# DriveML: Self-Drive machine learning projects

### Automated machine learning classification model functions

#### Dayananda Ubrangala, Sayan Putatunda, Kiran R, Ravi Prasad Kondapalli

#### 2020-05-29

## 1. Introduction

The document introduces the DriveML package and how it can help you to build effortless machine learning binary classification models in short period

DriveML is a series of functions sucha as `AutoDataPrep`, `AutoMAR`, `autoMLmodel`. DriveML automates some of the most difficult machine learning functions such as data exploratory analysis, data pre-processing, feature engineering, model training, model validation, model tuning and model selection.

This package automates following steps on any input dataset for machine learning classification problems

1. Exploratory data analysis using SmartEDA functions
   - Generate automated EDA report in HTML format to understand the distributions of the data
2. Data cleaning
   - Replacing NA, infinite values
   - Removing duplicates
   - Cleaning feature names
3. Feature engineering
   - Missing at random features
   - Missing variable imputation
   - Outlier treatment - Oultier flag and imputation with 5th or 95th percentile value
   - Date variable transformation
   - Bulk interactions for numerical features
   - Frequent tranformer for categorical features
   - Categorical feature engineering - one hot encoding
   - Feature selection using zero variance, correlation and AUC method
4. Model training and validation
   - Automated test and validation set creations
   - Hyperparameter tuing using random search
   - Mutliple binary classification included like logistic regression, randomForest, xgboost, glmnet, rpart
   - Model validation using AUC value
   - Model plots like training and testing ROC curve, threshold plot
   - Probaility scores and model objects
5. Ensemble modelling
   - Model ensembling using nnls and nnet method
6. Model Explanation
   - Lift plot
   - Partial dependence plot
   - Feature importance plot
7. Model report
   - model output in rmarkdown html format

To summarize, DriveML package helps in getting the complete Machine learning classification model just by running the function instead of writing lengthy r code.

This package performs ML models in R using MLR package

#### Missing not at random features

Algorithm: Missing at random features

1. Select all the missing features X_i where i=1,2,…..,N.
2. For i=1 to N:
   - Define Y_i, which will have value of 1 if X_i has a missing value, 0 if X_i is not having missing value
   - Impute all X_(i+1 ) 〖to X〗_(N ) variables using imputation method
   - Fit binary classifier f_m to the training data using Y_i ~ X_(i+1 ) 〖+⋯+ X〗_(N )
   - Calculate AUC ∝_ivalue between actual Y_i and predicted Y ̂_i
   - If ∝_i is low then the missing values in X_i are missing at random,Y_i to be dropped
   - Repeat steps 1 to 4 for all the independent variables in the original dataset

## 2. Functionalities of DriveML

The DriveML R package has four unique functionalities as

1. Data Exploration
   - `SmartEDA` has a complete exploratory data analysis function
2. Data preparations
   - `autoDataPrep` function to generate a novel features based on the functional understanding of the dataset
3. Machine learning models
   - `autoMLmodel` function to develope baseline machine learning models using regression and tree based classfication techniques
4. Model report
   - `autoMLReport` function to print the machine learning model outcome in HTML format

## 3. DriveML: Machine learning classfication model

#### About data

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The “goal” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4.

Data Source Kaggle.

Install the package “DriveML” to get the example data set.

    install.packages("DriveML")
    library("DriveML")
    library("SmartEDA")
    ## Load sample dataset from ISLR pacakge
    heart = DriveML::heart

more detailed attribute information is there in `DriveML` help page

### 3.1 Data Exploration

For data exploratory analysis used `SmartEDA` package

#### Overview of the data

Understanding the dimensions of the dataset, variable names, overall missing summary and data types of each variables

    # Overview of the data - Type = 1
    ExpData(data=heart,type=1)

    # Structure of the data - Type = 2
    ExpData(data=heart,type=2)

- Overview of the data

  Descriptions | Obs
  --- | ---
  Sample size (Nrow) | 303
  No. of Variables (Ncol) | 14
  No. of Numeric Variables | 14
  No. of Factor Variables | 0
  No. of Text Variables | 0
  No. of Logical Variables | 0
  No. of Unique Variables | 0
  No. of Date Variables | 0
  No. of Zero variance Variables (Uniform) | 0
  %. of Variables having complete cases | 100% (14)
  %. of Variables having <50% missing cases | 0% (0)
  %. of Variables having >50% missing cases | 0% (0)
  %. of Variables having >90% missing cases | 0% (0)

- Structure of the data

S.no | Variable Name | Variable Type | % of Missing | No. of Unique values
--- | --- | --- | --- | ---
1 | age | integer | 0 | 41
2 | sex | integer | 0 | 2
3 | cp | integer | 0 | 4
4 | trestbps | integer | 0 | 49
5 | chol | integer | 0 | 152
6 | fbs | integer | 0 | 2
7 | restecg | integer | 0 | 3
8 | thalach | integer | 0 | 91
9 | exang | integer | 0 | 2
10 | oldpeak | numeric | 0 | 40
11 | slope | integer | 0 | 3
12 | ca | integer | 0 | 5
13 | thal | integer | 0 | 4
14 | target_var | integer | 0 | 2

#### Summary of numerical variables

    ExpNumStat(heart,by="GA",gp="target_var",Qnt=seq(0,1,0.1),MesofShape=2,Outlier=TRUE,round=2)

#### Distributions of Numerical variables

- Box plots for all numerical variables vs categorical dependent variable - Bivariate comparision only with categories

Boxplot for all the numeric attributes by each category of target_var

    plot4 <- ExpNumViz(heart,target="target_var",type=1,nlim=3,fname=NULL,Page=c(2,2),sample=8)
    plot4[[1]]

#### Summary of categorical variables

Cross tabulation with target_var variable

- Custom tables between all categorical independent variables and target_var variable target_var

    ExpCTable(Carseats,Target="Urban",margin=1,clim=10,nlim=3,round=2,bin=NULL,per=F)

VARIABLE | CATEGORY | target_var:0 | target_var:1 | TOTAL
--- | --- | --- | --- | ---
sex | 0 | 24 | 72 | 96
sex | 1 | 114 | 93 | 207
sex | TOTAL | 138 | 165 | 303
fbs | 0 | 116 | 142 | 258
fbs | 1 | 22 | 23 | 45
fbs | TOTAL | 138 | 165 | 303
restecg | 0 | 79 | 68 | 147
restecg | 1 | 56 | 96 | 152
restecg | 2 | 3 | 1 | 4
restecg | TOTAL | 138 | 165 | 303
exang | 0 | 62 | 142 | 204
exang | 1 | 76 | 23 | 99
exang | TOTAL | 138 | 165 | 303
slope | 0 | 12 | 9 | 21
slope | 1 | 91 | 49 | 140
slope | 2 | 35 | 107 | 142
slope | TOTAL | 138 | 165 | 303
target_var | 0 | 138 | 0 | 138
target_var | 1 | 0 | 165 | 165
target_var | TOTAL | 138 | 165 | 303

#### Distributions of categorical variables

Stacked bar plot with vertical or horizontal bars for all categorical variables

    plot5 <- ExpCatViz(heart,target = "target_var", fname = NULL, clim=5,col=c("slateblue4","slateblue1"),margin=2,Page = c(2,1),sample=2)
    plot5[[1]]

#### Outlier analysis using boxplot

    ExpOutliers(heart, varlist = c("oldpeak","trestbps","chol"), method = "boxplot",  treatment = "mean", capping = c(0.1, 0.9))

Category | oldpeak | trestbps | chol
--- | --- | --- | ---
Lower cap : 0.1 | 0 | 110 | 188
Upper cap : 0.9 | 2.8 | 152 | 308.8
Lower bound | -2.4 | 90 | 115.75
Upper bound | 4 | 170 | 369.75
Num of outliers | 5 | 9 | 5
Lower outlier case |  |  |
Upper outlier case | 102,205,222,251,292 | 9,102,111,204,224,242,249,261,267 | 29,86,97,221,247
Mean before | 1.04 | 131.62 | 246.26
Mean after | 0.97 | 130.1 | 243.04
Median before | 0.8 | 130 | 240
Median after | 0.65 | 130 | 240

### 3.2 Data preparations using `autoDataprep`

    dateprep <- autoDataprep(data = heart,
                                 target = 'target_var',
                                 missimpute = 'default',
                                 auto_mar = FALSE,
                                 mar_object = NULL,
                                 dummyvar = TRUE,
                                 char_var_limit = 15,
                                 aucv = 0.002,
                                 corr = 0.98,
                                 outlier_flag = TRUE,
                                 uid = NULL,
                                 onlykeep = NULL,
                                 drop = NULL)
    printautoDataprep(dateprep)

    ## Data preparation result
    ## Call:
    ##  autoDataprep(data = heart, target = "target_var", missimpute = "default",      auto_mar = FALSE, mar_object = NULL, dummyvar = TRUE, char_var_limit = 15,      aucv = 0.002, corr = 0.98, outlier_flag = TRUE, uid = NULL,      onlykeep = NULL, drop = NULL)
    ##
    ##  *** Data preparation summary ***
    ## Total no. of columns available in the data set:  14
    ## No. of numeric columns:                          8
    ## No. of factor / character columns:               0
    ## No. of date columns:                             0
    ## No. of logical columns:                          0
    ## No. of unique columns:                           0
    ## No. of MAR columns:                              0
    ## No. of dummy variables created:                  0
    ##
    ##  *** Variable reduction ***
    ## Step 1 - Checked and removed useless variables:         6
    ## Step 2 - No. of variables before fetature reduction:    22
    ## Step 3 - No. of zero variance columns (Constant):       0
    ## Step 4 - No. of high correlated or bijection columns:   3
    ## Step 5 - No. of low AUC valued columns:                 2
    ## *Final number of columns considered for ML model:       17
    ##
    ##  *** Data preparation highlights ***
    ## Missing replaced with {
    ## --> factor = imputeMode()
    ## --> integer = imputeMean()
    ## --> numeric = imputeMedian()
    ## --> character = imputeMode() }

    train_data <- dateprep$master_data

### 3.3 Machine learning models using `autoMLmodel`

Automated training, tuning and validation of machine learning models. This function includes following binary classification techniques

- Logistic regression - logreg
  - Regularised regression - glmnet
  - Extreme gradient boosting - xgboost
  - Random forest - randomForest
  - Random forest - ranger
  - Decision tree - rpart

    mymodel <- autoMLmodel( train = heart,
                            test = NULL,
                            target = 'target_var',
                            testSplit = 0.2,
                            tuneIters = 10,
                            tuneType = "random",
                            models = "all",
                            varImp = 10,
                            liftGroup = 50,
                            maxObs = 4000,
                            uid = NULL,
                            htmlreport = FALSE,
                            seed = 1991)

### 3.3 Model output

Model performance

Model | Fitting time | Scoring time | Train AUC | Test AUC | Accuracy | Precision | Recall | F1_score
--- | --- | --- | --- | --- | --- | --- | --- | ---
glmnet | 2.558 secs | 0.008 secs | 0.928 | 0.908 | 0.820 | 0.824 | 0.848 | 0.836
logreg | 2.513 secs | 0.004 secs | 0.929 | 0.906 | 0.820 | 0.824 | 0.848 | 0.836
randomForest | 2.785 secs | 0.012 secs | 1.000 | 0.877 | 0.754 | 0.765 | 0.788 | 0.776
ranger | 2.981 secs | 0.044 secs | 0.999 | 0.900 | 0.803 | 0.784 | 0.879 | 0.829
xgboost | 2.938 secs | 0.004 secs | 0.996 | 0.907 | 0.820 | 0.806 | 0.879 | 0.841
rpart | 2.559 secs | 0.004 secs | 0.927 | 0.814 | 0.738 | 0.730 | 0.818 | 0.771

Randomforest model ROC curve and variable importance

Training data set ROC

    TrainROC <- mymodel$trainedModels$randomForest$modelPlots$TrainROC
    TrainROC

Test data set ROC

    TestROC <- mymodel$trainedModels$randomForest$modelPlots$TestROC
    TestROC

Variable importance

    VarImp <- mymodel$trainedModels$randomForest$modelPlots$VarImp
    VarImp

    ## [[1]]

Threshold

    Threshold <- mymodel$trainedModels$randomForest$modelPlots$Threshold
    Threshold