Test

Description	Identifier
calc	-
getTransactions	-
should return values	-
should contain all unique values of all transactions	-
should have a map of all unique values and indexes	-
should map values onto transactions	-
assocationRuleLearning	-
should use Eclat to associate transactions	-
should use accept options for eclat summary	-
should work with raw transactions	-
should handle errors	-
nlp	-
ColumnVectorizer class	-
constructor	-
should instantiate a new ColumnVectorizer Class	-
get_tokens	-
should return an array of all tokens	-
get_vector_array	-
should return an array of tokens as vectors	-
get_limited_features	-
should return a count of maxFeatures in array of tokens as vectors	-
should limit features in array of tokens as vectors	-
evaluateString	-
should return object of tokens and counts	-
evaluate	-
should return matrix vector for new predictions	-
fit_transform	-
should create a set of unique tokens this.tokens	-
should create a dictionary of total word counts in this.wordCountMap	-
should create a dictionary of all words this.wordMap	-
should create an array of all sorted words in this.sortedWordCount by word count	-
should create a sparse matrix dictionary words from corpus in this.data as this.vectors	-
should create a sparse matrix of words from corpus in this.data	-
cross_validation	-
train_test_split	-
should split dataset with default values	-
should split the data into two arrays	-
should split with defined test size	-
should split with defined train size	-
should use a randomized seed	-
cross_validation_split	-
should split dataset with default values	-
should split the data into k-folds	-
should use a randomized seed	-
cross_validate_score	-
should validate classification	-
should validate classification constructor	-
should validate regression with train method	-
should validate regression with constructor methods	-
grid_search	-
should return sorted best regression parameters	-
should return sorted best classification parameters	-
should sort parameter performance logically for regression	-
should sort parameter performance logically for classification	-
loadCSV	-
loading CSV from File	-
should load a csv from a filepath	-
should handle errors with invalid files	-
loading CSV from remote URI	-
should load a csv from a remote URI	-
should handle errors with invalid url	-
should load a csv from a remote URI directly	-
should handle errors with invalid url directly	-
loadTSV	-
should load tab separated values	-
ml	-
ReinforcedLearningBase	-
should create an instance with default values	-
should create configurable instance	-
should require implementations of learn, train and predict methods	-
UpperConfidenceBound	-
should create number of selections and sum of selections	-
should predict the next value using the upper confidence bound	-
should initially select each bandit	-
should train the next upper confidence bound	-
ThompsonSampling	-
should create the number of rewards	-
should predict the next value using thompson sampling	-
should evaluate the next thompson sampling sample	-
preprocessing	-
DataSet class	-
constructor	-
should instantiate a new DataSet Class	-
static getBinaryValue	-
should return 0 or 1 depending on input values	-
static mapToObject	-
should convert a map into an equivalent JavaScript Object	-
should convert nested map objects	-
should handle empty maps	-
exportFeatures	-
should export JavaScript Object of encoders,labels and scalers	-
importFeatures	-
should import exported Features	-
filterColumn	-
should by default return full dataset	-
should filter data by a filter function	-
columnMatrix	-
should create a matrix of values from columns	-
should create a matrix of values from an array of column names	-
should handle invalid columns	-
static reverseColumnMatrix	-
should reverse a matrix of values into labeled object	-
static reverseColumnVector	-
should reverse a vector of values into labeled object	-
selectColumns	-
should return a list of objects with only selected columns as properties	-
columnArray	-
should select a column from CSV Data by name	-
should prefilter the dataset	-
should filter the dataset	-
should replace values in dataset	-
should convert vals to numbers	-
should standardize scale values	-
should z-score / MinMax scale values	-
should log scale values	-
should exp scale values	-
columnScale / columnDescale	-
should scale a column and store the transform functions	-
should descale a column	-
labelEncoder	-
should binary label encode	-
should label encode	-
should decode labels	-
getTransforms	-
should take column fit options as an array	-
encodeObject	-
should onehot encode an object	-
transformObject / inverseTransformObject	-
should encode new data using existing transforms	-
should inverse transform objects	-
oneHotDecoder	-
should one hot decode	-
oneHotColumnArray	-
should return all encoded columns	-
oneHotEncoder	-
should one hot encode	-
columnReducer	-
should reduce column and greate a new column	-
columnReplace	-
should label encode	-
should onehot encode	-
should replace empty values with mean by default	-
should replace empty values with stat function from ml.js	-
should replace values by standard scaling	-
fitColumns	-
should accept simple format for transformations	-
should merge columns	-
should only merge columns if data length matches	-
should fit multiple columns	-
fitInverseTransforms / fitTransforms	-
should inverse transforms on dataset	-
util	-
forecasting metrics	-
forecastErrors	-
should return array of residuals	-
should throw an error if array lengths are not the same	-
meanForecastError	-
should return bias of forecast accuracy	-
meanAbsoluteDeviation	-
should return absolute size of the errors	-
trackingSignal	-
should return tracking Signal	-
meanSquaredError	-
should return MSE	-
MADMeanRatio	-
should return MMR	-
meanAbsolutePercentageError	-
should return MAPE	-
max	-
should return max value	-
min	-
should return min value	-
mean	-
should return mean value	-
Standard Scaler Transforms	-
should return an object with a scale function, descale function and values array	-
should have a values array that is equal to the array produced by StandardScaler function	-
should have a values array that is equal to the array produced by StandardScaler function	-
should properly scale single values	-
should properly descale single values	-
MinMax Scaler Transforms	-
should return an object with a scale function, descale function and values array	-
should have a values array that is equal to the array produced by MinMaxScaler function	-
should properly scale single values	-
should properly descale single values	-
Log Scaler	-
should return log scaled values	-
Exponent Scaler	-
should return exponent scaled values	-
Standard Error of the Estimate	-
should return the Standard Error of the Estimate	-
should return an error if array lengths are not the same	-
getSafePropertyName	-
should sanitize property names	-
Coefficient of correlation	-
should return the Coefficient of correlation	-
should return an error if array lengths are not the same	-
rSquared	-
should return r^2	-
Coefficient of determination	-
should return the Coefficient of determination	-
should return an error if array lengths are not the same	-
adjusted coefficient of determination	-
should return the adjusted Coefficient of determination	-
pivotVector	-
should pivot vectors into arrays	-
pivotArrays	-
should pivot arrays into vectors	-
Z Scores / Standard Scores	-
should calculate standard scores	-
should approximate the p-value from the z score	-