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Fixes learning rate and momentum definitions (#1498) 

* Fixes learning rate and momentum definitions

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

* Update algorithms.md

* Makes epsilon, beta1, beta2 and decayRate definitions uniform

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>

Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
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_ml-commons-plugin/algorithms.md
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@ -65,14 +65,14 @@ Linear regression maps the linear relationship between inputs and outputs. In ML
Parameter | Type | Description | Default Value Parameter | Type | Description | Default Value
:--- |:--- | :--- | :--- :--- |:--- | :--- | :---
learningRate | Double | The rate of speed at which the gradient moves during descent | 0.01 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 medium-term from which the regressor rises or falls | 0 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 criteria used to identify a linear model | 1.00E-06 epsilon | Double | The value for stabilizing gradient inversion. | 1.00E-06
beta1 | Double | The estimated exponential decay for the moment | 0.9 beta1 | Double | The exponential decay rates for the moment estimates. | 0.9
beta2 | Double | The estimated exponential decay for the moment | 0.99 beta2 | Double | The exponential decay rates for the moment estimates. | 0.99
decayRate | Double | The rate at which the model decays exponentially | 0.9 decayRate | Double | The Root Mean Squared Propagation (RMSProp). | 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 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 optimizerType | OptimizerType | The optimizer used in the model. | SIMPLE_SGD
### APIs ### APIs
@ -164,11 +164,11 @@ ML Commons only supports the linear Stochastic gradient trainer or optimizer, wh
Parameter | Type | Description | Default Value Parameter | Type | Description | Default Value
:--- |:--- | :--- | :--- :--- |:--- | :--- | :---
number_of_trees | integer | The number of trees in the forest | 30 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 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 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 training_data_size | integer | The size of your training data. | Dataset size
anomaly_score_threshold | double | The threshold of the anomaly score | 1.0 anomaly_score_threshold | double | The threshold of the anomaly score. | 1.0
#### Fit RCF #### Fit RCF
@ -176,15 +176,15 @@ All parameters are optional except `time_field`.
Parameter | Type | Description | Default Value Parameter | Type | Description | Default Value
:--- |:--- | :--- | :--- :--- |:--- | :--- | :---
number_of_trees | integer | The number of trees in the forest | 30 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 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 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 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 time_decay | double | The decay factor used by stream samplers in the forest. | 0.0001
anomaly_rate | double | The anomaly rate | 0.005 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 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" 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" time_zone | string | The time zone for the `time_field` field. | "UTC"
### APIs ### APIs
@ -206,8 +206,8 @@ RCFSummarize is a clustering algorithm based on the Clustering Using REpresentat
| Parameter | Type | Description | Default Value | | Parameter | Type | Description | Default Value |
|---|---|---|---| |---|---|---|---|
| max_k | integer | The max allowed number of centroids | 2 | | 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 | | distance_type | enum, such as `EUCLIDEAN`, `L1`, `L2`, or `LInfinity` | The type of measurement used to measure the distance between centroids. | EUCLIDEAN |
### APIs ### APIs
@ -295,16 +295,16 @@ All parameters are required except `filter_query` and `anomaly_start`.
Parameter | Type | Description | Default Value Parameter | Type | Description | Default Value
:--- | :--- | :--- | :--- :--- | :--- | :--- | :---
index_name | String | The data collection to analyze | N/A index_name | String | The data collection to analyze. | N/A
attribute_field_names | List<String> | The fields for entity keys | N/A attribute_field_names | List<String> | The fields for entity keys. | N/A
aggregations | List<AggregationBuilder> | The fields and aggregation for values | N/A aggregations | List<AggregationBuilder> | The fields and aggregation for values. | N/A
time_field_name | String | The timestamp field | null time_field_name | String | The timestamp field. | null
start_time | Long | The beginning of the time range | 0 start_time | Long | The beginning of the time range. | 0
end_time | Long | The end 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 min_time_interval | Long | The minimum time interval/scale for analysis. | 0
num_outputs | integer | The maximum number of values from localization/slicing | 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() 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() anomaly_star | QueryBuilder | (Optional) The time after which the data will be analyzed. | Optional.empty()
### Example: Execute localization ### 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 | | 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 | | 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 | | epsilon | Double | The value for stabilizing gradient inversion. | 0.1 |
| beta1 | Double | The exponential decay rates for the moment estimates | 0.9 | | beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 |
| beta2 | Double | The exponential decay rates for the moment estimates | 0.99 | | beta2 | Double | The exponential decay rates for the moment estimates. | 0.99 |
| decayRate | Double | The Root Mean Squared Propagation (RMSProp) | 0.9 | | 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 | | 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 | | optimizerType | OptimizerType | The optimizer used in the model. | AdaGrad |
| target | String | The target field | null | | target | String | The target field. | null |
| objectiveType | ObjectiveType | The objective function type | LogMulticlass | | objectiveType | ObjectiveType | The objective function type. | LogMulticlass |
| epochs | Integer | The number of iterations | 5 | | epochs | Integer | The number of iterations. | 5 |
| batchSize | Integer | The size of minbatches | 1 | | 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 | | loggingInterval | Integer | The interval of logs lost after many iterations. The interval is `1` if the algorithm contains no logs. | 1000 |
### APIs ### APIs