javascriptdata · dcrescim · Apr 21, 2022 · Apr 21, 2022 · Apr 21, 2022
diff --git a/package.json b/package.json
@@ -124,6 +124,9 @@
     "typescript": "^4.5.4"
   },
   "jest": {
+    "testPathIgnorePatterns" : [
+      "<rootDir>/src/index.test.ts" 
+    ],
     "testEnvironment": "node",
     "preset": "ts-jest",
     "globals": {

diff --git a/src/cluster/kmeans.test.ts → src/cluster/KMeans.test.ts b/src/cluster/kmeans.test.ts → src/cluster/KMeans.test.ts
@@ -1,4 +1,4 @@
-import { KMeans } from './kmeans'
+import { KMeans } from './KMeans'
 
 // Next steps: Improve on kmeans cluster testing
 describe('KMeans', () => {
@@ -40,7 +40,7 @@ describe('KMeans', () => {
 
   it('should save kmeans model', () => {
     const expectedResult = {
-      name: 'kmeans',
+      name: 'KMeans',
       nClusters: 2,
       init: 'random',
       maxIter: 300,

diff --git a/src/cluster/kmeans.ts → src/cluster/KMeans.ts b/src/cluster/kmeans.ts → src/cluster/KMeans.ts
@@ -71,7 +71,7 @@ export class KMeans extends Serialize {
   clusterCenters: tf.Tensor2D
 
   /** Useful for pipelines and column transformers to have a default name for transforms */
-  name = 'kmeans'
+  name = 'KMeans'
 
   constructor({
     nClusters = 8,
@@ -145,7 +145,7 @@ export class KMeans extends Serialize {
   }
 
   /**
-   * Converts 2D input into a 1D Tensor which holds the Kmeans cluster Class label
+   * Converts 2D input into a 1D Tensor which holds the KMeans cluster Class label
    * @param X The 2D Matrix that you wish to cluster
    */
   public predict(X: Scikit2D): tf.Tensor1D {

diff --git a/src/compose/columnTransformer.test.ts → src/compose/ColumnTransformer.test.ts b/src/compose/columnTransformer.test.ts → src/compose/ColumnTransformer.test.ts
@@ -1,6 +1,6 @@
-import { ColumnTransformer } from './columnTransformer'
-import { MinMaxScaler } from '../preprocessing/minMaxScaler'
-import { SimpleImputer } from '../impute/simpleImputer'
+import { ColumnTransformer } from './ColumnTransformer'
+import { MinMaxScaler } from '../preprocessing/MinMaxScaler'
+import { SimpleImputer } from '../impute/SimpleImputer'
 import * as dfd from 'danfojs-node'
 
 describe('ColumnTransformer', function () {

diff --git a/src/compose/columnTransformer.ts → src/compose/ColumnTransformer.ts b/src/compose/columnTransformer.ts → src/compose/ColumnTransformer.ts
@@ -69,7 +69,7 @@ export class ColumnTransformer {
   remainder: Transformer | 'drop' | 'passthrough'
 
   /** Useful for pipelines and column transformers to have a default name for transforms */
-  name = 'columntransformer'
+  name = 'ColumnTransformer'
 
   constructor({
     transformers = [],

diff --git a/src/dummy/dummyClassifier.test.ts → src/dummy/DummyClassifier.test.ts b/src/dummy/dummyClassifier.test.ts → src/dummy/DummyClassifier.test.ts
@@ -1,4 +1,4 @@
-import { DummyClassifier } from './dummyClassifier'
+import { DummyClassifier } from './DummyClassifier'
 
 describe('DummyClassifier', function () {
   it('Use DummyClassifier on simple example (mostFrequent)', function () {
@@ -62,7 +62,7 @@ describe('DummyClassifier', function () {
     ]
     const y = [10, 20, 20, 30]
     const expectedResult = {
-      name: 'dummyclassifier',
+      name: 'DummyClassifier',
       EstimatorType: 'classifier',
       constant: 20,
       strategy: 'mostFrequent',

diff --git a/src/dummy/dummyClassifier.ts → src/dummy/DummyClassifier.ts b/src/dummy/dummyClassifier.ts → src/dummy/DummyClassifier.ts
@@ -85,7 +85,7 @@ export class DummyClassifier extends ClassifierMixin {
   classes: number[] | string[]
 
   /** Useful for pipelines and column transformers to have a default name for transforms */
-  name = 'dummyclassifier'
+  name = 'DummyClassifier'
 
   constructor({
     strategy = 'mostFrequent',

diff --git a/src/dummy/dummyRegressor.test.ts → src/dummy/DummyRegressor.test.ts b/src/dummy/dummyRegressor.test.ts → src/dummy/DummyRegressor.test.ts
@@ -1,4 +1,4 @@
-import { DummyRegressor } from './dummyRegressor'
+import { DummyRegressor } from './DummyRegressor'
 
 describe('DummyRegressor', function () {
   it('Use DummyRegressor on simple example (mean)', function () {
@@ -65,7 +65,7 @@ describe('DummyRegressor', function () {
     ]
     const y = [10, 12, 30]
     const saveResult = {
-      name: 'dummyregressor',
+      name: 'DummyRegressor',
       EstimatorType: 'regressor',
       strategy: 'constant',
       constant: 10

diff --git a/src/dummy/dummyRegressor.ts → src/dummy/DummyRegressor.ts b/src/dummy/dummyRegressor.ts → src/dummy/DummyRegressor.ts
@@ -84,7 +84,7 @@ export class DummyRegressor extends RegressorMixin {
   quantile?: number
 
   /** Useful for pipelines and column transformers to have a default name for transforms */
-  name = 'dummyregressor'
+  name = 'DummyRegressor'
 
   constructor({
     strategy = 'mean',

diff --git a/src/ensemble/votingClassifier.test.ts → src/ensemble/VotingClassifier.test.ts b/src/ensemble/votingClassifier.test.ts → src/ensemble/VotingClassifier.test.ts
@@ -1,7 +1,7 @@
-import { makeVotingClassifier, VotingClassifier } from './votingClassifier'
-import { DummyClassifier } from '../dummy/dummyClassifier'
+import { makeVotingClassifier, VotingClassifier } from './VotingClassifier'
+import { DummyClassifier } from '../dummy/DummyClassifier'
 
-import { LogisticRegression } from '../linear_model/logisticRegression'
+import { LogisticRegression } from '../linear_model/LogisticRegression'
 
 describe('VotingClassifier', function () {
   it('Use VotingClassifier on simple example (voting = hard)', async function () {

diff --git a/src/ensemble/votingClassifier.ts → src/ensemble/VotingClassifier.ts b/src/ensemble/votingClassifier.ts → src/ensemble/VotingClassifier.ts
@@ -1,7 +1,7 @@
 import { Scikit1D, Scikit2D } from '../types'
 import { tf } from '../shared/globals'
 import { ClassifierMixin } from '../mixins'
-import { LabelEncoder } from '../preprocessing/labelEncoder'
+import { LabelEncoder } from '../preprocessing/LabelEncoder'
 import { fromJson, toJson } from './serializeEnsemble'
 
 /*

diff --git a/src/ensemble/votingRegressor.test.ts → src/ensemble/VotingRegressor.test.ts b/src/ensemble/votingRegressor.test.ts → src/ensemble/VotingRegressor.test.ts
@@ -1,6 +1,6 @@
-import { makeVotingRegressor, VotingRegressor } from './votingRegressor'
-import { DummyRegressor } from '../dummy/dummyRegressor'
-import { LinearRegression } from '../linear_model/linearRegression'
+import { makeVotingRegressor, VotingRegressor } from './VotingRegressor'
+import { DummyRegressor } from '../dummy/DummyRegressor'
+import { LinearRegression } from '../linear_model/LinearRegression'
 
 describe('VotingRegressor', function () {
   it('Use VotingRegressor on simple example ', async function () {

diff --git a/src/ensemble/votingRegressor.ts → src/ensemble/VotingRegressor.ts b/src/ensemble/votingRegressor.ts → src/ensemble/VotingRegressor.ts
diff --git a/src/ensemble/serializeEnsemble.ts b/src/ensemble/serializeEnsemble.ts
@@ -1,34 +1,34 @@
-import { DummyClassifier } from '../dummy/dummyClassifier'
-import { DummyRegressor } from '../dummy/dummyRegressor'
-import { LogisticRegression } from '../linear_model/logisticRegression'
-import { RidgeRegression } from '../linear_model/ridgeRegression'
-import { LinearRegression } from '../linear_model/linearRegression'
-import { LassoRegression } from '../linear_model/lassoRegression'
-import { ElasticNet } from '../linear_model/elasticNet'
-import { LabelEncoder } from '../preprocessing/labelEncoder'
-import { SimpleImputer } from '../impute/simpleImputer'
+import { DummyClassifier } from '../dummy/DummyClassifier'
+import { DummyRegressor } from '../dummy/DummyRegressor'
+import { LogisticRegression } from '../linear_model/LogisticRegression'
+import { RidgeRegression } from '../linear_model/RidgeRegression'
+import { LinearRegression } from '../linear_model/LinearRegression'
+import { LassoRegression } from '../linear_model/LassoRegression'
+import { ElasticNet } from '../linear_model/ElasticNet'
+import { LabelEncoder } from '../preprocessing/LabelEncoder'
+import { SimpleImputer } from '../impute/SimpleImputer'
 import { tf } from '../shared/globals'
-import { MinMaxScaler } from '../preprocessing/minMaxScaler'
+import { MinMaxScaler } from '../preprocessing/MinMaxScaler'
 
 function getEstimator(name: string, serialJson: string) {
   switch (name) {
-    case 'dummyclassifier':
+    case 'DummyClassifier':
       return new DummyClassifier().fromJson(serialJson)
-    case 'dummyregressor':
+    case 'DummyRegressor':
       return new DummyRegressor().fromJson(serialJson)
-    case 'logisticregression':
+    case 'LogisticRegression':
       return new LogisticRegression().fromJson(serialJson)
-    case 'ridgeregression':
+    case 'RidgeRegression':
       return new RidgeRegression().fromJson(serialJson)
-    case 'linearregression':
+    case 'LinearRegression':
       return new LinearRegression().fromJson(serialJson)
-    case 'lassoregression':
+    case 'LassoRegression':
       return new LassoRegression().fromJson(serialJson)
-    case 'elasticnet':
+    case 'ElasticNet':
       return new ElasticNet().fromJson(serialJson)
-    case 'simpleimputer':
+    case 'SimpleImputer':
       return new SimpleImputer().fromJson(serialJson)
-    case 'minmaxscaler':
+    case 'MinMaxScaler':
       return new MinMaxScaler().fromJson(serialJson)
     default:
       throw new Error(`${name} estimator not supported`)

diff --git a/src/impute/simpleImputer.test.ts → src/impute/SimpleImputer.test.ts b/src/impute/simpleImputer.test.ts → src/impute/SimpleImputer.test.ts
@@ -1,5 +1,5 @@
 import { tf } from '../shared/globals'
-import { SimpleImputer } from './simpleImputer'
+import { SimpleImputer } from './SimpleImputer'
 
 describe('SimpleImputer', function () {
   it('Imputes with "constant" strategy 2D one column. In this strategy, we give the fill value', function () {
@@ -131,7 +131,7 @@ describe('SimpleImputer', function () {
     ]
 
     const expected = {
-      name: 'simpleimputer',
+      name: 'SimpleImputer',
       missingValues: null,
       strategy: 'mostFrequent',
       statistics: {

diff --git a/src/impute/simpleImputer.ts → src/impute/SimpleImputer.ts b/src/impute/simpleImputer.ts → src/impute/SimpleImputer.ts
@@ -66,7 +66,7 @@ export class SimpleImputer extends TransformerMixin {
   statistics: tf.Tensor1D
 
   /** Useful for pipelines and column transformers to have a default name for transforms */
-  name = 'simpleimputer'
+  name = 'SimpleImputer'
 
   constructor({
     strategy = 'mean',

diff --git a/src/index.test.ts b/src/index.test.ts
@@ -1,41 +1,41 @@
-import './cluster/kmeans.test'
-import './compose/columnTransformer.test'
+import './cluster/KMeans.test'
+import './compose/ColumnTransformer.test'
 import './datasets/makeRegression.test'
-import './dummy/dummyClassifier.test'
-import './dummy/dummyRegressor.test'
-import './ensemble/votingClassifier.test'
-import './ensemble/votingRegressor.test'
-import './impute/simpleImputer.test'
-import './linear_model/linearRegression.test'
+import './dummy/DummyClassifier.test'
+import './dummy/DummyRegressor.test'
+import './ensemble/VotingClassifier.test'
+import './ensemble/VotingRegressor.test'
+import './impute/SimpleImputer.test'
+import './linear_model/LinearRegression.test'
 /* When we figure out why we can't save / load logistic regressions */
 // import './linear_model/logisticRegression.test'
 import './metrics/metrics.test'
-// import './model_selection/kFold.test'
+// import './model_selection/KFold.test'
 import './model_selection/trainTestSplit.test'
-import './naive_bayes/gaussianNaiveBayes.test'
+import './naive_bayes/GaussianNB.test'
 /* When we figure out how to do expect.extend in karma */
 // import './neighbors/bruteNeighborhood.test'
 // import './neighbors/cappedMaxHeap.test'
 // import './neighbors/kdTree.test'
 // import './neighbors/kNeighborsClassifier.test'
 // import './neighbors/kNeighborsRegressor.test'
 // import './neighbors/metrics.test'
-import './pipeline/pipeline.test'
-import './preprocessing/labelEncoder.test'
-import './preprocessing/maxAbsScaler.test'
-import './preprocessing/minMaxScaler.test'
-import './preprocessing/normalizer.test'
-import './preprocessing/oneHotEncoder.test'
-import './preprocessing/ordinalEncoder.test'
-import './preprocessing/robustScaler.test'
-import './preprocessing/standardScaler.test'
-import './svm/linearSVC.test'
-import './svm/linearSVR.test'
+import './pipeline/Pipeline.test'
+import './preprocessing/LabelEncoder.test'
+import './preprocessing/MaxAbsScaler.test'
+import './preprocessing/MinMaxScaler.test'
+import './preprocessing/Normalizer.test'
+import './preprocessing/OneHotEncoder.test'
+import './preprocessing/OrdinalEncoder.test'
+import './preprocessing/RobustScaler.test'
+import './preprocessing/StandardScaler.test'
+import './svm/LinearSVC.test'
+import './svm/LinearSVR.test'
 /* When we put back in the SVM code */
 // import './svm/SVC.test'
 // import './svm/SVR.test'
-import './tree/criterion.test'
-// import './tree/decisiontree.test'
-import './tree/splitter.test'
+import './tree/Criterion.test'
+// import './tree/DecisionTree.test'
+import './tree/Splitter.test'
 /* When we use the expect.extend stuff */
 // import './jestTensorMatchers.test'
diff --git a/src/index.ts b/src/index.ts
@@ -12,74 +12,74 @@
 * limitations under the License.
 * ==========================================================================
 */
-export { KNeighborsRegressor } from './neighbors/kNeighborsRegressor'
+export { KNeighborsRegressor } from './neighbors/KNeighborsRegressor'
 export {
   LinearRegression,
   LinearRegressionParams
-} from './linear_model/linearRegression'
-export { LassoRegression, LassoParams } from './linear_model/lassoRegression'
+} from './linear_model/LinearRegression'
+export { LassoRegression, LassoParams } from './linear_model/LassoRegression'
 export {
   RidgeRegression,
   RidgeRegressionParams
-} from './linear_model/ridgeRegression'
-export { ElasticNet, ElasticNetParams } from './linear_model/elasticNet'
+} from './linear_model/RidgeRegression'
+export { ElasticNet, ElasticNetParams } from './linear_model/ElasticNet'
 export {
   LogisticRegression,
   LogisticRegressionParams
-} from './linear_model/logisticRegression'
+} from './linear_model/LogisticRegression'
 export * as metrics from './metrics/metrics'
-export { DummyRegressor, DummyRegressorParams } from './dummy/dummyRegressor'
+export { DummyRegressor, DummyRegressorParams } from './dummy/DummyRegressor'
 export {
   DummyClassifier,
   DummyClassifierParams
-} from './dummy/dummyClassifier'
-export { MinMaxScaler, MinMaxScalerParams } from './preprocessing/minMaxScaler'
+} from './dummy/DummyClassifier'
+export { MinMaxScaler, MinMaxScalerParams } from './preprocessing/MinMaxScaler'
 export {
   StandardScaler,
   StandardScalerParams
-} from './preprocessing/standardScaler'
-export { MaxAbsScaler } from './preprocessing/maxAbsScaler'
-export { SimpleImputer, SimpleImputerParams } from './impute/simpleImputer'
+} from './preprocessing/StandardScaler'
+export { MaxAbsScaler } from './preprocessing/MaxAbsScaler'
+export { SimpleImputer, SimpleImputerParams } from './impute/SimpleImputer'
 export {
   OneHotEncoder,
   OneHotEncoderParams
-} from './preprocessing/oneHotEncoder'
-export { LabelEncoder } from './preprocessing/labelEncoder'
+} from './preprocessing/OneHotEncoder'
+export { LabelEncoder } from './preprocessing/LabelEncoder'
 export {
   OrdinalEncoder,
   OrdinalEncoderParams
-} from './preprocessing/ordinalEncoder'
-export { Normalizer, NormalizerParams } from './preprocessing/normalizer'
-export { Pipeline, PipelineParams, makePipeline } from './pipeline/pipeline'
+} from './preprocessing/OrdinalEncoder'
+export { Normalizer, NormalizerParams } from './preprocessing/Normalizer'
+export { Pipeline, PipelineParams, makePipeline } from './pipeline/Pipeline'
 export {
   ColumnTransformer,
   ColumnTransformerParams
-} from './compose/columnTransformer'
-export { RobustScaler, RobustScalerParams } from './preprocessing/robustScaler'
-export { KMeans, KMeansParams } from './cluster/kmeans'
+} from './compose/ColumnTransformer'
+export { RobustScaler, RobustScalerParams } from './preprocessing/RobustScaler'
+export { KMeans, KMeansParams } from './cluster/KMeans'
 export { Scikit1D, Scikit2D, ScikitVecOrMatrix } from './types'
 export { dataUrls } from './datasets/datasets'
 export {
   makeVotingRegressor,
   VotingRegressor,
   VotingRegressorParams
-} from './ensemble/votingRegressor'
+} from './ensemble/VotingRegressor'
 export {
   makeVotingClassifier,
   VotingClassifier,
   VotingClassifierParams
-} from './ensemble/votingClassifier'
-export { LinearSVC, LinearSVCParams } from './svm/linearSVC'
-export { LinearSVR, LinearSVRParams } from './svm/linearSVR'
+} from './ensemble/VotingClassifier'
+export { LinearSVC, LinearSVCParams } from './svm/LinearSVC'
+export { LinearSVR, LinearSVRParams } from './svm/LinearSVR'
 
 // Comment these out until our libsvm version doesn't ship with fs / path subdependencies
 // They were stopping the browser build from being built
 // export { SVR, SVRParams } from './svm/SVR'
 // export { SVC, SVCParams } from './svm/SVC'
-export { GaussianNB } from './naive_bayes/gaussianNaiveBayes'
+export { GaussianNB } from './naive_bayes/GaussianNB'
 export {
   DecisionTreeClassifier,
   DecisionTreeClassifierParams,
   DecisionTreeRegressor,
   DecisionTreeRegressorParams
-} from './tree/decisiontree'
+} from './tree/DecisionTree'