From 2da7b3ab43b82bcf08afc3ad6b8c4fb4de294be9 Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Tue, 11 Oct 2022 20:01:36 -0400 Subject: [PATCH] Fixes learning rate and momentum definitions (#1498) * Fixes learning rate and momentum definitions Signed-off-by: Fanit Kolchina * Update algorithms.md * Makes epsilon, beta1, beta2 and decayRate definitions uniform Signed-off-by: Fanit Kolchina Signed-off-by: Fanit Kolchina --- _ml-commons-plugin/algorithms.md | 88 ++++++++++++++++---------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/_ml-commons-plugin/algorithms.md b/_ml-commons-plugin/algorithms.md index cab6595a..685b779e 100644 --- a/_ml-commons-plugin/algorithms.md +++ b/_ml-commons-plugin/algorithms.md @@ -65,14 +65,14 @@ Linear regression maps the linear relationship between inputs and outputs. In ML Parameter | Type | Description | Default Value :--- |:--- | :--- | :--- -learningRate | Double | The rate of speed at which the gradient moves during descent | 0.01 -momentumFactor | Double | The medium-term from which the regressor rises or falls | 0 -epsilon | Double | The criteria used to identify a linear model | 1.00E-06 -beta1 | Double | The estimated exponential decay for the moment | 0.9 -beta2 | Double | The estimated exponential decay for the moment | 0.99 -decayRate | Double | The rate at which the model decays exponentially | 0.9 -momentumType | MomentumType | The defined Stochastic Gradient Descent (SDG) momentum type that helps accelerate gradient vectors in the right directions, leading to a fast convergence| STANDARD -optimizerType | OptimizerType | The optimizer used in the model | SIMPLE_SGD +learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value. | 0.01 +momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | 0 +epsilon | Double | The value for stabilizing gradient inversion. | 1.00E-06 +beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 +beta2 | Double | The exponential decay rates for the moment estimates. | 0.99 +decayRate | Double | The Root Mean Squared Propagation (RMSProp). | 0.9 +momentumType | MomentumType | The defined Stochastic Gradient Descent (SGD) momentum type that helps accelerate gradient vectors in the right directions, leading to a fast convergence.| STANDARD +optimizerType | OptimizerType | The optimizer used in the model. | SIMPLE_SGD ### APIs @@ -164,11 +164,11 @@ ML Commons only supports the linear Stochastic gradient trainer or optimizer, wh Parameter | Type | Description | Default Value :--- |:--- | :--- | :--- -number_of_trees | integer | The number of trees in the forest | 30 -sample_size | integer | The same size used by the stream samplers in the forest | 256 -output_after | integer | The number of points required by stream samplers before results return | 32 -training_data_size | integer | The size of your training data | Dataset size -anomaly_score_threshold | double | The threshold of the anomaly score | 1.0 +number_of_trees | integer | The number of trees in the forest. | 30 +sample_size | integer | The same size used by the stream samplers in the forest. | 256 +output_after | integer | The number of points required by stream samplers before results return. | 32 +training_data_size | integer | The size of your training data. | Dataset size +anomaly_score_threshold | double | The threshold of the anomaly score. | 1.0 #### Fit RCF @@ -176,15 +176,15 @@ All parameters are optional except `time_field`. Parameter | Type | Description | Default Value :--- |:--- | :--- | :--- -number_of_trees | integer | The number of trees in the forest | 30 -shingle_size | integer | A shingle, or a consecutive sequence of the most recent records | 8 -sample_size | integer | The sample size used by stream samplers in the forest | 256 -output_after | integer | The number of points required by stream samplers before results return | 32 -time_decay | double | The decay factor used by stream samplers in the forest | 0.0001 -anomaly_rate | double | The anomaly rate | 0.005 -time_field | string | (**Required**) The time filed for RCF to use as time series data | N/A -date_format | string | The date and time format for the time_field field | "yyyy-MM-ddHH:mm:ss" -time_zone | string | The time zone for the time_field field | "UTC" +number_of_trees | integer | The number of trees in the forest. | 30 +shingle_size | integer | A shingle, or a consecutive sequence of the most recent records. | 8 +sample_size | integer | The sample size used by stream samplers in the forest. | 256 +output_after | integer | The number of points required by stream samplers before results return. | 32 +time_decay | double | The decay factor used by stream samplers in the forest. | 0.0001 +anomaly_rate | double | The anomaly rate. | 0.005 +time_field | string | (**Required**) The time field for RCF to use as time series data. | N/A +date_format | string | The date and time format for the `time_field` field. | "yyyy-MM-ddHH:mm:ss" +time_zone | string | The time zone for the `time_field` field. | "UTC" ### APIs @@ -206,8 +206,8 @@ RCFSummarize is a clustering algorithm based on the Clustering Using REpresentat | Parameter | Type | Description | Default Value | |---|---|---|---| -| max_k | integer | The max allowed number of centroids | 2 | -| distance_type | enum, such as `EUCLIDEAN`, `L1`, `L2`, or `LInfinity` | The type of measurement used to measure the distance between centroids | EUCLIDEAN | +| max_k | integer | The max allowed number of centroids. | 2 | +| distance_type | enum, such as `EUCLIDEAN`, `L1`, `L2`, or `LInfinity` | The type of measurement used to measure the distance between centroids. | EUCLIDEAN | ### APIs @@ -295,16 +295,16 @@ All parameters are required except `filter_query` and `anomaly_start`. Parameter | Type | Description | Default Value :--- | :--- | :--- | :--- -index_name | String | The data collection to analyze | N/A -attribute_field_names | List | The fields for entity keys | N/A -aggregations | List | The fields and aggregation for values | N/A -time_field_name | String | The timestamp field | null -start_time | Long | The beginning of the time range | 0 -end_time | Long | The end of the time range | 0 -min_time_interval | Long | The minimum time interval/scale for analysis | 0 -num_outputs | integer | The maximum number of values from localization/slicing | 0 -filter_query | Long | (Optional) Reduces the collection of data for analysis | Optional.empty() -anomaly_star | QueryBuilder | (Optional) The time after which the data will be analyzed | Optional.empty() +index_name | String | The data collection to analyze. | N/A +attribute_field_names | List | The fields for entity keys. | N/A +aggregations | List | The fields and aggregation for values. | N/A +time_field_name | String | The timestamp field. | null +start_time | Long | The beginning of the time range. | 0 +end_time | Long | The end of the time range. | 0 +min_time_interval | Long | The minimum time interval/scale for analysis. | 0 +num_outputs | integer | The maximum number of values from localization/slicing. | 0 +filter_query | Long | (Optional) Reduces the collection of data for analysis. | Optional.empty() +anomaly_star | QueryBuilder | (Optional) The time after which the data will be analyzed. | Optional.empty() ### Example: Execute localization @@ -415,16 +415,16 @@ A classification algorithm, logistic regression models the probability of a disc |---|---|---|---| | learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value | 1 | | momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | 0 | -| epsilon | Double | The value for stabilizing gradient inversion | 0.1 | -| beta1 | Double | The exponential decay rates for the moment estimates | 0.9 | -| beta2 | Double | The exponential decay rates for the moment estimates | 0.99 | -| decayRate | Double | The Root Mean Squared Propagation (RMSProp) | 0.9 | -| momentumType | MomentumType | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors | STANDARD | -| optimizerType | OptimizerType | The optimizer used in the model | AdaGrad | -| target | String | The target field | null | -| objectiveType | ObjectiveType | The objective function type | LogMulticlass | -| epochs | Integer | The number of iterations | 5 | -| batchSize | Integer | The size of minbatches | 1 | +| epsilon | Double | The value for stabilizing gradient inversion. | 0.1 | +| beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 | +| beta2 | Double | The exponential decay rates for the moment estimates. | 0.99 | +| decayRate | Double | The Root Mean Squared Propagation (RMSProp). | 0.9 | +| momentumType | MomentumType | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | STANDARD | +| optimizerType | OptimizerType | The optimizer used in the model. | AdaGrad | +| target | String | The target field. | null | +| objectiveType | ObjectiveType | The objective function type. | LogMulticlass | +| epochs | Integer | The number of iterations. | 5 | +| batchSize | Integer | The size of minbatches. | 1 | | loggingInterval | Integer | The interval of logs lost after many iterations. The interval is `1` if the algorithm contains no logs. | 1000 | ### APIs