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hw 5 2014
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"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import scipy as sp\n",
"import pandas as pd\n",
"import sklearn\n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"\n",
"\n",
"import sklearn.cross_validation"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
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"source": [
"# Homework 5: In Vino Veritas\n",
"\n",
"Due: Thursday, November 13, 2014 11:59 PM\n",
"\n",
"<a href=https://raw.githubusercontent.com/cs109/2014/master/homework/HW5.ipynb download=HW5.ipynb> Download this assignment</a>\n",
"\n",
"#### Submission Instructions\n",
"To submit your homework, create a folder named lastname_firstinitial_hw# and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://www.winemaniacs.com/wp-content/uploads/2013/04/WineRotator-2000x925.jpg\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can a winemaker predict how a wine will be received based on the chemical properties of the wine? Are there chemical indicators that correlate more strongly with the perceived \"quality\" of a wine?\n",
"\n",
"In this problem we'll examine the wine quality dataset hosted on the <a href=\"https://archive.ics.uci.edu/ml/datasets/Wine+Quality\">UCI website</a>. This data records 11 chemical properties (such as the concentrations of sugar, citric acid, alcohol, pH etc.) of thousands of red and white wines from northern Portugal, as well as the quality of the wines, recorded on a scale from 1 to 10. In this problem, we will only look at the data for *red* wine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1: Data Collection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import only the data for **red** wine from the <a href='https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/'>dataset repository</a>. **Build a pandas dataframe** from the csv file and **print the head**. You might have to change the default delimiter used by the <a href='http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html'>read_csv</a> function in Pandas."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wine_df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', sep=';')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wine_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" <th>quality</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 7.4</td>\n",
" <td> 0.70</td>\n",
" <td> 0.00</td>\n",
" <td> 1.9</td>\n",
" <td> 0.076</td>\n",
" <td> 11</td>\n",
" <td> 34</td>\n",
" <td> 0.9978</td>\n",
" <td> 3.51</td>\n",
" <td> 0.56</td>\n",
" <td> 9.4</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 7.8</td>\n",
" <td> 0.88</td>\n",
" <td> 0.00</td>\n",
" <td> 2.6</td>\n",
" <td> 0.098</td>\n",
" <td> 25</td>\n",
" <td> 67</td>\n",
" <td> 0.9968</td>\n",
" <td> 3.20</td>\n",
" <td> 0.68</td>\n",
" <td> 9.8</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 7.8</td>\n",
" <td> 0.76</td>\n",
" <td> 0.04</td>\n",
" <td> 2.3</td>\n",
" <td> 0.092</td>\n",
" <td> 15</td>\n",
" <td> 54</td>\n",
" <td> 0.9970</td>\n",
" <td> 3.26</td>\n",
" <td> 0.65</td>\n",
" <td> 9.8</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 11.2</td>\n",
" <td> 0.28</td>\n",
" <td> 0.56</td>\n",
" <td> 1.9</td>\n",
" <td> 0.075</td>\n",
" <td> 17</td>\n",
" <td> 60</td>\n",
" <td> 0.9980</td>\n",
" <td> 3.16</td>\n",
" <td> 0.58</td>\n",
" <td> 9.8</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7.4</td>\n",
" <td> 0.70</td>\n",
" <td> 0.00</td>\n",
" <td> 1.9</td>\n",
" <td> 0.076</td>\n",
" <td> 11</td>\n",
" <td> 34</td>\n",
" <td> 0.9978</td>\n",
" <td> 3.51</td>\n",
" <td> 0.56</td>\n",
" <td> 9.4</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.70 0.00 1.9 0.076 \n",
"1 7.8 0.88 0.00 2.6 0.098 \n",
"2 7.8 0.76 0.04 2.3 0.092 \n",
"3 11.2 0.28 0.56 1.9 0.075 \n",
"4 7.4 0.70 0.00 1.9 0.076 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 11 34 0.9978 3.51 0.56 \n",
"1 25 67 0.9968 3.20 0.68 \n",
"2 15 54 0.9970 3.26 0.65 \n",
"3 17 60 0.9980 3.16 0.58 \n",
"4 11 34 0.9978 3.51 0.56 \n",
"\n",
" alcohol quality \n",
"0 9.4 5 \n",
"1 9.8 5 \n",
"2 9.8 5 \n",
"3 9.8 6 \n",
"4 9.4 5 "
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As in any machine learning problem, we have the feature data, usually labeled as $X$, and the target data, labeled $Y$. Every row in the matrix $X$ is a datapoint (i.e. a wine) and every column in $X$ is a feature of the data (e.g. pH). For a classification problem, $Y$ is a column vector containing the class of every datapoint.\n",
"\n",
"We will use the *quality* column as our target variable. **Save the *quality* column as a separate numpy array** (labeled $Y$) and **remove the *quality* column** from the dataframe.\n",
"\n",
"Also, we will simplify the problem to a binary world in which wines are either \"bad\" ($\\text{score} < 7$) or \"good\" ($\\text{score} \\geq 7)$. **Change the $Y$ array** accordingly such that it only contains zeros (\"bad\" wines) and ones (\"good\" wines). For example, if originally $Y = [1,3,8,4,7]$, the new $Y$ should be $[0,0,1,0,1]$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"Y = wine_df.quality.values\n",
"wine_df= wine_df.drop('quality', axis =1)\n",
"Y = np.asarray([1 if i>=7 else 0 for i in Y])\n",
"\n",
"wine_df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>fixed acidity</th>\n",
" <th>volatile acidity</th>\n",
" <th>citric acid</th>\n",
" <th>residual sugar</th>\n",
" <th>chlorides</th>\n",
" <th>free sulfur dioxide</th>\n",
" <th>total sulfur dioxide</th>\n",
" <th>density</th>\n",
" <th>pH</th>\n",
" <th>sulphates</th>\n",
" <th>alcohol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 7.4</td>\n",
" <td> 0.70</td>\n",
" <td> 0.00</td>\n",
" <td> 1.9</td>\n",
" <td> 0.076</td>\n",
" <td> 11</td>\n",
" <td> 34</td>\n",
" <td> 0.9978</td>\n",
" <td> 3.51</td>\n",
" <td> 0.56</td>\n",
" <td> 9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 7.8</td>\n",
" <td> 0.88</td>\n",
" <td> 0.00</td>\n",
" <td> 2.6</td>\n",
" <td> 0.098</td>\n",
" <td> 25</td>\n",
" <td> 67</td>\n",
" <td> 0.9968</td>\n",
" <td> 3.20</td>\n",
" <td> 0.68</td>\n",
" <td> 9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 7.8</td>\n",
" <td> 0.76</td>\n",
" <td> 0.04</td>\n",
" <td> 2.3</td>\n",
" <td> 0.092</td>\n",
" <td> 15</td>\n",
" <td> 54</td>\n",
" <td> 0.9970</td>\n",
" <td> 3.26</td>\n",
" <td> 0.65</td>\n",
" <td> 9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 11.2</td>\n",
" <td> 0.28</td>\n",
" <td> 0.56</td>\n",
" <td> 1.9</td>\n",
" <td> 0.075</td>\n",
" <td> 17</td>\n",
" <td> 60</td>\n",
" <td> 0.9980</td>\n",
" <td> 3.16</td>\n",
" <td> 0.58</td>\n",
" <td> 9.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 7.4</td>\n",
" <td> 0.70</td>\n",
" <td> 0.00</td>\n",
" <td> 1.9</td>\n",
" <td> 0.076</td>\n",
" <td> 11</td>\n",
" <td> 34</td>\n",
" <td> 0.9978</td>\n",
" <td> 3.51</td>\n",
" <td> 0.56</td>\n",
" <td> 9.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.70 0.00 1.9 0.076 \n",
"1 7.8 0.88 0.00 2.6 0.098 \n",
"2 7.8 0.76 0.04 2.3 0.092 \n",
"3 11.2 0.28 0.56 1.9 0.075 \n",
"4 7.4 0.70 0.00 1.9 0.076 \n",
"\n",
" free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
"0 11 34 0.9978 3.51 0.56 \n",
"1 25 67 0.9968 3.20 0.68 \n",
"2 15 54 0.9970 3.26 0.65 \n",
"3 17 60 0.9980 3.16 0.58 \n",
"4 11 34 0.9978 3.51 0.56 \n",
"\n",
" alcohol \n",
"0 9.4 \n",
"1 9.8 \n",
"2 9.8 \n",
"3 9.8 \n",
"4 9.4 "
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the <a href='http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.as_matrix.html'>as_matrix</a> function in Pandas to **save the feature information in your data frame as a numpy array**. This is the $X$ matrix."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X =wine_df.as_matrix()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2: Unbalanced Classification Evaluation\n",
"\n",
"In this section, we explore a number of different methods to predict the quality of a wine $Y$ based on the recorded features $X$. Formulated as a machine learning problem, we wish to predict the **target** $Y$ as a function of the **features** $X$.\n",
"\n",
"Because we have defined $Y$ as a binary variable (encoding *bad* as 0 and *good* as 1), this is a **classification** problem. In class, we have discussed several approaches to classifiction incuding **decision trees**, **random forests**, and **Support Vector Machines (SVM)**. \n",
"\n",
"For this problem, we will focus on **random forests**, but we will later in the Problem set invoke these other techniques. Recall from class that the random forest technique works by aggregating the results from a number of randomly perturbed decision trees constructed to explain the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(a)** In class, we saw that for a fixed set of data, a decision tree algorithm will generate a single fixed tree to perform a classification task. Describe how a random forest is built from individual decision trees. What are the sources of randomness in the process that are used to build a diverse set of decision trees?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE.**\n",
"\n",
"Random forest basically aggregates a group of decision trees together. It adds randomness in 2 ways , one is by sampling with replacement(boot strap sampling) from the training data and then fitting a tree for each of these samples. Then splitting on a feature in the decision tree, random forest considers random subset of variables to split on. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(b)** There are many ways to construct a random forest -- these differences in the method of construction are encoded as *tuning parameters*. As is often the case when our goal is to construct a good prediction, we can set these tuning parameters to obtain the best projected performance in a prediction task. One of the most important tuning parameters in building a random forest is the number of trees to construct. \n",
"\n",
"Here, you should apply the random forest classifier to the wine data and use cross-validation to explore how the score of the classifier changes when varying the number of trees in the forest. Use the <a href='http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html'>random forest classifier</a> built into the scikit-learn library and the <a href='http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_score.html#sklearn.cross_validation.cross_val_score'>cross_val_score</a> function (using the default scoring method) to **plot the scores of the random forests as a function of the number of trees** in the random forest, ranging from 1 (simple decision tree) to 40. You should use 10-fold cross-validation. Feel free to use the boxplot functionality of the <a href='http://web.stanford.edu/~mwaskom/software/seaborn/index.html'>seaborn</a> library."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.cross_validation import cross_val_score\n",
"\n",
"scores =[]\n",
"\n",
"for val in range(1,41):\n",
" clf = RandomForestClassifier(n_estimators = val )\n",
" validated = cross_val_score(clf, X, Y, cv =10)\n",
" scores.append(validated)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.boxplot(scores)\n",
"plt.xlabel('number of trees')\n",
"plt.ylabel('Classification scores')\n",
"plt.title('Classification score for number of trees')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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SlMLmjTROkgOAQqFAYWsnn3kw/oBo49Ynyc9Id1mEx5c3c8m91yWW2dj9LPlp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\n",
"text": [
"<matplotlib.figure.Figure at 0x1635df28>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(c)** Describe the relationship between cross validation accuracy and the number of trees. What tradeoffs should we consider when choosing the number of trees to use?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE.** <br>\n",
"From the box plot I can see that when more trees are addead then the accuracy of the classifier increases. But adding more trees will increase the computational cost of the algorithim."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(d)** These accuracy scores look very promising compared to, say, classifying the wine using a coinflip. However, in binary classification problems, accuracy can be misleading if one class (say, bad wine) is much more common than another (say, good wine), this is, when the classes are **unbalanced**.\n",
"\n",
"**Print** the percentage of wines that are labeled as \"bad\" in the dataset and **plot the same boxplot** as the last question (feel free to copy/paste), but this time draw a line across the plot denoting the **accuracy** of always guessing zero (\"bad wine\")."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len_y = len(Y)\n",
"temp = [i for i in Y if i ==0]\n",
"temp_1 = temp.count(0)\n",
"\n",
"percentage = float(temp_1)/float(len_y) \n",
"\n",
"print float(temp_1)/float(len_y) * 100\n",
"\n",
"sns.boxplot(scores)\n",
"plt.axhline(y = percentage, ls = '--')\n",
"\n",
"plt.xlabel('number of trees')\n",
"plt.ylabel('Classification Scores')\n",
"plt.title('Classification scores for trees')\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"86.4290181363\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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neSnbNWjf7zogE9kzYyqpi4hI6TKZDG1tbdx478WxZV5sa2Z6zXQdmI0yvdBF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\nSbGD7Dh1Gfj+nLFBvQmyjwO3SVo3hP6ayEVqkEM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"text": [
"<matplotlib.figure.Figure at 0x170e2a58>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Evaluation Metrics\n",
"\n",
"When there are unbalanced classes in a dataset, guessing the more common class will often yield very high accuracy. For this reason, we usually want to use different metrics that are less sensitive to imbalance when evaluating the predictive performance of classifiers. These metrics were originally developed for clinical trials, so to keep with the standard terminology, we define \"good\" wines (value of 1) as \"positive\" and the \"bad\" wines (value of 0) as the \"negatives\". We then define the following:\n",
"\n",
"$P$ - number of positives in the sample.\n",
"\n",
"$N$ - number of negatives in the sample.\n",
"\n",
"$TP$ - number of true positives: how many of the \"positive\" guesses of the classifier are true.\n",
"\n",
"$FP$ - number of false positives: how many of the \"positive\" guesses of the classifier are actually negatives.\n",
"\n",
"$TN$ - number of true negatives; similarly, this is how many of the \"negative\" guesses of the classifier are true.\n",
"\n",
"$FN$ - number of false negatives; how many of the \"negative\" guesses are actually positives.\n",
"\n",
"When calling the score functions in scikit-learn you obtained the default measure of efficiency, which is called **accuracy**. This is simply the ratio of successful guesses (both positives and negatives) across all samples:\n",
"$$\\text{accuracy} = \\frac{TP + TN}{P+N}.$$\n",
"In our case, when the two classes (good and bad wines) are very unbalanced in the sample, we should look for a better measure of efficiency. \n",
"\n",
"Usually, the goal is to identify the members of the positive class (the rare class) successfully -- this could be either the good wines or the patients presenting a rare disease. It is common practice to define the following ratios:\n",
"\n",
"The **recall** rate (also called the sensitivity or the true positive rate) is the ratio of true positive guesses among all positives:\n",
"$$\\text{recall} = \\frac{TP}{P}=\\frac{TP}{TP+FN}.$$\n",
"The **precision** is the ratio of the true positive guesses over all the positive guesses:\n",
"$$\\text{precision} = \\frac{TP}{TP+FP}.$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(e)** Describe in words what the **difference** is between **precision** and **recall**. Describe an **application scenario** where precision would be more important than recall, and one scenario where recall would be more important than precision."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE.**\n",
"\n",
"precision or recall is an evaluation metric for classification problem with skewed classes. For many applications we have to control the trade off between precision and recall. Cancer detection is a good example of precision and recall. If you have a high precision ,you have a high chance of telling a patient that they do not have a cancer when they actually have one. But If you have high recall, you may classify most of the patients as the ones having cancer. So cancer detection is a good example for precision and recall. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because precision and recall both provide valuable information about the quality of a classifier, we often want to combine them into a single general-purpose score. The **F1** score is defined as the harmonic mean of recall and precision:\n",
"$$F_1 = \\frac{2\\times\\text{recall}\\times\\text{precision}}{\\text{recall} + \\text{precision}}.$$\n",
"\n",
"The harmonic mean of two numbers is closer to the smaller of the two numbers than the standard arithmetic mean. The F1 score thus tends to favor classifiers that are strong in both precision and recall, rather than classifiers that emphasize one at the cost of the other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(f)** For this part, **repeat the cross-validation analysis in part (b) changing the `scoring` parameter** of the cross_val_score function such that the measure used is the **F1 score**. **Comment** briefly on these numbers. Hint: See the <a href=\"http://scikit-learn.org/stable/modules/model_evaluation.html\">scikit-learn documentation</a> for the options you can use for the *scoring* parameter."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## your code here\n",
"scores =[]\n",
"\n",
"for val in range(1,41):\n",
" clf = RandomForestClassifier(n_estimators = val )\n",
" validated = cross_val_score(clf, X, Y, cv =10, scoring ='f1')\n",
" scores.append(validated)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.boxplot(scores)\n",
"plt.xlabel('Number of trees')\n",
"plt.ylabel('F1 score')\n",
"plt.title('F1 Scores as a function of the number of trees')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Ry20r+zjYxtw2sh3qYS3No5pnt4vHbX46P1yxt3ka7zT9nfTmCXfXpfYNPEUO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\n8/8BGbqwCkQ7OVQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x170eef60>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR DISCUSSION HERE.** <br>\n",
"As you can see the scores are centered around 40% mark. There is only a little gain in accuracy as the number of trees increase. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 3: Classifier Calibration\n",
"\n",
"Many classifiers, including random forest classifiers, can return **prediction probabilities**, which can be interpreted as the probability that a given prediction point falls into a given class (i.e., given the data $X$ and a candidate class $c$, the prediction probability states $P(Y = c | X)$). However, when the classes in the training data are **unbalanced**, as in this wine example, these prediction probabilities calculated by a classifier can be inaccurate. This is because many classifiers, again including random forests, do not have a way to internally adjust for this imbalance.\n",
"\n",
"Despite the inaccuracy caused by imbalance, the prediction probabilities returned by a classifier can still be used to construct good predictions if we can choose the right way to turn a prediction probability into a prediction about the class that the datapoint belongs to. We call this task **calibration**.\n",
"\n",
"If a classifier's prediction probabilities are accurate, the appropriate way to convert its probabilities into predictions is to simply choose the class with probability > 0.5. This is the default behavior of classifiers when we call their `predict` method. When the probabilities are inaccurate, this does not work well, but we can still get good predictions by choosing a more appropriate cutoff. In this question, we will choose a cutoff by cross validation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(a)** Fit a random forest classifier to the wine data **using 15 trees**. Compute the **predicted probabilities** that the classifier assigned to each of the training examples (Hint: Use the `predict_proba` method of the classifier after fitting.). As a **sanity test**, construct a prediction based on these predicted probabilities that labels all wines with a predicted probability of being in class 1 > 0.5 with a 1 and 0 otherwise. For example, if originally probabilities $= [0.1,0.4,0.5,0.6,0.7]$, the predictions should be $[0,0,0,1,1]$. **Compare** this to the output of the classifier's `predict` method, and **show that they are the same**. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## your code here\n",
"\n",
"clf = RandomForestClassifier(n_estimators= 15)\n",
"clf.fit(X,Y)\n",
"\n",
"prediction =(clf.predict_proba(X)[:,1] > 0.5).astype(int)\n",
"(clf.predict(X) == prediction).all()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"True"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(b)** **Write a function** `cutoff_predict` that takes a **trained** classifier, a data matrix X, and a cutoff, and generates predictions based on the classifier's predicted **probability and the cutoff value**, as you did in the previous question."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"\n",
"cutoff_predict(clf, X, cutoff)\n",
"\n",
"Inputs:\n",
"clf: a **trained** classifier object\n",
"X: a 2D numpy array of features\n",
"cutoff: a float giving the cutoff value used to convert\n",
" predicted probabilities into a 0/1 prediction.\n",
"\n",
"Output:\n",
"a numpy array of 0/1 predictions.\n",
"\"\"\"\n",
"## your code here\n",
"def cutoff_predict(clf, X, cutoff):\n",
" return (clf.predict_proba(X)[:,1]> cutoff).astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(c)** Using **10-fold cross validation** find a cutoff in `np.arange(0.1,0.9,0.1)` that gives the best average **F1 score** when converting prediction probabilities from a **15-tree** random forest classifier into predictions.\n",
"\n",
"To help you with this task, we have provided you a function `custom_f1` that takes a cutoff value and returns a function suitable for using as the `scoring` argument to `cross_val_score`. **This function uses the `cutoff_predict` function that you defined in the previous question**.\n",
"\n",
"Using a **boxplot**, compare the **F1 scores** that correspond to each candidate **cutoff** value."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scores =[]\n",
"\n",
"def custom_f1(cutoff):\n",
" def f1_cutoff(clf, X, y):\n",
" ypred = cutoff_predict(clf, X, cutoff)\n",
" return sklearn.metrics.f1_score(y, ypred)\n",
" \n",
" return f1_cutoff\n",
"\n",
"## your code here\n",
"\n",
"for cutoff in np.arange(0.1, 0.9, 0.1):\n",
" \n",
" clf = RandomForestClassifier(n_estimators= 15)\n",
" validated = cross_val_score(clf, X, Y, cv =10, scoring = custom_f1(cutoff))\n",
" scores.append(validated)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"C:\\Users\\Manu\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\metrics.py:1771: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.boxplot(scores, names = np.arange(0.1, 0.9, 0.1))\n",
"plt.title('F scores for each tree')\n",
"plt.xlabel('each cut off value')\n",
"plt.ylabel('custom F score')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x173dd128>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(d)** According to this analysis, which cutoff value gives the **best predictive results**? **Explain** why this answer makes sense in light of the **unbalanced** classes in the training data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE.** <br>\n",
"It clearly shows that a lower than 0.5 gives a very good f score. As there are very few class one labels in our data it is obvious that a lower cut off giving a very high f1 score. It is very similar to prof. andrew ngs machine learning course (week6), cancer example. We adjusting the classifiers cutoff to catch the rarer cases. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 4: Visualizing Classifiers Using Decision Surfaces\n",
"\n",
"One common visual summary of a classifier is its decision surface. Recall that a trained classifier takes in features $X$ and tries to predict a target $Y$. We can visualize how the classifier translates different inputs $X$ into a guess for $Y$ by plotting the classifier's **prediction probability** (that is, for a given class $c$, the assigned probability that $Y = c$) as a function of the features $X$. Most classifiers in scikit-learn have a method called `predict_proba` that computes this quantity for new examples after the classifier has been trained."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(a)** Decision surface visualizations are really only meaningful if they are plotted against inputs $X$ that are one- or two-dimensional. So before we plot these surfaces, we will first find **two \"important\" dimensions** of $X$ to focus on. Recall that in the last homework we used SVD to perform a similar task. Here, we will use a different dimension reduction method based on random forests.\n",
"\n",
"Random forests allow us to compute a heuristic for determining how \"important\" a feature is in predicting a target. This heuristic measures the change in prediction accuracy if we take a given feature and permute (scramble) it across the datapoints in the training set. The more the accuracy drops when the feature is permuted, the more \"important\" we can conclude the feature is. Importance can be a useful way to select a small number of features for visualization.\n",
"\n",
"As you did in the last question, train a random forest classifier on the wine data using **15 trees**. Use the `feature_importances_` attribute of the classifier to obtain the relative importance of the features. These features are the columns of the dataframe. Show a simple **bar plot** showing the relative importance of the named features of the wines in the databes."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## your code here\n",
"\n",
"clf = RandomForestClassifier(n_estimators= 15)\n",
"clf.fit(X,Y)\n",
"\n",
"imp = clf.feature_importances_\n",
"names = wine_df.columns\n",
"\n",
"imp, names = zip(*sorted(zip(imp, names)))\n",
"\n",
"\n",
"plt.barh(range(len(names)), imp, align = 'center')\n",
"plt.yticks(range(len(names)), names)\n",
"\n",
"\n",
"plt.xlabel('Importance of features')\n",
"plt.ylabel('Features')\n",
"plt.title('Importance of each feature')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fvympTdIGkg4cyXObmZnZmnJX9JDNht7fjOgfAjYFiIj/qHKfQYuIr0bEH/pZ/4mI+Buw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\n",
"text": [
"<matplotlib.figure.Figure at 0x9ba8278>"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(b)** Below, we have provided you with a function `plot_decision_surface` that plots a classifier's decision surface, taking as arguments a classifier object, a two-column feature matrix, and a target vector.\n",
"\n",
"Using this function and the results from the \"importance\" analysis above, **subset** the data matrix to include just the **two features of highest importance**. Then **plot** the decision surfaces of a <a href='http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier'>decision tree classifier</a>, and a random forest classifier with **number of trees set to 15**, and a <a href='http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC'> support vector machine</a> **with `C` set to 100, and `gamma` set to 1.0**. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.tree import DecisionTreeClassifier\n",
"import sklearn.linear_model\n",
"import sklearn.svm\n",
"\n",
"def plot_decision_surface(clf, X_train, Y_train):\n",
" plot_step=0.1\n",
" \n",
" if X_train.shape[1] != 2:\n",
" raise ValueError(\"X_train should have exactly 2 columnns!\")\n",
" \n",
" x_min, x_max = X_train[:, 0].min() - plot_step, X_train[:, 0].max() + plot_step\n",
" y_min, y_max = X_train[:, 1].min() - plot_step, X_train[:, 1].max() + plot_step\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
" np.arange(y_min, y_max, plot_step))\n",
"\n",
" clf.fit(X_train,Y_train)\n",
" if hasattr(clf, 'predict_proba'):\n",
" Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:,1]\n",
" else:\n",
" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) \n",
" Z = Z.reshape(xx.shape)\n",
" cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Reds)\n",
" plt.scatter(X_train[:,0],X_train[:,1],c=Y_train,cmap=plt.cm.Paired)\n",
" plt.show()\n",
" \n",
"## your code here\n",
"imp_fe = np.argsort(imp)[::-1][0:2]\n",
"X_imp = X[:, imp_fe]\n",
"\n",
"algorithims = [DecisionTreeClassifier(), RandomForestClassifier(), sklearn.svm.SVC(C =100.0, gamma= 1)]\n",
"\n",
"title = ['Decision Tree Classifier', 'Random Forest Classifier', \n",
" 'Support Vector Machine']\n",
"\n",
"for i in xrange(3):\n",
" plt.title(title[i]) \n",
" plt.xlabel('Feature1')\n",
" plt.ylabel('Feature 2')\n",
" plot_decision_surface(algorithims[i],X_imp, Y)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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6Ul/fwC9XrsY+ah6NI2fzi1c+oKnJ98OglOzsNkuFtlak2zmSaWDs3FgiZkSw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\ny17juQcXk5IYx9+WvUPGxOl4GLzYs2EVS26cQeaho7R5x3Bo1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D+LwzmU+k+gEFghSRLA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\n",
"text": [
"<matplotlib.figure.Figure at 0x172f4fd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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NjuS1baXc4+j5359P+z9RweiM2F7ZlxCB0J2ZNzVAup/rIbpg1SfbOeyKZ/Ck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\n0vu2rid39bdUVFdTX1PHjNvvQ61WU5R3yJmWNTbaj7VffYi7t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/seyHOfVAimnweLgZwH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\n",
"text": [
"<matplotlib.figure.Figure at 0x175d8e80>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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8XbMm4zh1mK6p3YmPj6OnqZw+aT2YP/VGCnasJFffz8lDeynft4GZN8q9LuF7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\n5tL0j36BQZ2SroRGx1NWUckdU8bw10+W4ZsymtbGWlyrcpDGz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"text": [
"<matplotlib.figure.Figure at 0x16590908>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(c)** Recall from the lecture that there is a tradeoff between the bias and the variance of a classifier. We want to choose a model that generalizes well to unseen data. With a **high-variance** classifier we run the risk of **overfitting** to noisy or unrepresentative training data. In contrast, classifier with a **high bias** typically produce simpler models that tend to **underfit** the training data, failing to capture important regularities. \n",
"\n",
"Discuss the differences in the above decision surfaces in terms of their **complexity** and **sensitivity** to the training data. How do these properties relate to **bias** and **variance**?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(d)** The <a href='http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC'> SVM</a> implementation of sklearn has an **optional parameter** `class_weight`. This parameter is set to `None` per default, but it also provides an `auto` mode, which uses the values of the labels Y to **automatically adjust weights** inversely proportional to class frequencies. As done in sub-problem 4(b), **draw the decision boundaries** for two SVM classifiers. **Use `C=1.0`, and `gamma=1.0`** for **both** models, but for the first SVM set `class_weigth` to **`None`**, and for the second SVM set `class_weigth` to **`'auto'`**. (Hint: `None` is a keyword in Python, whereas the `'auto'` is a String and needs the quotation marks.) "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## your Code here\n",
"\n",
"svm = [sklearn.svm.SVC(C =1.0, gamma = 1.0, class_weight= None), sklearn.svm.SVC(C =1.0, gamma = 1.0, class_weight= 'auto')]\n",
"\n",
"title = ['Svm without class weight', ' Svm with class weight']\n",
"for i in xrange(2):\n",
" plt.title(title[i])\n",
" plt.xlabel('Feature 1')\n",
" plt.ylabel('Feature 2')\n",
" \n",
" plot_decision_surface(svm[i], X_imp, Y)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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6aQPx3ZOYfuO1f/999dadVHUfRL/EngBk791J/2Mn6Nu7h48j854ok4lz+2v5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Ut5c93H3HX9Mj7YeZSohGQEn0BNSRGz772W42eLcbhcFB7ZT23FBeYsvVmU22xrZsz4Kf7JQvzI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\nWxAEQRAGGBG8BUEQBGGA+f/fJVEZNF76LgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1660dda0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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393M1QgSWdv/HKKWeBtKBKUqptnNy2oCOn5AhutWEkRkMTIhj56E9zB4xiOFD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\n5t3pLnHt+9J1Gq5RL3Gp9teKdcHef/2VyjwFo9mb6jVZjDmLs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"text": [
"<matplotlib.figure.Figure at 0x17305a90>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**(e)** Discuss the difference in the decision boundary with respect to **precision**, **recall**, and **overall performance**. How could the performance be **improved**? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YOUR ANSWER HERE**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Submission Instructions\n",
"\n",
"To submit your homework, create a folder named **lastname_firstinitial_hw#** and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. *If we cannot access your work because these directions are not followed correctly, we will not grade your work.*\n"
]
}
],
"metadata": {}
}
]
}
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