Usecase clustering

src/use_case_classification.Rmd

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+# Use Cases
+
+## Classification
+
+One popular example regarding a classification task is the "Titanic" showcase. We have different passenger information - like name, age or fare - available with the aim to predict which kind of people would have survived the titanic sinking.
+
+Therefore we load the titanic dataset and other libraries that are needed for this use case.
+
+```{r, results='hide', message=FALSE, warning=FALSE}
+library(titanic)
+library(mlr)
+library(BBmisc)
+```
+
+
+```{r}
+data = titanic_train
+head(data)
+```
+
+Our aim - as mentioned before - is to predict which kind of people would have survided.
+
+Therefore we will work off the following steps:
+
+* preprocessing, [here](http://mlr-org.github.io/mlr-tutorial/devel/html/preproc/index.html) and [here](http://mlr-org.github.io/mlr-tutorial/devel/html/impute/index.html)
+* create a task,[here](http://mlr-org.github.io/mlr-tutorial/devel/html/task/index.html)
+* provide a learner, [here](http://mlr-org.github.io/mlr-tutorial/devel/html/learner/index.html)
+* train the model, [here](http://mlr-org.github.io/mlr-tutorial/devel/html/train/index.html)
+* predict the survival chance, [here](http://mlr-org.github.io/mlr-tutorial/devel/html/predict/index.html)
+* validate the model,[here](http://mlr-org.github.io/mlr-tutorial/devel/html/performance/index.html) and [here](http://mlr-org.github.io/mlr-tutorial/devel/html/resample/index.html)
+
+#### Preprocessing
+
+The data set is corrected regarding their data types.
+
+```{r}
+data[, c("Survived", "Pclass", "Sex", "SibSp", "Embarked")] = lapply(data[,  c("Survived", "Pclass", "Sex", "SibSp", "Embarked")], as.factor)
+```
+
+Next, unuseful columns will be dropped.
+
+```{r}
+data = dropNamed(data, c("Cabin","PassengerId", "Ticket", "Name"))
+```
+
+And missing values will be imputed, in this case Age and Fare.
+
+```{r}
+data$Embarked[data$Embarked == ""] = NA
+data$Embarked = droplevels(data$Embarked)
+data = impute(data, cols = list(Age = imputeMedian(), Fare = imputeMedian(), Embarked = imputeMode()))
+data = data$data
+```
+
+### Create a task
+
+In the "task" the data set and the target column is specified. People who survived are labelled with "1".
+
+```{r}
+task = makeClassifTask(data = data, target = "Survived", positive = "1")
+```
+
+### Define a learner
+
+A classification learner is selected. You can find an overview of all learners [here](http://mlr-org.github.io/mlr-tutorial/devel/html/integrated_learners/index.html)
+
+```{r}
+lrn = makeLearner("classif.randomForest", predict.type = "prob")
+```
+
+### Fit the model
+
+To fit the model - and afterwards predict - the data set is split into a training and a test data set.
+
+```{r}
+n = getTaskSize(task)
+trainSet = seq(1, n, by = 2)
+testSet = seq(2, n, by = 2)
+```
+
+```{r}
+mod = train(learner = lrn, task = task, subset = trainSet)
+```
+
+### Predict
+
+Predicting the target values for new observations is implemented the same way as most of the other predict methods in R. In general, all you need to do is call predict on the object returned by train and pass the data you want predictions for.
+
+```{r}
+pred = predict(mod, task, subset = testSet)
+```
+
+The quality of the predictions of a model in mlr can be assessed with respect to a number of different performance measures. In order to calculate the performance measures, call performance on the object returned by predict and specify the desired performance measures.
+
+```{r}
+calculateConfusionMatrix(pred)
+performance(pred, measures = list(acc, fpr, tpr))
+df = generateThreshVsPerfData(pred, list(fpr, tpr, acc))
+plotThreshVsPerf(df)
+plotROCCurves(df)
+```
+
+### Extension of the original use case
+
+As you might have seen the titanic library also provides a second dataset.
+
+```{r}
+test = titanic_test
+head(test)
+```
+
+This one does not contain any survival information, but we now can use our fitted model and predict the survival probability for this data set.
+
+The same preprocessing steps - as for the "data" data set - have to be applied
+
+```{r}
+test[, c("Pclass", "Sex", "SibSp", "Embarked")] = lapply(test[,  c("Pclass", "Sex", "SibSp", "Embarked")], as.factor)
+
+test = dropNamed(test, c("Cabin","PassengerId", "Ticket", "Name"))
+
+test = impute(test, cols = list(Age = imputeMedian(), Fare = imputeMedian()))
+test = test$data
+
+summarizeColumns(test)
+```
+
+You can use the task and learner that you have already created.
+
+```{r}
+task
+lrn
+```
+
+The training step will be different now. We don't use a subset to fit the model, but use all data.
+
+```{r}
+mod = train(learner = lrn, task = task)
+```
+
+For the prediction part, we will use the new test data set.
+
+```{r}
+pred = predict(mod, newdata = test)
+pred
+```
+
+

src/usecase_clustering.Rmd

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+---
+output:
+  pdf_document: default
+  html_document: default
+---
+
+# Clustering
+
+```{r, echo = FALSE}
+set.seed(1234)
+```
+
+This is a use case for clustering with the [%mlr] package. We consider the [agriculture](&cluster::agriculture) dataset that contains observations about $n=12$ countries including  
+
+* the GNP (Gross National Product) per head (\texttt{x}) ,
+* the percentage in agriculture (\texttt{y}).
+
+So let's have a look at the data first.
+
+```{r, fig.asp = 0.8}
+data(agriculture, package = "cluster")
+
+plot(y ~ x, data = agriculture)
+```
+
+
+We aim to group the observations into clusters that contain similar objects. We will 
+
+* define the learning task  ([here](http://mlr-org.github.io/mlr-tutorial/devel/html/task/index.html)),
+* select a learning method ([here](http://mlr-org.github.io/mlr-tutorial/devel/html/learner/index.html)),
+* train the learner ([here](http://mlr-org.github.io/mlr-tutorial/devel/html/train/index.html)), 
+* evaluate the performance of the model ([here](http://mlr-org.github.io/mlr-tutorial/devel/html/performance/index.html)) and
+* tune the model ([here](http://mlr-org.github.io/mlr-tutorial/devel/html/tune/index.html)).
+
+### Defining a task
+
+We now have to define a clustering task. Notice that a clustering task doesn't have a target variable. 
+
+```{r}
+agri.task = makeClusterTask(data = agriculture)
+agri.task
+```
+
+Printing the task shows us some basic information as the number of observations, the data types of the features or if there are still some missing values, that should have been preprocessed.
+
+### Defining a learner
+
+We generate the learner by calling [&makeLearner] and specifying the learning method, and, if needed, hyperparameters.
+
+An overview over all learners can be found  [here](integrated_learners.md). You can also call the [&listLearners] command for our specific task.
+
+
+```{r eval = FALSE}
+listLearners(obj = agri.task)
+```
+
+We will apply the $k$-means algorithm with $3$ centers for the moment
+
+```{r}
+cluster.lrn = makeLearner("cluster.kmeans", centers = 3)
+cluster.lrn
+```
+### Train the model
+
+The next step is to train our learner by feeding it with our data.
+
+```{r}
+agri.mod = train(learner = cluster.lrn, task = agri.task)
+```
+
+We can extract the model and have a look at it. 
+
+```{r}
+getLearnerModel(agri.mod)
+```
+
+### Prediction
+
+Now, we can predict the target values, our cluster labels.  
+
+```{r}
+agri.pred = predict(agri.mod, task = agri.task)
+agri.pred
+```
+
+### Performance 
+
+Since the data given to the learner is unlabeled, there is no objective evaluation of the accuracy of our model. We have to consider other criteria in unsupervised learning. 
+
+An overview over all performance measures can be found [here](measures.md). You can also call the [&listMeasures] command for our specific task.
+
+```{r}
+listMeasures(agri.task)
+```
+
+Let's have a look at the silhouette coefficient and the Davies-Boulding index.
+
+```{r}
+performance(agri.pred, measures = list(silhouette, db), task = agri.task)
+```
+
+### Tuning
+
+It's hard to say if our clustering is good since up to now we have nothing to compare to. Could we have done better by choosing a different number of centers? 
+
+Tuning will address the question of choosing the best hyperparameters for our problem.
+
+We first create a search space for the number of clusters $k$, e. g. $k \in \lbrace 2, 3, 4, 5 \rbrace$. Further we define an optimization algorithm and a [resampling strategy](resample.md). Here we use grid search and 3-fold cross validation.
+
+Finally, by combining all the previous pieces, we can tune the parameter $k$ by calling [&tuneParams]. We will use discrete_ps with grid search and the silhouette coefficient as optimization criterion:
+
+```{r}
+discrete_ps = makeParamSet(makeDiscreteParam("centers", values = c(2, 3, 4, 5)))
+ctrl = makeTuneControlGrid()
+res = tuneParams(cluster.lrn, agri.task, measures = silhouette, resampling = cv3,
+  par.set = discrete_ps, control = ctrl)
+```
+
+Setting $k=2$ yields the best results for our clustering problem. 
+So let's generate a learner with the optimal hyperparameter $k=2$. 
+
+```{r}
+tuned.lrn = setHyperPars(cluster.lrn, par.vals = res$x)
+```
+
+We have to train the tuned learner again and predict the results.
+```{r}
+tuned.mod = train(tuned.lrn, agri.task)
+tuned.pred = predict(tuned.mod, task = agri.task)
+tuned.pred
+```
+
+This is our final clustering for our problem.
+
+```{r, fig.asp = 0.8}
+plot(y ~ x, col = getPredictionResponse(tuned.pred), data = agriculture)
+```
+
+
+
+
+
+
+