8S+vHd\niyv5425/VCpLF78NGzdyW67p8vJyoqOjycrK6qzx2LDRqaSmnmbNhj+il23B2TuOHXv+xIGDG+7Z\neM6cOUbBlQsEXv6VWblBAPeKoRxZnt2p99NoNLy+/FFeTOjG5O1Z/PpICC/+cy5KpZJrl/Jx1vWy\nep2rKgCTyTyvW5IkPn11K5+NL6HstYdx2fFzTIKeIe9m8fvEwfzyX3Oxt7dvdSzX4o1mRvg6btow\nTm/Lu+nPlrZFj73RXABGQEBIjOTcqVSr12SkZWFf0cr+da4/paUlXDlcg4Poa3FYgZrCU5aXnd9Y\njdN1I3wD7imziV1zpN3PYcPGLRtio9HI66+/btVNY8PG/UBVVSVpGesYP8Ud/wAX3D0cGD3OCxfP\nFE4lxd2TMV3LPYlU6Wm22rqRhtw783vy9PRg8IiBZkXjB0b2psLFimUBCqvT+filvVRXtexfb/7y\nIPVfTMGrIhIBATVOdL28iJPvypGk9iVIRX3r0qBlhdUsfyeWNR/FUlVV1X5fooi2wLqGtKs2lPSz\nuVaP9erdnXqPNOudBubh5eXddnlqKwd1pdY9LGocqcq1yWvaaJ9bNsRvv/02ixYtwtvbu/2Tbdjo\nAHq9nt1717Fl+/ts3vYBR48d4HbS3BOObGXUGE+L9sDuzlzLPXE7Q71lZDIjGq+GVnWRlR56q+2S\nJHE8LomtK/ZSUlzWKWMJ6h6AJiYVI+b3rCEfO8mdku2BvDr/Sz54JpZ/LtrH4Y8K0IiWz9M7ezo7\nvrUsrPFDPAcYEa3IatVTQt5+O+refZjSPz/EP8edZ9/64232JZPJUPtaf4bVqgx69rNeJtLbxwvX\n8VmYMDeQehroObsBjUZD70lu1CksV+gGGvGPsOxT42d9HI1U4t3LtlCx0T63tEe8adMmPDw8iIyM\n5NNPP+3wdR1Nbv6p81N8To2NjXzy2WuMmWCPRqMCJIqLEtm24zLPPPW7Vq9r7Vnp9XqyriXTaKzH\n2cWOQUMCzYQf7OyM9+Q5uzgH0HdeI1+t+Rb/zGfNjtUqsol8zNtiXCln0vnyhSQUJ8egMflw3vso\nQQtP8ZsPFnS4AEVrn7VvtDdbN29AhSN2uNJAGSoc6MsjnOITQpKfQpV8PXd3m9U+5CiR6+zafZ7P\n/Wk6rx5dhduJx5rlNg1oSWMLgw3PfN+Xgi75Mzjy5l4mLdDj6eXRan+DF2q4cqEcjdhyjoSELPoo\nk2e1nnv+2rcLeddhA4W7vVEUdsMYkEH3OdW8+M5DKBQKHnpsPBcPr6Dm64k4fu+i1lKDaeYGnv2/\nJRY5zxOXdSP26CWca81d3o0jd/LYzxchl1tqa/8Y+Cm+p+4Ut6SstXjx4uYXwKVLl+jevTuffPIJ\nHh6t/2jApqzVEX5MijW1tTUcO34AjcaRURExbb6Qtm3/iqFR5SgU5ufkXKtGKU1nQP/hFte09qyu\npKdw+twKIsa4Ym+voqysjsS4DKLHh+Di2rSHGbdPZMFDv73NT3jz1NfXs3HLX/D3UXPwra44pTyM\nWvQg120L4UtFnnrFXH7RaDTyxuRd+F54zKxdK1TS9dVDPPKiecWkM2cTyciMQ6aoRTSpcNQEs3TJ\nMsrK6gBoaGhAqVQ2G5Nv39xD/b/mY8KInjrUODcbyQusph+Lmvu+yEaz8oXXqZMVMvSLZGJmjGr3\n89fU1LDxw3hKzymRKSTSr16mb/bvkGNu3EREXH+/gcX/M7XVviRJ4pu/7SJrswN2OQPQOefiEJXB\nU/8Y26YBv051dRUF+UUEBHbF0dHJ7PskSRKHdhzj0v4aJKNAQISKaQtHt1obe9fKIyR9W4d4MRjR\noQqniFwefWMEXQO7tDuOB5Ef03vqTnLXJC6XLFnCG2+8QffulpJxP8T2H9c+99MX3GQysSv2O7SG\nTASZAUl0oXfwePr2Hdbutbtil2MklSHDPWho0HPmZD3BPWYweJD1aj8bt73J6HHW9/yOxdkze8Yy\ni3Zrz0qSJFate5WJ0zws2mN3pDBtZj8uXqjEy3Uu4X0Gt/s57gQVFeUcjFuNRBFZaVVoa514fNmz\nBAVZ/oZi18WR9sLoZvlJs36GrePVnS2G6lTSYbTiQUJ7t0iE1tbquHjWFXflSBI+K6Iu1QU0WjxH\n1rD4z6M5HXeRC8+PwB5Lw/VDw1tCKjpqCKDFPysiUj72S15b98gtlYd8c/J+3M5ar11s/+J6nvjj\nlHb7qK+vJ/3SVXy7euPraxlk1VFu97cniiJ5ebk4Ojri7t7+ROBB5n56T93P3DWJy9upzWrj/mbN\nun8SGSNib9/yYr9wfiumZBMD+o9s9bojiXvo2uMaXfyaIlpdVArGTbLnSNxWugWFtfKSams+2PG5\n4vnkU4QPsJRLFAQBlVrJnh1lhAVPumdGGMDd3YP5c19o+sO6DWqmskBr1QgDGCrM02Iys+MYN8nV\nrM3JSY1CncWOF8MIKH+Y6/pR0gaJf+d+xSsb53D0m61oTj5pFsVdwGmc8Tfry5twijjPWcf/4O3i\nj9zeiM8oLb95fYbV94AkScTvPkFuahVe3eyZMDfSwiviEqqFs5afrVaZQ/+RlpMpURQt+nBwcGDg\nkJtLfboTyGQyAgOD7vUwbDyA3LYhXr58eWeMw8Z9RmbmZQJ71mBv727W3m+AO3H7DrZpiItKzxLS\n39GiPSLKmyMJ25g180mLY3KhC0ZjhYVrOjenmkD/9l2e16mqKqNbmPUAGVdXZyIG/xY3N3erx+9H\neg7y4pAqE2e9pTCEQ1BLkJDRaEShrAEr6UGDh/sQrzJX76pVZmESi/j6q9foNs+VbMePqD0egKLR\njUrpGkbHctyMPUBr3pcP/fGdn8LP345pcxJeWlLOx88exP7EVBxMXSimgqOfb+OJfw+he0hg83lT\nloWz8thuvLJbVr5G9JjG7yZywkIAqquqWf6nQxQfc0BsUOAa3kj0c/4MH3fvja8NG52BTdDjLpOV\nlUldXTVhYX07JHR/r0i7dIpho60bLEHWdnqJTK4HLEUM5HIZgsx6hOnEmIWs2/hXJkxzRa1uei5l\nZfVcuuDMYwtbjH5NTTUJiduRJD2hIQPp1XOQmUEYPCiSw0fjGRlpGc1fW2Vvlr7zIDB8zCD2jVmL\nuL9b894tQJVTKqMe82r+Wy6XYzRa34OvrGhE3tByboXrcXr93yrGzXNHEJqeU0rIFVjmRkivYLRa\nBzw9vTi04Twpb5/Eo7ppf76CTDK9V9IjJZR/PLKHoLEC85dNsLr3v/yVBDyPtqyyNbijObuUVX/4\njlc2tBjiXn26s/ArkT2frKHyohqFxoRflJHnfvsQgiAgiiLvP7kbr8Sn8bu+Yi+GvSlHUX2exsCI\njulVS5LEhk8PkLHbiK5cgWM3AxFLfIicfO88IzZsXMdmiO8SVzMvcSJpNf7d9Dg7K9mycx3uzkMY\nH2NdDvBeo7ZzoqEhD3t7S4Mqim1/bUSD5WoYoKFeh50q0OoxBwcHFj38OgcPb0KnLwBJhqf7MB59\npEXF6FTSIXIKY4mI8kKhkFOYv53lK7az8OHfN6tlOTu7IBl6UVZWhKdni7hExpUqArpG3XdbKYWF\neRw7sRVBVoMkqgnwH8qwoWOajwuCwK8+m8HXr62kIMEJah3QhFQw4gl3omeONDtPEP0wmYzI5eZZ\nibFrC/CtntT8t/3UzcTMb3L7Zl6u4sqZRnoNsCOv+Dhju0xvNqxzn4mmT0QG8WvWUFlUT9lxOUOL\n/wgl34/9cD3vXVjNy58+Yna/mppqqo760sVK0q14ciBX0tIJ6d1SNCK0X09CP7ZeDunA1mM4Hp1t\nKX5SMorD36zusCH+/PXt1H42BZfrEdZXIO7EefTvnmTcbMtAQBs27iY2Q3wX0Gq1nEj6mglTW1SA\n/LpCzrVUTpx0Z8TwmHs4OuuMjpzC5h1HiZlkrlxk0BtRyNouRdev70TOn1nLgMEtK2pJkog/UM3C\nBbNavc7Ozo5pUx61eqy+vp7s/FjGxLQE43Tp6oSnl5Fdsd8wd85zze2zZjzNwUObST2XgiDTIZkc\n6RE0kSEjR1vtW6/XIwjCXfdQXM1MI/niciJjPBGEpolEbvZBYnfnMHXK4ubzHB0defG9ORiNRnQ6\nHfb29lYnFNOn/oz1m95m4DA5fl2daGjQcyyhCl+PKPKURTgY/KijmPBJtdTVyvnuZQHhyKO4Nw7h\ngOYcDYPX4usZx5gxLd/H0PBehP6lF5/87w5CiheZ3U+FA9odMZxKOMew0QOb2+vq6pDXWg9WUmt9\nKC1KJqQd+1lVWcXGfx8haWse/STrAVt1WR3L0S0rLSd/Uxd8RPMxuVYP4PjX6xg3u5ULbdi4S9gM\n8V0gLn47kdGWLtHAbk7E7z/BCO6uIW5sbORi2nlcXdzp2TPE6jlqtZrewXM5tG8TEaM9sLNTknOt\nmovnFTyy4KU2+w8N6YdWW8/hvftQ21djNAoYdV5MnvDSLevuJiTuJCLKUkxCqVJglMzFFwRBYHzM\nQ8BDbfaZdukcF1J3oVRXIEoCJr0nEcMfJiCg/QyAzuDM+W2MneBl1hYQ5ExBXgpVVZUWbnSFQtFq\n+gw0eRUeX/xnzpw5xqmEdNQqJ+bNmkHXrh68W7aWzNVuaAr7IokSa16T8Nj312Z3t0fjYNwTB7FX\n8YGZIb5OZYoaV4tWcNb3IOXgaYbdMMfx9e2CELIXki3dvvVBJxg4vO2o+/KyCv71aDze5x5DxS5M\nGCzSmwBUbh1TrTqyOwnPkjlWj9VddqWhoaFNaU4bNu40NkN8F9Abqr4XqbBEJtdabb9TxO5eQYM+\nhbBwDUWVeo6tkTNqxGJ6dLc0yAP6jyQkeAAJCTvQG+oJDBzN0sXtpy5dv3ZA/5E0NjaiUChue7Vp\nNDSiVFn/ugqym5cRzMu7RnrWWqIneQEtKkz7Yj9httsfcXS8s2IFkiQhCKVm977O0JGeHDu+l6lT\nHrG8sB0EQWDIkFGAeYDb47+fRtWyKuJ2HuPK1XoajkzH9QfCegIC8vODKS0txcvLfIIgU1uXsJSQ\nkKnMj8lkMgYvtSf1tXScG1pc0PXKQno9rMXBwaHNz7Dxw0R8zi1GQKAbY0knljDMPSkN8mJCp3Rs\nRezl58oVWQmOouWzFhwbbEUZbNxzbPWI7wIatQcN9daDlETx7s3E4+J34BOQgVJVR9zBy1xIzkah\nKuJw4lusWf8OxcWFFtdoNBomTVrAjOlP0L9fx4zwD6/vDJdvSPBQ0i9XWD0mmm4+AOtE0g5GRnlZ\ntEdP8OJw3Kab7u86RUUFHDy0gytXLIsOnDt/gh07lxMXvwuTyYQoWd+vNhlF5PLOd5O7uroy+7FJ\n9A6dhKrCuidEVRVEYV6RRXvXSJNVWc5StyPEPDrAon3m0jEMfS+LunHrKA7eTE3kOkLfPM2Sl9vP\nC65MtmveE1bjhAsBpLIBLdWIiBR7HcJ12UFmPRHdbl8Ao2KG0zDooEW7iIhPZG2bXgYbNu4Gtm/g\nXWDMmBms23SSSdPNI3kzM6rpHjDhro0jNW0/ji41hIT6YDCYGB0dbHZ8z44PmDf79U5300mSxNFj\nBygtbxLb9/Xuy4jh0TcVOBUSEs6p1c509ddh79Aisn/6ZDn9wx++6THJZHVYi+xWKuUYpfaLMUGP\neAAAIABJREFUDvwQg8HAhk3/wsOnjLC+bhTknWDFahkTY5bh4ODEhs3v0H+IjOFjnKmpyWXNxnj0\nWusruqNHSpgx6YWbHkNHiY6exJGARMi1NMa6gBR6BA+xaH/klxN498K3KPfOwMHkh4REqetR+v26\ngoCggRbnA0x4KIIJbe8OWEVQmutRd2EQ7gRzgVUoB6fxl29fxNun43WbBUFg/l97s/Y3q3C/OBM1\nTtQqctFH7eGlNyyVuyoqKtiz8gQmncDwab0I6WO9QpUNG52FzRDfBVQqFWMjn2N/7Ao8fepwdJST\nmy3QxWskI0dbDyDqbEwmE5KsginTB7Jv90UmTLaMlome6M6huE1Mn7rYSg+3hiiKfLfq7wyLFAnu\n1+SSLCqMZ9WaJB5d+Nt2jXF9fT37D67FJJWgtpPYtKYc/0BH5HIjSoUnYSGLCAnue9PjkiQ11oRC\nJEkCsf16xaIoErt7JQ26qwgyHfl55fTuZ8/AQU1R4T16udGjF+zd8SkqpQtTZjkjkzU5oJydNbi4\nFpJ6oYTN69XY26vwC3ClX/+unD2dQ8blGjSzrauMdQYajYZe83RU/qsCO6kloE5LFYGzanF0tIx6\nV6lU/N83C4nffYL0xCPI1SJPPNaPbj06P5fXL0KiIk6HAjWNVJLMShzxwY2e1J5R88bcjTzxVhRD\nx3T8/73vkBBC9nZjz7pDVBXqGDDIk1HjH7b4/u1cnsCJd+V4F81Dhpx1n5zDbd4mnn977n0XcW/j\nx8NtS1zeDDZJNCgpKaG+vo6goG7NL+YbuVPScfHxsXTpcQYXFw2H9l9i3ATrNVmPxyuZNf3FTrvv\ngYNbCQpNwdnZ3LCUldZRWTSKyFGtewTq6+tZv+lNJs1waxb6aKjXcXifkaWPvYKPj8tNPav6+nqO\nnziIWmWHWm2HqNxP957mqlVnk0rp3fNZAgPbDthas/59hkc24ujUsqq9mFKASZTo179rc9u1rCpO\nHivk4UdbJj6HD1ymd3gXfHydm9vSUgo4eTKbmAmhaDRqqksiiRjZOUF8rUqBvreH9B1gKnRD5lNJ\nz+kSS16eYmZwiotK2LP8NMYGgeAId6ImDbvjBkmv1/OPpzei2jOHVNYzjOfN0peKSKbcI5HXDk3C\nx7fzqr/V11bxzvBMfMrHmrU3CuX0fOsIc568/7Ib7hU2icuOcdckLm3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s7KmtVrN6RSaL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VODe/cl6vOFZHTqVO/arvsPMKdny9T63bgnnb+fOkb+8GEoBgNeWzdwt0bP45NubYnVO4HsI25l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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.datasets import make_blobs\n",
- "\n",
- "X, y = make_blobs(n_samples=300, centers=4,\n",
- " random_state=0, cluster_std=1.0)\n",
- "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "A simple decision tree built on this data will iteratively split the data along one or the other axis according to some quantitative criterion, and at each level assign the label of the new region according to a majority vote of points within it.\n",
- "This figure presents a visualization of the first four levels of a decision tree classifier for this data:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Levels)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice that after the first split, every point in the upper branch remains unchanged, so there is no need to further subdivide this branch.\n",
- "Except for nodes that contain all of one color, at each level *every* region is again split along one of the two features."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This process of fitting a decision tree to our data can be done in Scikit-Learn with the ``DecisionTreeClassifier`` estimator:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "from sklearn.tree import DecisionTreeClassifier\n",
- "tree = DecisionTreeClassifier().fit(X, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's write a quick utility function to help us visualize the output of the classifier:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "def visualize_classifier(model, X, y, ax=None, cmap='rainbow'):\n",
- " ax = ax or plt.gca()\n",
- " \n",
- " # Plot the training points\n",
- " ax.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=cmap,\n",
- " clim=(y.min(), y.max()), zorder=3)\n",
- " ax.axis('tight')\n",
- " ax.axis('off')\n",
- " xlim = ax.get_xlim()\n",
- " ylim = ax.get_ylim()\n",
- " \n",
- " # fit the estimator\n",
- " model.fit(X, y)\n",
- " xx, yy = np.meshgrid(np.linspace(*xlim, num=200),\n",
- " np.linspace(*ylim, num=200))\n",
- " Z = model.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n",
- "\n",
- " # Create a color plot with the results\n",
- " n_classes = len(np.unique(y))\n",
- " contours = ax.contourf(xx, yy, Z, alpha=0.3,\n",
- " levels=np.arange(n_classes + 1) - 0.5,\n",
- " cmap=cmap, clim=(y.min(), y.max()),\n",
- " zorder=1)\n",
- "\n",
- " ax.set(xlim=xlim, ylim=ylim)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can examine what the decision tree classification looks like:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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LE2ruPffcCJgBWhnkrVxPcJcgHBooUWksMpmMmf/+C5u+W4FZajoqdzfCn1lg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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "visualize_classifier(DecisionTreeClassifier(), X, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you're running this notebook live, you can use the helpers script included in [The Online Appendix](06.00-Figure-Code.ipynb#Helper-Code) to bring up an interactive visualization of the decision tree building process:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
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QuXrzxPNTKC4s4cDxg0yf2Lx2cu9RDVGxU1tt4/j+bayYW9K0PMnDTUGgVwEK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\nVZJV3OI2c0lWcafb2XdgH4YxPlZXmtW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WaMBPi2ZoKHVerjdHo2LAIJlocFhb/IHOhXjd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\nSUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# helpers_05_08 is found in the online appendix\n",
- "import helpers_05_08\n",
- "helpers_05_08.plot_tree_interactive(X, y);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Notice that as the depth increases, we tend to get very strangely shaped classification regions; for example, at a depth of five, there is a tall and skinny purple region between the yellow and blue regions.\n",
- "It's clear that this is less a result of the true, intrinsic data distribution, and more a result of the particular sampling or noise properties of the data.\n",
- "That is, this decision tree, even at only five levels deep, is clearly over-fitting our data."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Decision trees and over-fitting\n",
- "\n",
- "Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from.\n",
- "Another way to see this over-fitting is to look at models trained on different subsets of the data—for example, in this figure we train two different trees, each on half of the original data:"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "[figure source in Appendix](06.00-Figure-Code.ipynb#Decision-Tree-Overfitting)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two clusters).\n",
- "The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from *both* of these trees, we might come up with a better result!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you are running this notebook live, the following function will allow you to interactively display the fits of trees trained on a random subset of the data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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xItv2fMKidZvve6+C1mtoXHQ2jI3GNq5+cKPGTeBU+m6mT2wZYr9VaMPbP6Jb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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# helpers_05_08 is found in the online appendix\n",
- "import helpers_05_08\n",
- "helpers_05_08.randomized_tree_interactive(X, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Just as using information from two trees improves our results, we might expect that using information from many trees would improve our results even further."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Ensembles of Estimators: Random Forests\n",
- "\n",
- "This notion—that multiple overfitting estimators can be combined to reduce the effect of this overfitting—is what underlies an ensemble method called *bagging*.\n",
- "Bagging makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over-fits the data, and averages the results to find a better classification.\n",
- "An ensemble of randomized decision trees is known as a *random forest*.\n",
- "\n",
- "This type of bagging classification can be done manually using Scikit-Learn's ``BaggingClassifier`` meta-estimator, as shown here:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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bZE4OqCl77rkVMAG0Mshes5nAoADsGihR2VZkMhlz//1Xtn33MyZJKahcXRj9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00tdhuDrRZHhrG1qgqXqI7n3PqyMlyMRhJnDMXXEzFYJPgKIYYVNw8PspZlOeTeM2fO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\nVwghhLCz/w+BfjfvUVnKbQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.tree import DecisionTreeClassifier\n",
- "from sklearn.ensemble import BaggingClassifier\n",
- "\n",
- "tree = DecisionTreeClassifier()\n",
- "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8,\n",
- " random_state=1)\n",
- "\n",
- "bag.fit(X, y)\n",
- "visualize_classifier(bag, X, y)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points.\n",
- "In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen: this way all the data contributes to the fit each time, but the results of the fit still have the desired randomness.\n",
- "For example, when determining which feature to split on, the randomized tree might select from among the top several features.\n",
- "You can read more technical details about these randomization strategies in the [Scikit-Learn documentation](http://scikit-learn.org/stable/modules/ensemble.html#forest) and references within.\n",
- "\n",
- "In Scikit-Learn, such an optimized ensemble of randomized decision trees is implemented in the ``RandomForestClassifier`` estimator, which takes care of all the randomization automatically.\n",
- "All you need to do is select a number of estimators, and it will very quickly (in parallel, if desired) fit the ensemble of trees:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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bZE4OqKh477kVMAE0MshZu5mg4EDsmihRaSgymYx5//4r2775EZPEFJSuLox5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\n51Fv3wZAv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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.ensemble import RandomForestClassifier\n",
- "\n",
- "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
- "visualize_classifier(model, X, y);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We see that by averaging over 100 randomly perturbed models, we end up with an overall model that is much closer to our intuition about how the parameter space should be split."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Random Forest Regression\n",
- "\n",
- "In the previous section we considered random forests within the context of classification.\n",
- "Random forests can also be made to work in the case of regression (that is, continuous rather than categorical variables). The estimator to use for this is the ``RandomForestRegressor``, and the syntax is very similar to what we saw earlier.\n",
- "\n",
- "Consider the following data, drawn from the combination of a fast and slow oscillation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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Od48kVn2+ZclvCJsfeSKAeJ4yqGsI1XK7frklduULRZRMKHvdCy0gb79pRWC7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\nAZmIiEgCDMhEREQS+P8p5hEpezc9PwAAAABJRU5ErkJggg==\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "rng = np.random.RandomState(42)\n",
- "x = 10 * rng.rand(200)\n",
- "\n",
- "def model(x, sigma=0.3):\n",
- " fast_oscillation = np.sin(5 * x)\n",
- " slow_oscillation = np.sin(0.5 * x)\n",
- " noise = sigma * rng.randn(len(x))\n",
- "\n",
- " return slow_oscillation + fast_oscillation + noise\n",
- "\n",
- "y = model(x)\n",
- "plt.errorbar(x, y, 0.3, fmt='o');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Using the random forest regressor, we can find the best fit curve as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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lef4veKfTSXdHC3YpjtFqRpFlLPkMl21sxmIvaJEV1pCn5kk/8ryPyMQ4Nkcd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W0LsZgM9Id6AFBdbnSDg6V60GP3Xd6iza0OhULE4zEWGwxgMECFK3RNlb/QBx6OlH8xhlxymECd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\nQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.ensemble import RandomForestRegressor\n",
- "forest = RandomForestRegressor(200)\n",
- "forest.fit(x[:, None], y)\n",
- "\n",
- "xfit = np.linspace(0, 10, 1000)\n",
- "yfit = forest.predict(xfit[:, None])\n",
- "ytrue = model(xfit, sigma=0)\n",
- "\n",
- "plt.errorbar(x, y, 0.3, fmt='o', alpha=0.5)\n",
- "plt.plot(xfit, yfit, '-r');\n",
- "plt.plot(xfit, ytrue, '-k', alpha=0.5);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here the true model is shown in the smooth gray curve, while the random forest model is shown by the jagged red curve.\n",
- "As you can see, the non-parametric random forest model is flexible enough to fit the multi-period data, without us needing to specifying a multi-period model!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example: Random Forest for Classifying Digits\n",
- "\n",
- "Earlier we took a quick look at the hand-written digits data (see [Introducing Scikit-Learn](05.02-Introducing-Scikit-Learn.ipynb)).\n",
- "Let's use that again here to see how the random forest classifier can be used in this context."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dict_keys(['target', 'data', 'target_names', 'DESCR', 'images'])"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from sklearn.datasets import load_digits\n",
- "digits = load_digits()\n",
- "digits.keys()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To remind us what we're looking at, we'll visualize the first few data points:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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Hl946Ie3vrVjRTqXjaOSeNH36dC+r8R6f+IiIyFHY8RERkaOw4yMiIkdhx0dERI7Cjo+I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\n4xMfERE5Cjs+IiJyFHZ8RETkKOz4iIjIUdjxERG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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# set up the figure\n",
- "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n",
- "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
- "\n",
- "# plot the digits: each image is 8x8 pixels\n",
- "for i in range(64):\n",
- " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
- " ax.imshow(digits.images[i], cmap=plt.cm.binary, interpolation='nearest')\n",
- " \n",
- " # label the image with the target value\n",
- " ax.text(0, 7, str(digits.target[i]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can quickly classify the digits using a random forest as follows:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from sklearn.cross_validation import train_test_split\n",
- "\n",
- "Xtrain, Xtest, ytrain, ytest = train_test_split(digits.data, digits.target,\n",
- " random_state=0)\n",
- "model = RandomForestClassifier(n_estimators=1000)\n",
- "model.fit(Xtrain, ytrain)\n",
- "ypred = model.predict(Xtest)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can take a look at the classification report for this classifier:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " precision recall f1-score support\n",
- "\n",
- " 0 1.00 0.97 0.99 38\n",
- " 1 1.00 0.98 0.99 44\n",
- " 2 0.95 1.00 0.98 42\n",
- " 3 0.98 0.96 0.97 46\n",
- " 4 0.97 1.00 0.99 37\n",
- " 5 0.98 0.96 0.97 49\n",
- " 6 1.00 1.00 1.00 52\n",
- " 7 1.00 0.96 0.98 50\n",
- " 8 0.94 0.98 0.96 46\n",
- " 9 0.96 0.98 0.97 46\n",
- "\n",
- "avg / total 0.98 0.98 0.98 450\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from sklearn import metrics\n",
- "print(metrics.classification_report(ypred, ytest))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And for good measure, plot the confusion matrix:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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IC5iISJBHuidcRbJnXzoWLVuJ4uJiNPD3x2ydFi4uLjaby2zbf68nRoahY9d2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\nCfL/ADUiXKng20C6AAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from sklearn.metrics import confusion_matrix\n",
- "mat = confusion_matrix(ytest, ypred)\n",
- "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
- "plt.xlabel('true label')\n",
- "plt.ylabel('predicted label');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Summary of Random Forests\n",
- "\n",
- "This section contained a brief introduction to the concept of *ensemble estimators*, and in particular the random forest – an ensemble of randomized decision trees.\n",
- "Random forests are a powerful method with several advantages:\n",
- "\n",
- "- Both training and prediction are very fast, because of the simplicity of the underlying decision trees. In addition, both tasks can be straightforwardly parallelized, because the individual trees are entirely independent entities.\n",
- "- The multiple trees allow for a probabilistic classification: a majority vote among estimators gives an estimate of the probability (accessed in Scikit-Learn with the ``predict_proba()`` method).\n",
- "- The nonparametric model is extremely flexible, and can thus perform well on tasks that are under-fit by other estimators.\n",
- "\n",
- "A primary disadvantage of random forests is that the results are not easily interpretable: that is, if you would like to draw conclusions about the *meaning* of the classification model, random forests may not be the best choice."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "< [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb) | [Contents](Index.ipynb) | [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb) >"
- ]
- }
- ],
- "metadata": {
- "anaconda-cloud": {},
- "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": "3.5.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/notebooks/Executable Runbook Demo.ipynb b/notebooks/Executable Runbook Demo.ipynb
index d486ca40d..03122d192 100644
--- a/notebooks/Executable Runbook Demo.ipynb
+++ b/notebooks/Executable Runbook Demo.ipynb
@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -20,27 +20,15 @@
"text": [
"env: DB_USER=tankman\n",
"env: DB_PASSWORD=abcd1234\n",
- "env: DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com\n"
+ "env: DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com\n",
+ "The sql module is not an IPython extension.\n"
]
},
{
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'sql'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'env'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'DB_ENDPOINT=xyz-api-prod-db.cxvlabuzou3z.ap-south-1.rds.amazonaws.com'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sql'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sql'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'postgres://$DB_IDE:$DB_PASSWORD@$DB_POINT_BEND:5432/xyz_api_db'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2093\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2094\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2095\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2096\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 86\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m print((\"Loading extensions from {dir} is deprecated. \"\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
- "\u001b[0;32m/anaconda3/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
- "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sql'"
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "UsageError: Line magic function `%sql` not found.\n"
]
}
],
@@ -68,7 +56,29 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: SQL in /anaconda3/lib/python3.6/site-packages (0.4.0)\n",
+ "\u001b[31mgithub-email-explorer 0.2.8 has requirement Jinja2==2.8, but you'll have jinja2 2.10 which is incompatible.\u001b[0m\n",
+ "\u001b[31mecs 0.1 has requirement six==1.5.2, but you'll have six 1.10.0 which is incompatible.\u001b[0m\n",
+ "\u001b[31mawscli 1.15.4 has requirement botocore==1.10.4, but you'll have botocore 1.8.50 which is incompatible.\u001b[0m\n",
+ "\u001b[33mYou are using pip version 10.0.1, however version 18.0 is available.\n",
+ "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install SQL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -100,294 +110,294 @@
"name": "AWS/EC2 CPUUtilization",
"type": "scatter",
"x": [
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