{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification\n", "\n", "\n", "Back in the Review Notebook we had Women Athlete Data that looked like:\n", "\n", "Name | \tSport\t| Height | \tWeight\n", ":---: | :---: | :---: | :---: \n", "0\t| Asuka Teramoto\t| Gymnastics\t| 54\t| 66\n", "1\t| Brittainey Raven | \tBasketball\t| 72\t| 162\n", "2\t| Chen Nan\t| Basketball\t| 78\t| 204\n", "3\t| Gabby Douglas\t| Gymnastics\t| 49 | \t90\n", "4\t| Helalia Johannes\t| Track\t| 65 | \t99\n", "5\t| Irina Miketenko\t| Track\t| 63\t| 106\n", "6\t| Jennifer Lacy\t| Basketball | \t75\t | 175\n", "7\t| Kara Goucher\t| Track\t| 67\t| 123\n", "8\t| Linlin Deng\t| Gymnastics\t| 54 | 68\n", "9\t| Nakia Sanford\t| Basketball\t| 76\t| 200\n", "\n", "We looked over the data and then came up with `if` rules that used height and weight to predict what sport someone played. So, for example, we might have a rule like:\n", "\n", "\n", " if athlete['Height'] > 60:\n", " return 'Gymnastics'\n", " \n", "\n", "Once we wrote our rules we determined their accuracy by running them on our dataset. \n", "\n", "But notice there is a way of **cheating** and getting near perfect accuracy. Here's how. \n", "\n", "We look at the first entry of our data: Asuka Teramota who participates in Gymnastics and is 54 inches tall and weighs 66 pounds. We can write a rule specific to her:\n", "\n", " if athlete['Height'] == 54 and athlete['Weight'] == 66:\n", " return 'Gymnastics'\n", "\n", "Then we look at the next row of our data, Brittainey Raven and add a rule for her:\n", "\n", " # the Asuka rule\n", " if athlete['Height'] == 54 and athlete['Weight'] == 66:\n", " return 'Gymnastics'\n", " \n", " # The Brittainey rule\n", " elif athlete['Height'] == 72 and athlete['Weight'] == 162:\n", " return 'Basketball'\n", "\n", "and then add one for Chen Nan:\n", "\n", " # the Asuka rule\n", " if athlete['Height'] == 54 and athlete['Weight'] == 66:\n", " return 'Gymnastics'\n", " \n", " # The Brittainey rule\n", " elif athlete['Height'] == 72 and athlete['Weight'] == 162:\n", " return 'Basketball'\n", "\n", " # The Chen rule\n", " elif athlete['Height'] == 78 and athlete['Weight'] == 204:\n", " return 'Basketball'\n", "\n", "\n", "and so on.\n", "\n", "Now when we run our rules on the same dataset we should have 100% accuracy (or near 100%). For example, when we want to see what our prediction is for Asuka who is 54 inches tall and weighs 66 pounds, it perfectly matches (not surprisingly) rule 1 and we correctly predict *Gymnastics*. Of course if we have two athletes who are 54 inches tall anad 66 pounds -- one who is a gymnasts and the other a marathon runner -- one of our predictions will be wrong, but that will be a rare event.\n", "\n", "#### over fitting\n", "The rules we wrote overfit the data. Here's the problem in tuning the rules so precisely to fit our data. Suppose we want to classify what sport a new person plays given that she is 53 inches tall and 67 pounds. That doesn't exactly match anyone in the data so our rules fail, but our common sense tells us she is likely to participate in gymnastics. So when we overfit on the data we trained on, we may lower the performance on new -- sight unseen -- data. Here are some terms that will help us in our discussion.\n", "\n", "### Training Set\n", "The training set is the dataset that we use to train our system. In the above case we wrote the rules by hand, so the training set is the data we looked at to determine what rules to construct. Shortly, we will have our laptops come up with rules on its own. In that case we give our program a training set. The program examines and analyzes the training data and comes up with a program that will classify instances (in the above case, classify athletes by what sport they play). \n", "\n", "### Test Set\n", "The test set is a dataset that we use to test the accuracy of our system. It is used to answer the question: \"How well did my classifier work?\" \n", "\n", "In both our previous Titanic work, and our Althlete work the training set and the test set were the same. And we have seen above, this is an exceedingly bad idea. Doing this will overestimate the accuracy of our system compared to testing on new data. \n", "\n", "A better test of your prowess at writing rules would be to give you a training set and let you write rules. Then I would takes your rules and run them on a dataset you have never seen before. \n", "\n", "Dividing a dataset into a training set and a test set is common practice. For example, if we have 1,000 rows in our data we might use 900 rows for training and reserve 100 for testing. \n", "\n", "\n", "# Training a classifier\n", "\n", "We are going to give our program a training set. For example,\n", "\n", "Name | \tSport\t| Height | \tWeight\n", ":---: | :---: | :---: | :---: \n", "0\t| Asuka Teramoto\t| Gymnastics\t| 54\t| 66\n", "1\t| Brittainey Raven | \tBasketball\t| 72\t| 162\n", "2\t| Chen Nan\t| Basketball\t| 78\t| 204\n", "\n", "\n", "And ask the program to **build a classifier** that will predict the values of one column based on the values of other columns (just as you did by hand). This is step 1: building a classifier.\n", "\n", "Once the classifier is built we can use it to make predictions. And we can determine the accuracy of the classifier by running it on the test set. \n", "\n", "Again, instead of us writing the rules we are going to have our laptops do so. This is called:\n", "\n", "## machine learning\n", "\n", "Machine learning is a very large subject. This Python Notebook will introduce you to the general idea.\n", "\n", "\n", "### Step one - load the data\n", "\n", "Even though it is a small dataset let's go ahead and use the athlete dataset available at:\n", "https://raw.githubusercontent.com/zacharski/machine-learning/master/data/athletes.csv" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameSportHeightWeight
0Asuka TeramotoGymnastics5466
1Brittainey RavenBasketball72162
2Chen NanBasketball78204
3Gabby DouglasGymnastics4990
4Helalia JohannesTrack6599
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" ], "text/plain": [ " Name Sport Height Weight\n", "0 Asuka Teramoto Gymnastics 54 66\n", "1 Brittainey Raven Basketball 72 162\n", "2 Chen Nan Basketball 78 204\n", "3 Gabby Douglas Gymnastics 49 90\n", "4 Helalia Johannes Track 65 99" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "athletes = pd.read_csv('https://raw.githubusercontent.com/zacharski/machine-learning/master/data/athletes.csv')\n", "athletes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step two - divide the data into a training and a test set.\n", "\n", "Python has a library that can automatically divide data into training and test sets. Let's say we want those datasets called `athletes_train` and `athletes_test` and we want 80% of the data used for training and 20% for testing:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameSportHeightWeight
21Simone BilesGymnastics57104
26Shalane FlanaganTrack65106
16Tatyana PetrovaTrack63108
13Rene KalmerTrack70108
6Jennifer LacyBasketball75175
12Rebecca TunneyGymnastics5877
2Chen NanBasketball78204
10Nikki BlueBasketball68163
3Gabby DouglasGymnastics4990
24Seimone AugustusBasketball72166
25Desiree LindenTrack6197
11Qiushuang HuangGymnastics6195
22Madison KocianGymnastics62101
14Shanna CrossleyBasketball70155
9Nakia SanfordBasketball76200
0Asuka TeramotoGymnastics5466
23Elena Delle DonneBasketball77188
18Valeria StraneoTrack6697
20Amy CraggTrack6499
5Irina MiketenkoTrack63106
8Linlin DengGymnastics5468
27Laurie HernandezGymnastics60106
\n", "
" ], "text/plain": [ " Name Sport Height Weight\n", "21 Simone Biles Gymnastics 57 104\n", "26 Shalane Flanagan Track 65 106\n", "16 Tatyana Petrova Track 63 108\n", "13 Rene Kalmer Track 70 108\n", "6 Jennifer Lacy Basketball 75 175\n", "12 Rebecca Tunney Gymnastics 58 77\n", "2 Chen Nan Basketball 78 204\n", "10 Nikki Blue Basketball 68 163\n", "3 Gabby Douglas Gymnastics 49 90\n", "24 Seimone Augustus Basketball 72 166\n", "25 Desiree Linden Track 61 97\n", "11 Qiushuang Huang Gymnastics 61 95\n", "22 Madison Kocian Gymnastics 62 101\n", "14 Shanna Crossley Basketball 70 155\n", "9 Nakia Sanford Basketball 76 200\n", "0 Asuka Teramoto Gymnastics 54 66\n", "23 Elena Delle Donne Basketball 77 188\n", "18 Valeria Straneo Track 66 97\n", "20 Amy Cragg Track 64 99\n", "5 Irina Miketenko Track 63 106\n", "8 Linlin Deng Gymnastics 54 68\n", "27 Laurie Hernandez Gymnastics 60 106" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "athletes_train, athletes_test = train_test_split(athletes, test_size = 0.2)\n", "athletes_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we have the function `train_test_split` which takes the name of the original dataset as an argument as well as a `test_size` parameter specifying 20% of the data will be used for testing. It randomly selects rows of the original dataset so 80% are in the training data and 20% in the testing and returns these two dataframes. We assign the variables names `athletes_train` and `athletes_test` to these dataframes.\n", "\n", "### Step three - create a decision tree classifer\n", "Again, in other courses we can learn about the different kinds of classifiers, what settings they take, and how they actually work, but for now let's treat it as magic -- as a black box. Let's create and train a Decision Tree Classifier.\n", "\n", "First create the classifier:\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import tree\n", "clf = tree.DecisionTreeClassifier(criterion='entropy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4 - Train the classifier on the training data\n", "We do this with the fit method, which takes two arguments. The first is a dataframe of the columns of the data that we want to use to make the prediction. The second is the column data we want to predict. In our case we want to use height and weight to predict sport. So our `athletes_train` dataframe looks like:\n", "\n", "\n", "index| Name | \tSport\t| Height | \tWeight\n", ":---: | :---: | :---: | :---: \n", "21\t| Simone Biles\t| Gymnastics\t| 57\t| 104\n", "26\t| Shalane Flanagan | \tTrack\t| 65\t| 106\n", "16\t| Tatyana Petrova\t| Track\t| 63\t| 108\n", "13\t| Rene Kalmer\t| Track\t| 70\t| 108\n", "6\t| Jennifer Lacy\t| Basketball\t| 75\t| 175\n", "12\t| Rebecca Tunney | \tGymnastics\t| 58\t| 77\n", "2\t| Chen Nan\t| Basketball\t| 78\t| 204\n", "\n", "\n", "and we want to divide it up so that the first argument given to `fit` is\n", "\n", "index| Height | \tWeight\n", ":---: | :---: | :---: \n", "21\t| 57\t| 104\n", "26\t\t| 65\t| 106\n", "16\t| 63\t| 108\n", "13\t| 70\t| 108\n", "6\t| 75\t| 175\n", "12\t| 58\t| 77\n", "2\t| 78\t| 204\n", "\n", "This is just the columns we want to use for our rules. These are commonly called **features** and in this specific case we can get them using the Pandas expression:\n", "\n", " athletes_train[['Height', 'Weight']]\n", " \n", "And the second argument to fit is just the column that contains the values we want to predict. These are often called the **labels**.\n", "\n", "index| \tSport\t\n", ":---: | :---: \n", "21\t| Gymnastics\t\n", "26\t| \tTrack\t\n", "16\t| Track\t\n", "13\t| Track\t\n", "6\t| Basketball\t\n", "12\t| \tGymnastics\t\n", "2\t| Basketball\t\n", "\n", "\n", "We can get this column using:\n", "\n", " athletes_train['Sport']\n", " \n", "Let's put this altogether and train our classifier using `fit`:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n", " max_features=None, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(athletes_train[['Height', 'Weight']], athletes_train['Sport'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit method trains the classifier. The Decision Tree Classifier is sort of like a 20-questions type classifier. It creates a set of yes-no questions much like we do when we play 20-questions. \n", "\n", "* *Does it have horns?*\n", "* *Is it bigger than a dog?*\n", "\n", "Looking inside a classifier to see the questions requires you to install several packages on your laptop so I will just show you what it looks like:\n", "\n", "\n", "\n", "So the first question is: *Does the person weigh less than or equal to 139?*\n", "\n", "If the answer is *no* we predict basketball player.\n", "\n", "If the answer is *yes* we ask if the height is less than or equal to 62.5. If it isn't, we guess track. and so on.\n", "\n", "If you want to install the packages, I provide the code to generate the nice picture here:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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JQ+8Lr7AMoT5sFAivIS8NmKopAv5CQAmVv5DVeRUBRcAvCAR7zgs8NRCiRH4REtiRSwQv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0qm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NlDLpkxZX1oYSEJH+rgqGeH3CEId0J2oXnz5pKgGR3cyAUQyUGDBtEXX3whX8jpggI7Nkwg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\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"text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image \n", "import pydotplus\n", "dot_data = tree.export_graphviz(clf, out_file=\"iris.dot\", \n", " feature_names=['Height', 'Weight'], \n", " class_names=['Basketball', 'Gymnastics', 'Track'], \n", " filled=True, rounded=True, \n", " special_characters=True) \n", "graph = pydotplus.graphviz.graph_from_dot_file(\"iris.dot\")\n", "#graph = pydotplus.graph_from_dot_data(dot_data)\n", "Image(graph.create_png()) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5. Make predictions\n", "Now that we trained the classifier we can use it to make predictions using the `predict` method" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Track', 'Gymnastics', 'Gymnastics', 'Track', 'Gymnastics',\n", " 'Basketball'], dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "athlete_predictions = clf.predict(athletes_test[['Height', 'Weight']])\n", "athlete_predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So our classifier predicts:\n", "\n", " prediction\n", " -----\n", " Track \n", " Gymnastics \n", " Gymnastics\n", " Track\n", " Gymnastics\n", " Basketball\n", " \n", "and the real values are:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameSportHeightWeight
5Irina MiketenkoTrack63106
8Linlin DengGymnastics5468
3Gabby DouglasGymnastics4990
18Valeria StraneoTrack6697
19Viktoria KomovaGymnastics6176
14Shanna CrossleyBasketball70155
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" ], "text/plain": [ " Name Sport Height Weight\n", "5 Irina Miketenko Track 63 106\n", "8 Linlin Deng Gymnastics 54 68\n", "3 Gabby Douglas Gymnastics 49 90\n", "18 Valeria Straneo Track 66 97\n", "19 Viktoria Komova Gymnastics 61 76\n", "14 Shanna Crossley Basketball 70 155" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "athletes_test\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6 Compute Accuracy\n", "\n", "It is easy to eyeball our accuracy when we have so few examples (in this case we are 100% accurate). When we have larger datasets we can use a library that helps us compute accuracy. `accuracy_score` takes two arguments:\n", " \n", "* our list of predictions\n", "* the list of actual values\n", "\n", "and outputs the accuracy." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import accuracy_score\n", "accuracy_score(athletes_test['Sport'], athlete_predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we are 100% accurate.\n", "\n", "\n", "Let's try this using a new dataset\n", "\n", "## Iris Dataset 500 xp\n", "\n", "\n", "\n", " We are going to use an Iris Dataset. The data set contains 3 classes of Irises. Each class has 50 instances each\n", "\n", "1. Iris Setosa \n", "2. Iris Versicolour \n", "3. Iris Virginica (the picture above)\n", "\n", "There are only 4 attributes or features:\n", "\n", "1. sepal length in cm \n", "2. sepal width in cm \n", "3. petal length in cm \n", "4. petal width in cm \n", "\n", "Here is an example of the data:\n", "\n", "Sepal Length|Sepal Width|Petal Length|Petal Width|Class\n", ":--: | :--: |:--: |:--: |:--: \n", "5.3|3.7|1.5|0.2|Iris-setosa\n", "5.0|3.3|1.4|0.2|Iris-setosa\n", "5.0|2.0|3.5|1.0|Iris-versicolor\n", "5.9|3.0|4.2|1.5|Iris-versicolor\n", "6.3|3.4|5.6|2.4|Iris-virginica\n", "6.4|3.1|5.5|1.8|Iris-virginica\n", "\n", "The job of the classifier is to determine the class of an instance from the values of the features.\n", "\n", "\n", "### Step One: Load the data\n", "\n", " https://raw.githubusercontent.com/zacharski/machine-learning/master/data/iris.csv\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step Two - divide the data into a training and a test set.\n", "Let's use 20% of the data for the test set." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step Three - create a decision tree classifier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step Four - Train the classifier on the training data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5. Make predictions on the test data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6 Compute Accuracy" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How did you do? \n", "\n", "## Pima Indians Diabetes Dataset 750 xp\n", "\n", "\n", "\n", "It is time to look at a new dataset, the Pima Indians Diabetes Data Set developed by the\n", "United States National Institute of Diabetes and Digestive and Kidney Diseases. \n", "\n", "Astonishingly, over 30% of Pima people develop diabetes. In contrast, the diabetes rate in\n", "the United States is 8.3% and in China it is 4.2%.\n", "\n", "Each instance in the dataset represents information about a Pima woman over the age of 21\n", "and belonged to one of two classes: a person who developed diabetes within five years, or a\n", "person that did not. There are eight attributes in addition to the column representing whether or not they developed diabetes:\n", "\n", "\n", "1. The number of times the woman was pregnant\n", "2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test \n", "3. Diastolic blood pressure (mm Hg)\n", "4. Triceps skin fold thickness (mm) \n", "5. 2-Hour serum insulin (mu U/ml) \n", "6. Body mass index (weight in kg/(height in m)^2) \n", "7. Diabetes pedigree function \n", "8. Age\n", "9. Whether they got diabetes or not (0 = no, 1 = yes)\n", "\n", "The csv file at is at\n", "\n", " https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\n", " \n", "This file does not have a header row\n", "\n", "You will need to \n", "\n", "1. load the file into a dataframe\n", "2. divide the data into training and test sets. (an 80-20 split sounds good)\n", "3. train a decision tree classifier on the training data\n", "3. run the classifier on the test data\n", "4. compute the accuracy\n", "\n", "\n", "### hint\n", "The file does not contain a header row. Suppose I create a list of column names and then use that when I read my csv file:\n", "\n", "\n", " columns = ['pregnant', 'glucose', 'bloodpressure', 'triceps_skin', 'insulin', 'bmi', 'pedigree', 'age', 'diabetes']\n", " diabetes = pd.read_csv('pima.datafile', header=None, names=columns)\n", "\n", "What's cool is that I can use that `columns` variable when I want use `fit` and `predict`. Recall that fit takes two arguments. The first is all the data columns I want to use to make the prediction (in this case all the columns except *diabetes*. The second argument is the values I want to predict (in this case *diabetes*. Here is how I can use my column variable:\n", "\n", " clf.fit(dia_train[columns[:-1]], dia_train[columns[-1]])\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "#TBD\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TBD\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predicting the Party of Members of The House Of Representatives 750 xp\n", "\n", "\n", "\n", "Using the \n", "1984 Congressional Voting Records Data Set can we create a classifier that decides whether someone is a Democrat or Republican based on their votes on 16 key votes?\n", "\n", "I'll help you get started." ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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partyhandicapped-infantswater-project-cost-sharingadoption-of-the-budget-resolutionphysician-fee-freezeel-salvador-aidreligious-groups-in-schoolsanti-satellite-test-banaid-to-nicaraguan-contrasmx-missileimmigrationsynfuels-corporation-cutbackeducation-spendingsuperfund-right-to-suecrimeduty-free-exportsexport-administration-act-south-africa
0republicannynyyynnny?yyyny
1republicannynyyynnnnnyyyn?
2democrat?yy?yynnnnynyynn
3democratnyyn?ynnnnynynny
4democratyyynyynnnny?yyyy
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" ], "text/plain": [ " party handicapped-infants water-project-cost-sharing \\\n", "0 republican n y \n", "1 republican n y \n", "2 democrat ? y \n", "3 democrat n y \n", "4 democrat y y \n", "\n", " adoption-of-the-budget-resolution physician-fee-freeze el-salvador-aid \\\n", "0 n y y \n", "1 n y y \n", "2 y ? y \n", "3 y n ? \n", "4 y n y \n", "\n", " religious-groups-in-schools anti-satellite-test-ban \\\n", "0 y n \n", "1 y n \n", "2 y n \n", "3 y n \n", "4 y n \n", "\n", " aid-to-nicaraguan-contras mx-missile immigration \\\n", "0 n n y \n", "1 n n n \n", "2 n n n \n", "3 n n n \n", "4 n n n \n", "\n", " synfuels-corporation-cutback education-spending superfund-right-to-sue \\\n", "0 ? y y \n", "1 n y y \n", "2 y n y \n", "3 y n y \n", "4 y ? y \n", "\n", " crime duty-free-exports export-administration-act-south-africa \n", "0 y n y \n", "1 y n ? \n", "2 y n n \n", "3 n n y \n", "4 y y y " ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columnNames = ['party', 'handicapped-infants', 'water-project-cost-sharing', 'adoption-of-the-budget-resolution', 'physician-fee-freeze', 'el-salvador-aid', 'religious-groups-in-schools', 'anti-satellite-test-ban', 'aid-to-nicaraguan-contras', 'mx-missile', 'immigration', 'synfuels-corporation-cutback', 'education-spending', 'superfund-right-to-sue', 'crime' , 'duty-free-exports', 'export-administration-act-south-africa']\n", "datafile = 'https://archive.ics.uci.edu/ml/machine-learning-databases/voting-records/house-votes-84.data' \n", "votes = pd.read_csv(datafile, header = None, names=columnNames)\n", "votes.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the data has an *n* in a cell when that person votes no on that bill, a *y* for yes and a *?* when the person didn't vote. Let's divide the data into training and test sets and then train the classifier: " ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "could not convert string to float: 'y'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\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[0mvtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvotes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m 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"metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " party handicapped-infants water-project-cost-sharing \\\n", "0 1 1 2 \n", "1 1 1 2 \n", "2 0 0 2 \n", "3 0 1 2 \n", "4 0 2 2 \n", "\n", " adoption-of-the-budget-resolution physician-fee-freeze el-salvador-aid \\\n", "0 1 2 2 \n", "1 1 2 2 \n", "2 2 0 2 \n", "3 2 1 0 \n", "4 2 1 2 \n", "\n", " religious-groups-in-schools anti-satellite-test-ban \\\n", "0 2 1 \n", "1 2 1 \n", "2 2 1 \n", "3 2 1 \n", "4 2 1 \n", "\n", " aid-to-nicaraguan-contras mx-missile immigration \\\n", "0 1 1 2 \n", "1 1 1 1 \n", "2 1 1 1 \n", "3 1 1 1 \n", "4 1 1 1 \n", "\n", " synfuels-corporation-cutback education-spending superfund-right-to-sue \\\n", "0 0 2 2 \n", "1 1 2 2 \n", "2 2 1 2 \n", "3 2 1 2 \n", "4 2 0 2 \n", "\n", " crime duty-free-exports export-administration-act-south-africa \n", "0 2 1 2 \n", "1 2 1 0 \n", "2 2 1 1 \n", "3 1 1 2 \n", "4 2 2 2 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import preprocessing\n", "le = preprocessing.LabelEncoder()\n", "votes2 = votes.apply(le.fit_transform)\n", "votes2.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now its your turn\n", "With that introduction you should be able to finish this task on your own." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The 115th Congress 2000 xp\n", "\n", "\n", "We just worked on the 1984 congress. Now let's move to the 2017 Congress. Do you think your accuracy will be better or worse?\n", "\n", "We will look at how House members voted on the following bills:\n", "* H.J.Res. 38: Disapproving the rule submitted by the Department of the Interior known as the Stream Protection Rule.\n", "* H.R. 36: Pain-Capable Unborn Child Protection Act\n", "* H.R. 1177: Removing Outdated Restrictions to Allow for Job Growth Act\n", "* H.R. 1628: American Health Care Act of 2017\n", "* H.Res. 74: Providing for consideration of the joint resolution (H.J. Res. 36) providing for congressional disapproval under chapter 8 of title 5, United States Code, of the final rule of the Bureau of Land Management relating to “Waste Prevention, Produc\n", "* H.R. 1430: HONEST Act\n", "* H.R. 3004: Kate’s Law\n", "* H.R. 2521: South Carolina Peanut Parity Act of 2017\n", "* H.R. 3441: Save Local Business Act\n", "* H.R. 3043: Hydropower Policy Modernization Act of 2017\n", "* H.R. 2201: Micro Offering Safe Harbor Act\n", "\n", "I downloaded the data from [GovTrack](https://www.govtrack.us/congress/votes). The vote for each bill is in a separate file. A zip file of a directory containing these files is available at [http://zacharski.org/files/courses/data101/congress2017.zip](http://zacharski.org/files/courses/data101/congress2017.zip)\n", "\n", "This task will require the skills you learned in the cleaning data course.\n", "\n", "To help you a bit, I created a Python dictionary with the names of the vote column in the respective files.\n", "\n" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vote_names = {'hr36': 'unborn_child_protection',\n", " 'hr38': 'anti_stream_protection',\n", " 'hr74': 'blm_waste',\n", " 'hr1177': 'remove_restrictions',\n", " 'hr1430': 'honest_act',\n", " 'hr1628': 'american_health_care_act',\n", " 'hr2201': 'safe_harbor', \n", " 'hr2521': 'peanut_parity_act',\n", " 'hr3004': 'kates_law',\n", " 'hr3043': 'hydropower_modernization',\n", " 'hr3441': 'save_local_business'\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### You task is to create and evaulate a classifier for the 115th congress. \n", "Can you predict who is a Democrat or Republican based on their voting history?" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#TBD" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TBD\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }