Alien test dataset

Description

A dataset describing features of living beings

Usage

alien.test

Format

A data.table with 8 rows and 5 variables:

SkinColor

Skin color of the individual

IQScore

IQ score of the individual

Cat1

Categorical descriptor

Cat2

Categorical descriptor

Cat3

Categorical descriptor

Details

library(data.table)

alien.test <- data.table::data.table( SkinColor=c("white", "green", "brown", "white", "red"), IQScore=c(79, 100, 125, 90, 115), Cat1=c("type4", "type4", "type3", "type1", "type1"), Cat2=c("type5", "type5", "type9", "type8", "type2"), Cat3=c("type2", "type2", "type7", "type4", "type4") )

save(alien.test, file="data/alien_test.rda")

Alien training dataset

Description

A dataset describing features of living beings and whether or not they are an alien

Usage

alien.train

Format

A data.table with 8 rows and 6 variables:

SkinColor

Skin color of the individual

IQScore

IQ score of the individual

Cat1

Categorical descriptor

Cat2

Categorical descriptor

Cat3

Categorical descriptor

IsAlien

Is this being an alien?

Details

library(data.table)

alien.train <- data.table::data.table( SkinColor=c("green", "white", "brown", "white", "blue", "white", "green", "white"), IQScore=c(300, 95, 105, 250, 115, 85, 130, 115), Cat1=c("type1", "type1", "type2", "type4", "type2", "type4", "type1", "type1"), Cat2=c("type1", "type2", "type6", "type5", "type7", "type5", "type2", "type1"), Cat3=c("type4", "type4", "type11", "type2", "type11", "type2", "type4", "type4"), IsAlien=c(TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE) )

save(alien.train, file="data/alien_train.rda")

Area Under the ROC Curve

Description

Calculates Area Under the ROC Curve

Usage

auc_roc(preds, actuals, returnDT = FALSE)

Arguments

Details

If returnDT=FALSE, returns Area Under the ROC Curve.If returnDT=TRUE, returns a data.table object with False Positive Rate and True Positive Rate for plotting the ROC curve.

References

https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve

Examples

library(data.table)
preds <- c(.1, .3, .3, .9)
actuals <- c(0, 0, 1, 1)
auc_roc(preds, actuals)
auc_roc(preds, actuals, returnDT=TRUE)

Map a vector of numeric values into bins

Description

Takes a vector of values and bin parameters and maps each value to an ordered factor whose levels are a set of bins like [0,1), [1,2), [2,3).

Values may be provided as a vector or via a pair of parameters - a data.table object and the name of the column to bin.

Usage

bin_data(x = NULL, binCol = NULL, bins = 10, binType = "explicit",
  boundaryType = "lcro]", returnDT = FALSE)

Arguments

Details

This function can return two different types of output, depending on whether returnDT is TRUE or FALSE.

If returnDT=FALSE, returns an ordered factor vector of bins like [1, 2), [-3,-2), ... corresponding to the values which were binned and whose levels correspond to all the generated bins. (Note that empty bins may be present as unused factor levels).

If returnDT=TRUE, returns a data.table object with all values and all bins (including empty bins). If dt is provided instead of vals, a full copy of dt is created and merged with the set of generated bins.

Examples

library(data.table)
iris.dt <- data.table(iris)

# custom bins
bin_data(iris.dt, binCol="Sepal.Length", bins=c(4, 5, 6, 7, 8))

# 10 equally spaced bins
bin_data(iris$Petal.Length, bins=10, returnDT=TRUE)

# make the last bin [left-closed, right-open)
bin_data(c(0,0,1,2), bins=2, boundaryType="lcro)", returnDT=TRUE)

# bin values by quantile
bin_data(c(0,0,0,0,1,2,3,4), bins=4, binType="quantile", returnDT=TRUE)

Date Factor

Description

Map a vector of dates to a factor at one of these levels "yearmonth", "yearquarter", "quarter", "month"

Usage

date_factor(dateVec, type = "yearmonth", minDate = min(dateVec, na.rm =
  TRUE), maxDate = max(dateVec, na.rm = TRUE))

Arguments

Details

The resulting vector is an ordered factor of the specified type (e.g. yearmonth)

Examples

library(data.table)
dts <- as.Date(c("2014-1-1", "2015-1-15", "2015-6-1"))
date_factor(dts, type = "yearmonth")
date_factor(dts, type = "yearquarter")
date_factor(
  dateVec = dts, 
  type = "yearquarter", 
  minDate = as.Date("2015-1-1"), 
  maxDate = as.Date("2015-12-31")
)
date_factor(
  dateVec = as.Date(character(0)), 
  type = "yearmonth", 
  minDate = as.Date("2016-1-1"), 
  as.Date("2016-12-31")
)

Empirical Cumulative Distribution Function

Description

Given a vector x, calculate P(x <= X) for a set of upper bounds X. Can be applied to a data.table object for multivariate use. That is, calculate P(x <= X, y <= Y, z <= Z, ...)

Usage

empirical_cdf(x, ubounds)

Arguments

Details

Calculate the empirical CDF of a vector, or data.table with multiple columns for multivariate use.

Examples

library(data.table)
dt <- data.table(x=c(0.3, 1.3, 1.4, 3.6), y=c(1.2, 1.2, 3.8, 3.9))
empirical_cdf(dt$x, ubounds=1:4)
empirical_cdf(dt, ubounds=CJ(x = 1:4, y = 1:4))

Explore Dataset

Description

(Experimental) Automated Exploratory Data Analysis

Usage

explore_dataset(dt1, dt2 = NULL, targetCol = NULL, verbose = FALSE)

Arguments

Details

Expirimental. Evaluates and summarizes the data in every column of a data.table. Can identify columns with hierarchical structure and columns with perfectly correlated values.

Examples

library(data.table)
explore_dataset(alien.train)

Exponential Weight

Description

Generate exponential weights

Usage

exponential_weight(k, base = exp(1), offset = 0, slope = 0.1)

Arguments

Details

Returns a weight based on the formula 1-base^(offset-slope*k)

Examples

exponential_weight(1:3, slope=.1)
exponential_weight(1:3, slope=1)
exponential_weight(1:3, slope=10)

Cross Validation Folds

Description

Map an object x into equal (or nearly equal) size folds. If x is a positive integer, a vector of FoldIDs of length matching x is returned, otherwise If x is a vector, a matching vector of FoldIDs is returned. If x is a data.table, a list of partitions of x is returned.

Usage

folds(x, nfolds = 5L, stratified = FALSE, seed = NULL)

Arguments

Details

Convenient method for mapping an object into equal size folds, potentially with stratification

Examples

library(data.table)
folds(8, nfolds=2)
folds(alien.train$IsAlien, nfolds=2)
folds(alien.train$IsAlien, nfolds=2, stratified=TRUE, seed=2016)
folds(alien.train$IQScore, nfolds=2, stratified=TRUE, seed=2016)
folds(alien.train, nfolds=2, stratified="IsAlien", seed=2016)

Geometric Weight

Description

Generate geometric weights

Usage

geometric_weight(k, n, r = 1)

Arguments

Details

Returns a weight based on the formula r^k/sum(r^seq_len(n)). The sequence of weights for k=1, 2, ..., n sum to 1

Examples

geometric_weight(1:3, n=3, r=1)
geometric_weight(1:3, n=3, r=.5)
geometric_weight(1:3, n=3, r=2)

Gini Impurities

Description

Identify group weighted gini impurities using pairs of columns within a dataset. Can be used to located hierarchical data, or 1-1 correspondences

Usage

gini_impurities(dt, wide = FALSE, verbose = FALSE)

Arguments

Details

For pairs of columns (Var1, Var2) in a dataset, calculates the weighted gini impurity of Var2 relative to the groups determined by Var1

Examples

library(data.table)
gini_impurities(alien.train)
gini_impurities(alien.train, wide=TRUE)

Gini Impurity

Description

Calculates the Gini Impurity of a set

Usage

gini_impurity(vals)

Arguments

Details

Gini Impurity is a measure of how often a randomly chosen element from a set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the set.

Examples

gini_impurity(c("red", "red", "blue", "green"))
gini_impurity(c(red=2, blue=1, green=1))

Matthews correlation coefficient

Description

Calculate Matthews correlation coefficient

Usage

mcc(preds = NULL, actuals = NULL, TP = NULL, FP = NULL, TN = NULL,
  FN = NULL, confusionM = NULL)

Arguments

Details

Calculate Matthews correlation coefficient. Provide either

• preds and actuals or

• TP, FP, TN, and FN

• confusionM

References

https://en.wikipedia.org/wiki/Matthews_correlation_coefficient

Examples

preds <- c(1,1,1,0,1,1,0,0)
actuals <- c(1,1,1,1,0,0,0,0)
mcc(preds, actuals)
mcc(actuals, actuals)
mcc(TP=3, FP=2, TN=2, FN=1)

# Multiclass
preds <- data.frame(
  setosa = rnorm(n = 150), 
  versicolor = rnorm(n = 150), 
  virginica = rnorm(n = 150)
)
preds <- preds == apply(preds, 1, max)
actuals <- data.frame(
  setosa = rnorm(n = 150), 
  versicolor = rnorm(n = 150), 
  virginica = rnorm(n = 150)
)
actuals <- actuals == apply(actuals, 1, max)
mcc(preds = preds, actuals = actuals)

# Confusion matrix
mcc(confusionM = matrix(c(0,3,3,3,0,3,3,3,0), nrow = 3))
mcc(confusionM = matrix(c(1,0,0,0,1,0,0,0,1), nrow = 3))

Mean Square Error

Description

Calculate Mean-Square Error (Deviation)

For the ith sample, Squared Error is calculated as SE = (prediction - actual)^2. MSE is then mean(squared errors).

Usage

mse(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

Details

Calculate Mean-Square Error (Deviation)

References

https://en.wikipedia.org/wiki/Mean_squared_error

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
mse(preds, actuals)

Mean Square Logarithmic Error

Description

Calculate Mean-Square-Logarithmic Error (Deviation)

For the ith sample, Squared Logarithmic Error is calculated as SLE = (log(prediction + 1) - log(actual + 1))^2. MSE is then mean(squared logarithmic errors). Note the '+1' in the calculation of SLE which avoids taking the logarithm of 0 for data which may include 0s.

Usage

msle(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

Details

Calculate Mean-Square-Logarithmic Error (Deviation)

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
msle(preds, actuals)

One Hot Encode

Description

One-Hot-Encode unordered factor columns of a data.table

Usage

one_hot(dt, cols = "auto", sparsifyNAs = FALSE, naCols = FALSE,
  dropCols = TRUE, dropUnusedLevels = FALSE)

Arguments

Details

One-hot-encoding converts an unordered categorical vector (i.e. a factor) to multiple binarized vectors where each binary vector of 1s and 0s indicates the presence of a class (i.e. level) of the of the original vector.

Examples

library(data.table)

dt <- data.table(
  ID = 1:4,
  color = factor(c("red", NA, "blue", "blue"), levels=c("blue", "green", "red"))
)

one_hot(dt)
one_hot(dt, sparsifyNAs=TRUE)
one_hot(dt, naCols=TRUE)
one_hot(dt, dropCols=FALSE)
one_hot(dt, dropUnusedLevels=TRUE)

Relative Position

Description

Scale a vector of values to the range [0, 1] based on rank/position

Usage

relative_position(vals)

Arguments

Details

Values are ranked and then scaled to the range [0, 1]. Ties result in the same relative position (e.g. relative_position(c(1, 2, 2, 3)) returns the vector c(0.0 0.5 0.5 1.0)). NAs remain as NAs.

Examples

relative_position(1:10)
relative_position(c(1, 2, 2, 3))
relative_position(c(1, NA, 3, 4))

Replace NA Values

Description

Convenience method for returning a copy of a vector such that NA values are substituted with a replacement value

Usage

replace_na(x, repl = "auto")

Arguments

Details

Returns a copy of x such that NAs get replaced with a replacement value. Default replacement value is 0.

Examples

replace_na(c(1, NA, 1, 0))

Root Mean Square Error

Description

Calculate Root-Mean-Square Error (Deviation)

For the ith sample, Squared Error is calculated as SE = (prediction - actual)^2. RMSE is then sqrt(mean(squared errors)).

Usage

rmse(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

Details

Calculate Root-Mean-Square Error (Deviation)

References

https://en.wikipedia.org/wiki/Root-mean-square_deviation

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
rmse(preds, actuals)

Root Mean Square Logarithmic Error

Description

Calculate Root-Mean-Square-Logarithmic Error (Deviation)

For the ith sample, Squared Logarithmic Error is calculated as SLE = (log(prediction + 1) - log(actual + 1))^2. RMSLE is then sqrt(mean(squared logarithmic errors)). Note the '+1' in the calculation of SLE which avoids taking the logarithm of 0 for data which may include 0s.

Usage

rmsle(preds = NULL, actuals = NULL, weights = 1, na.rm = FALSE)

Arguments

Details

Calculate Root-Mean-Square-Logarithmic Error (Deviation)

Examples

preds <- c(1.0, 2.0, 9.5)
actuals <- c(0.9, 2.1, 10.0)
rmsle(preds, actuals)

ROC scores

Description

This function provides a way to identify the worst predictions when measuring Area Under the ROC curve. Simply put, the worst predictions are the ones with very low or high relative prediction scores (usually probabilities) which relate to the positive and negative samples respectively.

Usage

roc_scores(preds, actuals)

Arguments

Details

How it works

• First the relative position (between 0 and 1) of each prediction is determined

• Next the mean of actuals is determined

• For samples whose position is on the correct side of the overall mean, 0 is given

• For samples whose position is on the wrong side of the overall mean, its distance from the mean is given

Examples

roc_scores(c(1,2,3,4), actuals=c(1,1,0,0))
roc_scores(c(0.1, 0.2, 0.3, 0.4), actuals=c(TRUE, FALSE, TRUE, FALSE))

Set Factor

Description

Convience method for dealing with factors. Map a list of vectors to a list of factor vectors (1-1 mapping) such that the factor vectors all have the same levels - the unique values of the union of all the vectors in the list. Optionally group all low frequency values into a "_other_" level.

Usage

set_factor(vectorList, aggregationThreshold = 0)

Arguments

Examples

x <- c("a", "b", "c", "c")
y <- c("a", "d", "d")
set_factor(list(x, y))
set_factor(list(x, y), aggregationThreshold=1)

Skewness

Description

Calculates the skewness of each field in a data.table

Usage

skewness(dt)

Arguments

Details

Counts the frequency of each value in each column, then displays the results in descending order

Examples

library(data.table)
skewness(alien.train)

Sparsify

Description

Convert a data.table object into a sparse matrix (with the same number of rows).

Usage

sparsify(dt, sparsifyNAs = FALSE, naCols = "none")

Arguments

Details

Converts a data.table object to a sparse matrix (class "dgCMatrix"). Requires the Matrix package. All sparsified data is assumed to take on the value 0/FALSE

### Data Type | Description & NA handling

numeric | If sparsifyNAs = FALSE, only 0s will be sparsified If sparsifyNAs = TRUE, 0s and NAs will be sparsified

factor (unordered) | Each level will generate a sparsified binary column Column names are feature_level, e.g. "color_red", "color_blue"

factor (ordered) | Levels are converted to numeric, 1 - NLevels If sparsifyNAs = FALSE, NAs will remain as NAs If sparsifyNAs = TRUE, NAs will be sparsified

logical | TRUE and FALSE values will be converted to 1s and 0s If sparsifyNAs = FALSE, only FALSEs will be sparsified If sparsifyNAs = TRUE, FALSEs and NAs will be sparsified

Examples

library(data.table)
library(Matrix)

dt <- data.table(
  intCol=c(1L, NA_integer_, 3L, 0L),
  realCol=c(NA, 2, NA, NA),
  logCol=c(TRUE, FALSE, TRUE, FALSE),
  ofCol=factor(c("a", "b", NA, "b"), levels=c("a", "b", "c"), ordered=TRUE),
  ufCol=factor(c("a", NA, "c", "b"), ordered=FALSE)
)

sparsify(dt)
sparsify(dt, sparsifyNAs=TRUE)
sparsify(dt[, list(realCol)], naCols="identify")
sparsify(dt[, list(realCol)], naCols="efficient")