Package 'ppsr'

Lists all algorithms currently supported

Description

Lists all algorithms currently supported

Usage

available_algorithms()

Value

a list of all available parsnip engines

Examples

available_algorithms()

---

Lists all evaluation metrics currently supported

Description

Lists all evaluation metrics currently supported

Usage

available_evaluation_metrics()

Value

a list of all available evaluation metrics and their implementation in functional form

Examples

available_evaluation_metrics()

---

Normalizes the original score compared to a naive baseline score The calculation that's being performed depends on the type of model

Description

Normalizes the original score compared to a naive baseline score The calculation that's being performed depends on the type of model

Usage

normalize_score(baseline_score, model_score, type)

Arguments

baseline_score	float, the evaluation metric score for a naive baseline (model)
model_score	float, the evaluation metric score for a statistical model
type	character, type of model

Value

numeric vector of length one, normalized score

---

ppsr: An R implementation of the Predictive Power Score (PPS)

Description

The PPS is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two columns. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). It can be used as an alternative to the correlation (matrix).

---

Calculate predictive power score for x on y

Description

Calculate predictive power score for x on y

Usage

score(
  df,
  x,
  y,
  algorithm = "tree",
  metrics = list(regression = "MAE", classification = "F1_weighted"),
  cv_folds = 5,
  seed = 1,
  verbose = TRUE
)

Arguments

df	data.frame containing columns for x and y
x	string, column name of predictor variable
y	string, column name of target variable
algorithm	string, see available_algorithms()
metrics	named list of eval_* functions used for regression and classification problems, see available_evaluation_metrics()
cv_folds	float, number of cross-validation folds
seed	float, seed to ensure reproducibility/stability
verbose	boolean, whether to print notifications

Value

a named list, potentially containing

x

the name of the predictor variable

y

the name of the target variable

result_type

text showing how to interpret the resulting score

pps

the predictive power score

metric

the evaluation metric used to compute the PPS

baseline_score

the score of a naive model on the evaluation metric

model_score

the score of the predictive model on the evaluation metric

cv_folds

how many cross-validation folds were used

seed

the seed that was set

algorithm

text shwoing what algorithm was used

model_type

text showing whether classification or regression was used

Examples

score(iris, x = 'Petal.Length', y = 'Species')

---

Calculate correlation coefficients for whole dataframe

Description

Calculate correlation coefficients for whole dataframe

Usage

score_correlations(df, ...)

Arguments

df	data.frame containing columns for x and y
...	arguments to pass to stats::cor()

Value

a data.frame with x-y correlation coefficients

Examples

score_correlations(iris)

---

Calculate predictive power scores for whole dataframe Iterates through the columns of the dataframe, calculating the predictive power score for every possible combination of x and y.

Description

Calculate predictive power scores for whole dataframe Iterates through the columns of the dataframe, calculating the predictive power score for every possible combination of x and y.

Usage

score_df(df, ..., do_parallel = FALSE, n_cores = -1)

Arguments

df	data.frame containing columns for x and y
...	any arguments passed to score
do_parallel	bool, whether to perform score calls in parallel
n_cores	numeric, number of cores to use, defaults to maximum minus 1

Value

a data.frame containing

x

the name of the predictor variable

y

the name of the target variable

result_type

text showing how to interpret the resulting score

pps

the predictive power score

metric

the evaluation metric used to compute the PPS

baseline_score

the score of a naive model on the evaluation metric

model_score

the score of the predictive model on the evaluation metric

cv_folds

how many cross-validation folds were used

seed

the seed that was set

algorithm

text shwoing what algorithm was used

model_type

text showing whether classification or regression was used

Examples

score_df(iris)
score_df(mtcars, do_parallel = TRUE, n_cores = 2)

---

Calculate predictive power score matrix Iterates through the columns of the dataset, calculating the predictive power score for every possible combination of x and y.

Description

Note that the targets are on the rows, and the features on the columns.

Usage

score_matrix(df, ...)

Arguments

df	data.frame containing columns for x and y
...	any arguments passed to score_df, some of which will be passed on to score

Value

a matrix of numeric values, representing predictive power scores

Examples

score_matrix(iris)
score_matrix(mtcars, do_parallel = TRUE, n_cores=2)

---

Calculates out-of-sample model performance of a statistical model

Description

Calculates out-of-sample model performance of a statistical model

Usage

score_model(train, test, model, x, y, metric)

Arguments

train	df, training data, containing variable y
test	df, test data, containing variable y
model	parsnip model object, with mode preset
x	character, column name of predictor variable
y	character, column name of target variable
metric	character, name of evaluation metric being used, see available_evaluation_metrics()

Value

numeric vector of length one, evaluation score for predictions using naive model

---

Calculate out-of-sample model performance of naive baseline model The calculation that's being performed depends on the type of model For regression models, the mean is used as prediction For classification, a model predicting random values and a model predicting modal values are used and the best model is taken as baseline score

Description

Calculate out-of-sample model performance of naive baseline model The calculation that's being performed depends on the type of model For regression models, the mean is used as prediction For classification, a model predicting random values and a model predicting modal values are used and the best model is taken as baseline score

Usage

score_naive(train, test, x, y, type, metric)

Arguments

train	df, training data, containing variable y
test	df, test data, containing variable y
x	character, column name of predictor variable
y	character, column name of target variable
type	character, type of model
metric	character, evaluation metric being used

Value

numeric vector of length one, evaluation score for predictions using naive model

---

Calculate predictive power scores for y Calculates the predictive power scores for the specified y variable using every column in the dataset as x, including itself.

Description

Calculate predictive power scores for y Calculates the predictive power scores for the specified y variable using every column in the dataset as x, including itself.

Usage

score_predictors(df, y, ..., do_parallel = FALSE, n_cores = -1)

Arguments

df	data.frame containing columns for x and y
y	string, column name of target variable
...	any arguments passed to score
do_parallel	bool, whether to perform score calls in parallel
n_cores	numeric, number of cores to use, defaults to maximum minus 1

Value

a data.frame containing

x

the name of the predictor variable

y

the name of the target variable

result_type

text showing how to interpret the resulting score

pps

the predictive power score

metric

the evaluation metric used to compute the PPS

baseline_score

the score of a naive model on the evaluation metric

model_score

the score of the predictive model on the evaluation metric

cv_folds

how many cross-validation folds were used

seed

the seed that was set

algorithm

text shwoing what algorithm was used

model_type

text showing whether classification or regression was used

Examples

score_predictors(df = iris, y = 'Species')
score_predictors(df = mtcars, y = 'mpg', do_parallel = TRUE, n_cores = 2)

---

Visualize the PPS & correlation matrices

Description

Visualize the PPS & correlation matrices

Usage

visualize_both(
  df,
  color_value_positive = "#08306B",
  color_value_negative = "#8b0000",
  color_text = "#FFFFFF",
  include_missings = TRUE,
  nrow = 1,
  ...
)

Arguments

df	data.frame containing columns for x and y
color_value_positive	color used for upper limit of gradient (high positive correlation)
color_value_negative	color used for lower limit of gradient (high negative correlation)
color_text	string, hex value or color name used for text, best to pick high contrast with color_value_high
include_missings	bool, whether to include the variables without correlation values in the plot
nrow	numeric, number of rows, either 1 or 2
...	any arguments passed to score

Value

a grob object, a grid with two ggplot2 heatmap visualizations

Examples

visualize_both(iris)

visualize_both(mtcars, do_parallel = TRUE, n_cores = 2)

---

Visualize the correlation matrix

Description

Visualize the correlation matrix

Usage

visualize_correlations(
  df,
  color_value_positive = "#08306B",
  color_value_negative = "#8b0000",
  color_text = "#FFFFFF",
  include_missings = FALSE,
  ...
)

Arguments

df	data.frame containing columns for x and y
color_value_positive	color used for upper limit of gradient (high positive correlation)
color_value_negative	color used for lower limit of gradient (high negative correlation)
color_text	color used for text, best to pick high contrast with color_value_high
include_missings	bool, whether to include the variables without correlation values in the plot
...	arguments to pass to stats::cor()

Value

a ggplot object, a heatmap visualization

Examples

visualize_correlations(iris)

---

Visualize the Predictive Power scores of the entire dataframe, or given a target

Description

If y is specified, visualize_pps returns a barplot of the PPS of every predictor on the specified target variable. If y is not specified, visualize_pps returns a heatmap visualization of the PPS for all X-Y combinations in a dataframe.

Usage

visualize_pps(
  df,
  y = NULL,
  color_value_high = "#08306B",
  color_value_low = "#FFFFFF",
  color_text = "#FFFFFF",
  include_target = TRUE,
  ...
)

Arguments

df	data.frame containing columns for x and y
y	string, column name of target variable, can be left NULL to visualize all X-Y PPS
color_value_high	string, hex value or color name used for upper limit of PPS gradient (high PPS)
color_value_low	string, hex value or color name used for lower limit of PPS gradient (low PPS)
color_text	string, hex value or color name used for text, best to pick high contrast with color_value_high
include_target	boolean, whether to include the target variable in the barplot
...	any arguments passed to score

Value

a ggplot object, a vertical barplot or heatmap visualization

Examples

visualize_pps(iris, y = 'Species')

visualize_pps(iris)

visualize_pps(mtcars, do_parallel = TRUE, n_cores = 2)

---