Decision tree tutorial v1.ipynb

{"nbformat_minor": 0, "cells": [{"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "# Intro to decision tree learning\n\nThis notebook is an in depth tutorial on developing decision tree models for supervised learning tasks. Decision trees are the foundation for boosted algorithms that are known for winning data science competitions.\n\n<b>For beginners:</b> follow the tutorial to learn what a decision tree is, and how to train a single decision tree model using Python's scikit-learn library.<br/>\n<b>For more advanced users:</b> skip to Tree Ensemble Methods to learn how to combine many models for more accurate predictive performance.\n\nPre-requisite knowledge: Python pandas and numpy\n\n## Table of contents\n* [Primer](#first-bullet)\n* [Terms & definitions](#second-bullet)\n* [Example with housing data](#third-bullet)\n* [Data pre-processing](#fifth-bullet)\n* [Decision tree model](#sixth-bullet)\n* [Cross-validation](#seventh-bullet)\n* [Parameter tuning](#eighth-bullet)\n* [Tree ensemble methods](#ninth-bullet)\n* [Format & output results](#tenth-bullet)"}, {"cell_type": "markdown", "metadata": {}, "source": "## Primer <a class=\"anchor\" id=\"first-bullet\"></a>\n\n   **Supervised learning** is a type of machine learning. The purpose of machine learning is to identify a mathematical function (a model) that correctly interprets features to identify a target. It is 'supervised' because we will define the possible targets or outputs of the model (instead of letting it discover inherent patterns in the data). A model is developed using a small amount of information, and model performance is measured by the accuracy of its outputs when it is given new information. \n   \n   For example, beats per minute (BPM) and number of acoustic instruments can be used to describe a genre of music. If we have data on these two features for a million songs, we can use that to create a model with the goal of identifying the genre for any song. We can make the model more precise by giving it more data (another million songs), and we can make it more accurate by giving it more features (e.g. length, lyrics). \n   \n   **Decision trees** are an abstract structure that are simple to understand graphically. The example below is a decision tree for a car purchase. Each node (ellipses) represents a decision to be made. The answer to each decision (arrows) directs you down the tree until you reach a leaf, which represents an outcome based on all the cumulative decisions. In this case, the outcomes are buy/don't buy.\n   \n### Car purchase decision tree<br/><small>(Source: IBM Knowledge Center)\n   \n<img src=\"https://www.ibm.com/support/knowledgecenter/en/SS3RA7_15.0.0/com.ibm.spss.modeler.help/images/dectree.gif\" width=\"500\" height=\"500\" align=\"left\"/>"}, {"cell_type": "markdown", "metadata": {}, "source": "## Terms & definitions <a class=\"anchor\" id=\"second-bullet\"></a>\n\n**Target**: The variable or 'output' that we wish to predict.\n \n**Features/independent variables/predictors**: is an individual measurable property of an object being observed..\n\n**Model**: A mathematical function that takes the features as inputs and outputs a prediction of the target.\n \n**Parameters**: The variables that affect model shape most significantly. For a line, the main parameters would be the slope and y-intercept.\n\n**Hyperparameters**: Parameters that control how the model learns. \n \n**Residuals**: The difference between the observed and predicted values \n \n**Training/testing set**: The available data should be split into training and testing sets. The training set is used to train the model, and the testing set is used for cross-validation (checking model performance on unseen data).\n \n**Over/under-fitting**\nOverfitting: A model that is too specifically attuned to its training set, such that it has poor predictive performance on unseen data\nUnderfitting: A model that is not specific enough such that it behaves poorly on unseen data.\n \n**Cross-validation**: Measuring the performance of a model by calculating the error of its predictions on unseen data (the validation set).\n\n**Decision tree learning**: An algorithm that picks important features from the training data to function as each decision in the tree."}, {"cell_type": "markdown", "metadata": {}, "source": "## Example with housing data <a class=\"anchor\" id=\"third-bullet\"></a>\n\nThe Ames Housing dataset was compiled by Dean De Cock for use in data science education. We will be using a subset of the data provided by Kaggle. With 79 independent variables describing (almost) every aspect of residential homes in Ames, Iowa, our challenge is to predict the final price of each home."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from io import StringIO\nimport requests\nimport json\nimport pandas as pd\n\n#@hidden_cell\n# This function accesses a file in your Object Storage. The definition contains your credentials.\n# You might want to remove those credentials before you share your notebook.\ndef get_object_storage_file_with_credentials_9faf960ba3c641fd8924a87ad848f417(container, filename):\n    \"\"\"This functions returns a StringIO object containing\n    the file content from Bluemix Object Storage.\"\"\"\n\n    url1 = ''.join(['https://identity.open.softlayer.com', '/v3/auth/tokens'])\n    data = {'auth': {'identity': {'methods': ['password'],\n            'password': {'user': {'name': 'member_937684970eeff520b9cc88e81c531974da481dab','domain': {'id': '5917e7dc71f4409ebc70906569cee575'},\n            'password': 'cR6#JYpy_?}l5=by'}}}}}\n    headers1 = {'Content-Type': 'application/json'}\n    resp1 = requests.post(url=url1, data=json.dumps(data), headers=headers1)\n    resp1_body = resp1.json()\n    for e1 in resp1_body['token']['catalog']:\n        if(e1['type']=='object-store'):\n            for e2 in e1['endpoints']:\n                        if(e2['interface']=='public'and e2['region']=='dallas'):\n                            url2 = ''.join([e2['url'],'/', container, '/', filename])\n    s_subject_token = resp1.headers['x-subject-token']\n    headers2 = {'X-Auth-Token': s_subject_token, 'accept': 'application/json'}\n    resp2 = requests.get(url=url2, headers=headers2)\n    return StringIO(resp2.text)", "execution_count": 2, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "train = pd.read_csv(get_object_storage_file_with_credentials_9faf960ba3c641fd8924a87ad848f417('Tutorialnotebooks', 'train.csv'))\ntest = pd.read_csv(get_object_storage_file_with_credentials_9faf960ba3c641fd8924a87ad848f417('Tutorialnotebooks', 'test.csv'))\ntrain.head(10)", "execution_count": 9, "outputs": [{"metadata": {}, "data": {"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Id</th>\n      <th>MSSubClass</th>\n      <th>MSZoning</th>\n      <th>LotFrontage</th>\n      <th>LotArea</th>\n      <th>Street</th>\n      <th>Alley</th>\n      <th>LotShape</th>\n      <th>LandContour</th>\n      <th>Utilities</th>\n      <th>...</th>\n      <th>PoolArea</th>\n      <th>PoolQC</th>\n      <th>Fence</th>\n      <th>MiscFeature</th>\n      <th>MiscVal</th>\n      <th>MoSold</th>\n      <th>YrSold</th>\n      <th>SaleType</th>\n      <th>SaleCondition</th>\n      <th>SalePrice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>65</td>\n      <td>8450</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>208500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>80</td>\n      <td>9600</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>5</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>181500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>68</td>\n      <td>11250</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>9</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>223500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>70</td>\n      <td>RL</td>\n      <td>60</td>\n      <td>9550</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2006</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>140000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>84</td>\n      <td>14260</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>12</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>250000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>50</td>\n      <td>RL</td>\n      <td>85</td>\n      <td>14115</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>MnPrv</td>\n      <td>Shed</td>\n      <td>700</td>\n      <td>10</td>\n      <td>2009</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>143000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>20</td>\n      <td>RL</td>\n      <td>75</td>\n      <td>10084</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>8</td>\n      <td>2007</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>307000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>60</td>\n      <td>RL</td>\n      <td>NaN</td>\n      <td>10382</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>IR1</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Shed</td>\n      <td>350</td>\n      <td>11</td>\n      <td>2009</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>200000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>50</td>\n      <td>RM</td>\n      <td>51</td>\n      <td>6120</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>4</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Abnorml</td>\n      <td>129900</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>190</td>\n      <td>RL</td>\n      <td>50</td>\n      <td>7420</td>\n      <td>Pave</td>\n      <td>NaN</td>\n      <td>Reg</td>\n      <td>Lvl</td>\n      <td>AllPub</td>\n      <td>...</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2008</td>\n      <td>WD</td>\n      <td>Normal</td>\n      <td>118000</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows \u00d7 81 columns</p>\n</div>", "text/plain": "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n0   1          60       RL           65     8450   Pave   NaN      Reg   \n1   2          20       RL           80     9600   Pave   NaN      Reg   \n2   3          60       RL           68    11250   Pave   NaN      IR1   \n3   4          70       RL           60     9550   Pave   NaN      IR1   \n4   5          60       RL           84    14260   Pave   NaN      IR1   \n5   6          50       RL           85    14115   Pave   NaN      IR1   \n6   7          20       RL           75    10084   Pave   NaN      Reg   \n7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n8   9          50       RM           51     6120   Pave   NaN      Reg   \n9  10         190       RL           50     7420   Pave   NaN      Reg   \n\n  LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n0         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n1         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n2         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n3         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n4         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n5         Lvl    AllPub    ...            0    NaN  MnPrv        Shed     700   \n6         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n7         Lvl    AllPub    ...            0    NaN    NaN        Shed     350   \n8         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n9         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n\n  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n0      2   2008        WD         Normal     208500  \n1      5   2007        WD         Normal     181500  \n2      9   2008        WD         Normal     223500  \n3      2   2006        WD        Abnorml     140000  \n4     12   2008        WD         Normal     250000  \n5     10   2009        WD         Normal     143000  \n6      8   2007        WD         Normal     307000  \n7     11   2009        WD         Normal     200000  \n8      4   2008        WD        Abnorml     129900  \n9      1   2008        WD         Normal     118000  \n\n[10 rows x 81 columns]"}, "execution_count": 9, "output_type": "execute_result"}]}, {"cell_type": "markdown", "metadata": {}, "source": "## Pre-processing <a class=\"anchor\" id=\"fifth-bullet\"></a>\nWe need to fill in missing values, transform variables that are skewed, and convert categorical variables to an appropriate numerical equivalent."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom scipy.stats import skew\nimport sklearn.linear_model  as lm\n%matplotlib inline", "execution_count": 10, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "# To make it it a little bit easier, we will combine the train and test data\nall_data = pd.concat((train.loc[:,'MSSubClass':'SaleCondition'],\n                      test.loc[:,'MSSubClass':'SaleCondition']))\n# Log transform the target:\ntrain[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\n\n# Log transform skewed numeric features:\nnumeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\nskewed_feats = train[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness\nskewed_feats = skewed_feats[skewed_feats > 0.75]\nskewed_feats = skewed_feats.index\nall_data[skewed_feats] = np.log1p(all_data[skewed_feats])\nall_data = pd.get_dummies(all_data)\n\n# Filling NA's with the mean of the column:\nall_data = all_data.fillna(all_data.mean())\nX_train = all_data[:train.shape[0]]\nX_test = all_data[train.shape[0]:]\ny = train.SalePrice\n\n\n# This is where we actually train the model with the fitted data\nmodel_lasso = lm.LassoCV(alphas = [1, 0.1, 0.001, 0.0005]).fit(X_train, y)\n\ncoef = pd.Series(model_lasso.coef_, index = X_train.columns)\nprint(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")\n# One thing to note here however is that the features selected are not necessarily the \"correct\" ones\n# especially since there are a lot of collinear features in this dataset.\n\n#This wil tell us which features in the Lasso Model will have the greatest effect on the data\nimp_coef = pd.concat([coef.sort_values().head(10),\n                     coef.sort_values().tail(10)])\nmatplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\nimp_coef.plot(kind = \"barh\")\nplt.title(\"Coefficients in the Lasso Model\")\n# As expected, the square footage of the house has the highest impact on the price\n# Furthermore, some neighborhoods have a positive impact and others have a negative inpact", "execution_count": 11, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Lasso picked 111 variables and eliminated the other 177 variables\n"}, {"metadata": {}, "data": {"text/plain": "<matplotlib.text.Text at 0x7f1563e86cd0>"}, "execution_count": 11, "output_type": "execute_result"}, {"data": {"image/png": 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BVgAmARPbuVcXp7FGpntxVUQ8L+k3wMyIuDDN7/Z2rqMlIvpJ2gK4HbhF0r7ApyJip5TJ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\nwJERsQWwD/ALSWv2Y4y9StJKwDnAvwCbAyMlfbSs29HAyxGxCfAL4Iz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"text/plain": "<matplotlib.figure.Figure at 0x7f156664ff90>"}, "output_type": "display_data", "metadata": {}}]}, {"cell_type": "markdown", "metadata": {}, "source": "## Decision tree model <a class=\"anchor\" id=\"sixth-bullet\"></a>\n**Decision Trees (DTs)** are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.\nDepending on the type of our dependent variable, decision trees can be used in both regression and classification.\nInternal nodes of a decision tree represents a series of splitting rules over observations. And, each leaf contains a prediction of  which we will make for observations falling into that region.\n\n**Decision Trees** for regression have a number of advantages:\n\n1. Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!\n\n2. Decision trees more closely mirror human decision-making than do the classical regression and classification approaches.\n\n3. Trees can be displayed graphically.\n\n4. Trees can easily handle categorical predictors without the need to create dummy variables.\n\nIn python, we can use the **tree** class to create a decision tree."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn import tree\n#here we set the two paramters max_depth and min_samples_leaf to 3 and 50 respectively\n#We will explain them in following section\nregr = tree.DecisionTreeRegressor(max_depth = 3,min_samples_leaf=50)\nregr.fit(X_train,y)\n\n#run prediction on training set to get a rough idea on how well it does\ny_pred = regr.predict(X_train)", "execution_count": 16, "outputs": []}, {"cell_type": "markdown", "metadata": {}, "source": "To measure how well our decision tree model fit on our training set, we use root-mean-squared error (**RMSE**), which is the square root of the mean/average of the square of all the error."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn.metrics import mean_squared_error\ndef rmse(y_true, y_pred):\n    return np.sqrt(mean_squared_error(y_true, y_pred))\n# note that both y(actual observed values) and y_pred (our prediction on training set) are log-transformed values\nprint \"Regression Tree score on training set: \"+str(rmse(y, y_pred))", "execution_count": 17, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Regression Tree score on training set: 0.214835469676\n"}]}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from IPython.display import Image  \nimport pydotplus\ndot_data = tree.export_graphviz(regr, out_file=None, feature_names = X_train.columns.values ,filled=True)  \ngraph = pydotplus.graph_from_dot_data(dot_data)  \n#Image(graph.create_png())", "execution_count": 18, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "Image(graph.create_png())", "execution_count": 19, "outputs": [{"metadata": {}, "data": {"image/png": 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uUbHxJ9w8TrpfMTHUX7t4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u6p+6aHUamOp/xpLa1OCybtWjYDALyDo5w8Ak87zrOdNlzU6nJwlPOCyVQy4ci1J+cCwyaa9xT1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"text/plain": "<IPython.core.display.Image object>"}, "execution_count": 19, "output_type": "execute_result"}]}, {"cell_type": "markdown", "metadata": {}, "source": "## Interpretation\n\nAs we can see from the decision tree above, OverallQual (abbr for overall quality of a house) appears to be the most significant factor on house prices. On the top of the tree, it splits the data into two groups, and houses falling into the first group will be expected to have a lower sale price. Besides, there are two more splits based on OverallQual. GrLivArea (abbr for ground living area) makes one less split than OverallQual, and it is also true that houses with higher GrLivArea are going to be sold at higher price. All of these results make a lot of sense because living area and overall quality are the things which will first come to our mind when purchasing a house."}, {"cell_type": "markdown", "metadata": {}, "source": "## Cross-Validation <a class=\"anchor\" id=\"seventh-bullet\"></a>\nThe **test error** is the average error that results from using a statistical learning method to predict the value on a new observation\u2014\nthat is, a measurement that was not used in training the method. \nTo avoid **overfitting**, we need to figure out some ways to estimate the test error of our model.Cross-Validation represents a class of methods that estimate the test error by \"holding out\" a subset of the training observations from the fitting process, and then applying the statistical learning method to those held out observations.\n\nIn this tutorial, we are going to use **k-fold CV**.This approach involves randomly dividing the set of observations into k groups, or folds, of approximately equal size. The first fold is treated as a validation set, and the method is fit on the remaining k \u2212 1 folds."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn.model_selection import KFold\n# this is a wrapper to set up the Cross-validation\ndef cv_model(model, x, y ,n_splits):\n  \n   \n    kf = KFold(n_splits, shuffle=False)\n    idx = 0\n    rmse_buf = np.empty(n_splits)\n    for train, test in kf.split(x):\n        model.fit(x.iloc[train], y.iloc[train])\n        y_cv = model.predict(x.iloc[test])\n        rmse_buf[idx] = rmse(y.iloc[test], y_cv)\n        idx += 1\n\n    mean_rmse = np.mean(rmse_buf)\n    return mean_rmse", "execution_count": 20, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "#perform cross-validation on the regression tree model\npred_score = cv_model(regr,X_train,y,5)\nprint \"Estimated test error rate: \" + str(pred_score)", "execution_count": 21, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Estimated test error rate: 0.229729766552\n"}]}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "## Parameter Tuning - Grid Search <a class=\"anchor\" id=\"eighth-bullet\"></a>\nwe may conclude that our decision tree model overfits on the training set a little bit since the value above is quite large.\nUsually, decision trees do not have the same level of predictive accuracy as some of the other regression models. An explanation for this\nis that the resulting tree might be too complex or too simple.\n\nThere are two parameters controlling the complexity of a regression tree:\n\n**max_depth**:The maximum depth of the tree.\n\n**min_samples_leaf**:The minimum number of observations required to be at a leaf node.\n\nIn case where we have very few ideas on what are the best values for these parameters, we should do **grid search**.**Grid search** means given a set of models (which differ from each other in their parameter values), we train each of the models and evaluate it using cross-validation. The one that performs best in cross-validation is then selected."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn.model_selection import GridSearchCV\n#first, we need to build a regressor\nregr = tree.DecisionTreeRegressor()\n\n##Second, specify all candidates for parameters\nparam_list = {\"max_depth\": [3, 6,12,16,20],\n              \"min_samples_leaf\": [1,2,6,12,20,30]}\n\n# run Grid search\ngrid_search = GridSearchCV(regr, param_list, n_jobs=1, cv=5)\ngrid_search.fit(X_train, y)", "execution_count": 16, "outputs": [{"metadata": {}, "data": {"text/plain": "GridSearchCV(cv=5, error_score='raise',\n       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n           max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n           splitter='best'),\n       fit_params={}, iid=True, n_jobs=1,\n       param_grid={'max_depth': [3, 6, 12, 16, 20], 'min_samples_leaf': [1, 2, 6, 12, 20, 30]},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n       scoring=None, verbose=0)"}, "execution_count": 16, "output_type": "execute_result"}]}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "grid_search.best_params_", "execution_count": 17, "outputs": [{"metadata": {}, "data": {"text/plain": "{'max_depth': 16, 'min_samples_leaf': 12}"}, "execution_count": 17, "output_type": "execute_result"}]}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "##check the model (regression tree with max_depth = 16 and min_samples_leaf=12) returned from grid search\nregr = tree.DecisionTreeRegressor(max_depth = 16, min_samples_leaf = 12)\npred_score = cv_model(regr,X_train,y,5)\nprint \"Estimated test error rate: \" + str(pred_score)", "execution_count": 18, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Estimated test error rate: 0.183875638146\n"}]}, {"cell_type": "markdown", "metadata": {}, "source": "## Tree Ensemble Methods <a class=\"anchor\" id=\"ninth-bullet\"></a>\nAs we can see, there is an improvement (from 0.229729766 to 0.1839001).It is still too inaccurate once we recall the fact that we need to take exponential on the prediction to recover the sale prices.However, by aggregating many decision trees, using methods like bagging,random forests and boosting, the predictive performance of trees can be substantially improved.\n\n### Bagging\nRegression trees suffer from **high variance**.This means that if we split the training data into two parts at random,and fit a decision tree to both halves, the results that we get could be quite different. In contrast, a procedure with low variance will yield similar results if applied repeatedly to distinct data sets.**Bootstrap aggregation**, or **bagging**, is a general-purpose procedure for reducing the variance of a statistical learning method. \nA natural way to achieve this and hence increase the prediction accuracy of a statistical learning method is to take many training sets from the population, build a separate prediction model using each training set, and average the resulting predictions.\n\n### Random forests\nAs in bagging, we build a number of decision trees on bootstrapped training samples. But when building these decision trees, each time a split in a tree is considered, a random sample of m features is chosen as split candidates from the full set of p features. The split is allowed to use only one of those m features.\n\nIn other words, in building a random forest, at each split in the tree, the algorithm is not even allowed to consider a majority of the available features.There is a very clever rationale behind that method. Suppose that there is one very strong feature in the data set, along with a number of other moderately strong features. Then in the collection of bagged trees, most or all of the trees will use this strong feature in the top split. Consequently, all of the bagged trees will look quite similar to each other. Hence the predictions from the bagged trees will be highly correlated. \n\n### Boosting\nBoosting is another approach for improving the predictions resulting from a decision tree. It is a general approach that can be applied to many statistical learning methods for both regression and classification. Here, we restrict our discussion of boosting to the context of decision trees.\n\nBoosting works in a similar way as bagging, except that the trees are grown sequentially rather than simultaneously: each tree is grown using information from previously constructed regression trees. In addition, boosting does not involve bootstrap sampling; however, each tree is fit on a modified version of the original data set. The core principle is to fit a sequence of weak learners (i.e., models that are only slightly better than random guessing, such as small decision trees) on repeatedly modified versions of the data.\n\n\n"}, {"cell_type": "markdown", "metadata": {}, "source": "### hyperparameters for random forest regressor:\n\n**n_estimators**: The number of trees in the forest.\n\n**max_depth**: The maximum depth of each tree in the forest.\n\n**min_sample_leaf**: The minimum number of observations required to be at a leaf node\n\n### hyperparameters for extreme gradient boosting regressor (an implementation of the idea of boosting):\n\n**learning_rate**:This determines the impact of each tree on the final outcome.\n\n**n_estimators**: The same as random forest.\n\n**max_depth**: The same as random forest.\n\n**min_child_weight**: Defines the minimum sum of weights of all observations required in a child.\n\n**subsample**: The fraction of observations to be selected for each tree."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn.ensemble import RandomForestRegressor\n\n#first, build a random forest regressor \nrf_regr = RandomForestRegressor(max_features = 0.2)\n\n#From now on, we will proceed directly to grid search\n#candidates for parameter values on random forest regressor\nrf_params = {\n    'n_estimators': [1000,1200],\n    'max_depth': [12,18],\n    'min_samples_leaf': [2,6]\n}\n\n\n#do grid search on random forest regressors\ngsearch_rf = GridSearchCV(estimator = rf_regr,param_grid = rf_params,n_jobs=1, cv=5)\ngsearch_rf.fit(X_train,y)\n", "execution_count": null, "outputs": [{"metadata": {}, "data": {"text/plain": "GridSearchCV(cv=5, error_score='raise',\n       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features=0.2, max_leaf_nodes=None, min_impurity_split=1e-07,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n           oob_score=False, random_state=None, verbose=0, warm_start=False),\n       fit_params={}, iid=True, n_jobs=1,\n       param_grid={'n_estimators': [1000, 1200], 'max_depth': [12, 18], 'min_samples_leaf': [2, 6]},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n       scoring=None, verbose=0)"}, "execution_count": 19, "output_type": "execute_result"}]}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "gsearch_rf.best_params_", "execution_count": null, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "import xgboost as xgb\n\n#The value of n_estimators is initialized since it is quite time-consuming to do grid search on that parameter\n#Adapt the parameter values from the grid search I have done previously\nxgb_regr = xgb.XGBRegressor(n_estimators = 1000,\n                            learning_rate = 0.05,\n                            max_depth = 6,\n                            min_child_weight = 1.5,\n                            subsample = 0.2)", "execution_count": 22, "outputs": []}, {"cell_type": "markdown", "metadata": {}, "source": "Next, we perform cross-validation on the models obtained through grid search above to check their prediction accuracy."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "\nrf_regr  = RandomForestRegressor(max_features = 0.2,max_depth = 18 ,min_samples_leaf = 2, n_estimators = 1000)\npred_score_rf = cv_model(rf_regr,X_train,y,5)\nprint \"Estimated test error rate of Random Forest Regressor: \" + str(pred_score_rf)\npred_score_xgb = cv_model(xgb_regr,X_train,y,5)\nprint \"Estimated test error rate of extreme gradient boosting Regressor: \" + str(pred_score_xgb)", "execution_count": 23, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Estimated test error rate of Random Forest Regressor: 0.139434942582\nEstimated test error rate of extreme gradient boosting Regressor: 0.129978046026\n"}]}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "Clearly, both of them are much better than the decision tree model which has an estimated test error about 0.18390012748595302.\n\nExtreme gradient boosting method (0.129978) surpasses random forest method (0.13943)\n\nNext, we do **bagging** on extreme gradient boosting to further decrease the error."}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "from sklearn.ensemble import BaggingRegressor\nxgb_bagg_regr = BaggingRegressor(xgb_regr , n_estimators = 100, max_samples = 0.95)\npred_score_xgb_bagged = cv_model(xgb_bagg_regr,X_train,y,5)", "execution_count": 24, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "print \"Estimated test error rate of Extreme Gradient Boosting after Bagging: \" + str(pred_score_xgb_bagged)", "execution_count": 25, "outputs": [{"name": "stdout", "output_type": "stream", "text": "Estimated test error rate of Extreme Gradient Boosting after Bagging: 0.120714246233\n"}]}, {"cell_type": "markdown", "metadata": {}, "source": "there is an apparent improvement on test error after **bootstrap aggregation** (0.129978046 -> 0.120714246233)"}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "xgb_bagg_regr.fit(X_train , y)\ny_pred = xgb_bagg_regr.predict(X_test)\n#recover sale prices by taking exponential \ny_pred = np.exp(y_pred)", "execution_count": 26, "outputs": []}, {"cell_type": "markdown", "metadata": {}, "source": "## Format & output results <a class=\"anchor\" id=\"tenth-bullet\"></a>"}, {"cell_type": "code", "metadata": {"collapsed": true}, "source": "pred_df = pd.DataFrame(y_pred, index=test[\"Id\"], columns=[\"SalePrice\"])\npred_df.to_csv('output.csv', header=True, index_label='Id')", "execution_count": null, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": true}, "source": "## a convenient wrapper one can reuse to output csv file to object storage\ndef put_file(credentials, local_file_name):  \n    \"\"\"This functions returns a StringIO object containing\n    the file content from Bluemix Object Storage V3.\"\"\"\n    f = open(local_file_name,'r')\n    my_data = f.read()\n    url1 = ''.join(['https://identity.open.softlayer.com', '/v3/auth/tokens'])\n    data = {'auth': {'identity': {'methods': ['password'],\n            'password': {'user': {'name': credentials['username'],'domain': {'id': credentials['domain_id']},\n            'password': credentials['password']}}}}}\n    headers1 = {'Content-Type': 'application/json'}\n    resp1 = requests.post(url=url1, data=json.dumps(data), headers=headers1)\n    resp1_body = resp1.json()\n    for e1 in resp1_body['token']['catalog']:\n        if(e1['type']=='object-store'):\n            for e2 in e1['endpoints']:\n                        if(e2['interface']=='public'and e2['region']=='dallas'):\n                            url2 = ''.join([e2['url'],'/', credentials['container'], '/', local_file_name])\n    s_subject_token = resp1.headers['x-subject-token']\n    headers2 = {'X-Auth-Token': s_subject_token, 'accept': 'application/json'}\n    resp2 = requests.put(url=url2, headers=headers2, data = my_data )\n    print(resp2)", "execution_count": null, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": false}, "source": "@hidden_cell\ncredentials_7 = {\n  'auth_url':'https://identity.open.softlayer.com',\n  'project':'object_storage_6f82dfd7_eaa9_4543_8709_4d9394031ece',\n  'project_id':'ec46c12ebb1a42c4bbf9fe2985ef8658',\n  'region':'dallas',\n  'user_id':'0f5dce84c4134fc081197d46c9643f6f',\n  'domain_id':'144b7dfe4b134930aabd09d6be006368',\n  'domain_name':'1184035',\n  'username':'member_ed921c62764bc5e58885e02b58601a2a1cba3cbc',\n  'password':\"\"\"MH4Yx2dI,cY7&{po\"\"\",\n  'container':'HousePrices',\n  'tenantId':'undefined',\n  'filename':'output.csv'\n}\nput_file(credentials_7,'output.csv')", "execution_count": null, "outputs": []}, {"cell_type": "code", "metadata": {"collapsed": true}, "source": "", "execution_count": null, "outputs": []}], "metadata": {"kernelspec": {"display_name": "Python 2 with Spark 2.0", "language": "python", "name": "python2-spark20"}, "language_info": {"codemirror_mode": {"version": 2, "name": "ipython"}, "version": "2.7.11", "name": "python", "file_extension": ".py", "nbconvert_exporter": "python", "mimetype": "text/x-python", "pygments_lexer": "ipython2"}}, "nbformat": 4}