Notice RedisML is planned to be replaced by RedisAI, adding support for deep learning.
RedisML is a Redis module that implements several machine learning models as Redis data types.
The stored models are fully operational and support the prediction/evaluation process.
RedisML is a turnkey solution for using trained models in a production environment. Load ML models from any platform, immediately ready to serve.
See Full Documentation at http://redisml.io
- Decision Tree ensembles (random forests) classification and regression
- Linear regression
- Logistic regression
- Matrix operations
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Build a Redis server with support for modules (currently available from the unstable branch).
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You'll also need a BLAS library such as ATLAS. To install ATLAS:
- Ubuntu:Â
sudo apt-get install libatlas-base-dev
- CentOS/RHEL/Fedora:Â
sudo yum install -y atlas-devel atlas-static ln -s /usr/lib64/atlas/libatlas.a /usr/lib64/libatlas.a ln -s /usr/lib64/atlas/libtatlas.so /usr/lib64/libcblas.a
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Build the RedisML module:
git clone https://github.com/RedisLabsModules/redisml.git cd redisml/src make -
To load the module, start Redis with theÂ
--loadmodule /path/to/redisml/src/redis-ml.so option, add it as a directive to the configuration file or send aÂMODULE LOAD command.
The following code creates a random forest under the key myforest that consists of three trees with IDs ranging from 0 to 2, where each consists of a single numeric splitter and its predicate values. Afterwards, the forest is used to classify two inputs and yield their predictions.
redis> ML.FOREST.ADD myforest 0 . NUMERIC 1 0.1 .l LEAF 1 .r LEAF 0
OK
redis> ML.FOREST.ADD myforest 1 . NUMERIC 1 0.1 .l LEAF 1 .r LEAF 0
OK
redis> ML.FOREST.ADD myforest 2 . NUMERIC 1 0.1 .l LEAF 0 .r LEAF 1
OK
redis> ML.FOREST.RUN myforest 1:0.01 CLASSIFICATION
"1"
redis> ML.FOREST.RUN myforest 1:0.2 CLASSIFICATION
"0"
Available since 1.0.0.
Time complexity: O(M*log(N)) where N is the tree's depth and M is the number of nodes added
ML.FOREST.ADD key tree path ((NUMERIC|CATEGORIC) attr val | LEAF val [STATS]) [...]
Add nodes to a tree in the forest.
This command adds one or more nodes to the tree in the forest that's stored under key. Trees are identified by numeric IDs, treeid, that must begin at 0 and be incremented by exactly 1 for each new tree.
Each of the nodes is described by its path and definition. The path argument is the path from the tree's root to the node. A valid path always starts with the period character (.), which denotes the root. Optionally, the root may be followed by left or right branches, denoted by the characters l and r, respectively. For example, the path ".lr" refers to the right child of the root's left child.
A node in the decision tree can either be a splitter or a terminal leaf. Splitter nodes are either numerical or categorical, and are added using the NUMERIC or CATEGORIC keywords. Splitter nodes also require specifying the examined attribute (attr) as well as the value (val) used in the comparison made during the branching decision. val is expected to be a double-precision floating point value for numerical splitters, and a string for categorical splitter nodes.
The leaves are created with the LEAF keyword and only require specifying their double-precision floating point value (val).
Simple string reply
Available since 1.0.0.
Time complexity: O(M*log(N)) where N is the depth of the trees and M is the number of trees in the forest
ML.FOREST.RUN key sample (CLASSIFICATION|REGRESSION)
Predicts the classified (discrete) or regressed (continuous) value of a sample using the forest.
The forest that's stored in key is used for generating the predicted value for the sample. The sample is given as a string that is a vector of attribute-value pairs in the format of attr:val. For example, the sample "gender:male" has a single attribute, gender, whose value is male. A sample may have multiple such attribute-value pairs, and these must be comma-separated (,) in the string vector. For example, a sample of a 25-years-old male is expressed as "gender:male,age:25".
Bulk string reply: the predicted value of the sample
The first line of the example shows how a linear regression predictor is set to the key named linear. The predictor has an intercept of 2 and its coefficients are 3, 4 and 5. Once the predictor is ready, it is used to predict the result given the independent variables' values (features) of 1, 1 and 1.
redis> ML.LINREG.SET linear 2 3 4 5
OK
redis> ML.LINREG.PREDICT linear 1 1 1
"14"
Available since 1.0.0.
Time complexity: O(N) where N is the number of coefficients
ML.LINREG.SET key intercept coefficient [...]
Sets a linear regression predictor.
This command creates or updates the linear regression predictor that's stored in key. The predictor's intercept is specified by intercept, followed by one or more coefficient arguments of the independent variables.
Simple string reply
Available since 1.0.0.
Time complexity: O(N) where N is the number of features
ML.LINREG.PREDICT key feature [...]
Predicts the result for a set of features.
The linear regression predictor stored in key is used for predicting the result based on one or more features that are given by the feature argument(s).
Bulk string reply: the predicted result for the feature set
In this example, the first line shows how a logistic regression predictor is set to the key named logistic. The predictor has an intercept of 0 and its coefficients are 2 and 2. Once the predictor is ready, it is used to predict the result given the independent variables' values (features) of -3 and 1.
redis> ML.LOGREG.SET logistic 0 2 2
OK
redis> ML.LOGREG.PREDICT logistic -3 1
"0.017986209962091559"
Available since 1.0.0.
Time complexity: O(N) where N is the number of coefficients
ML.LOGREG.SET key intercept coefficient [...]
Sets a linear regression predictor.
This command sets or updates the logistic regression predictor that's stored in key. The predictor's intercept is specified by intercept, followed by one or more coefficient arguments of the independent variables.
Simple string reply
Available since 1.0.0.
Time complexity: O(N) where N is the number of features
ML.LOGREG.PREDICT key feature [...]
Predicts the result for a set of features.
The logistic regression predictor stored in key is used for predicting the result based on one or more features that are given by the feature argument(s).
Bulk string reply: the predicted result for the feature set
The following example shows how to set two matrices, a and b, multiply them, and store the result in the matrix ab. Lastly, the contents of ab are fetched.
redis> ML.MATRIX.SET a 2 3 1 2 5 3 4 6
OK
redis> ML.MATRIX.SET b 3 2 1 2 3 4 7 1
OK
redis> ML.MATRIX.MULTIPLY a b ab
OK
redis> ML.MATRIX.GET ab
1) (integer) 2
2) (integer) 2
3) "42"
4) "15"
5) "57"
6) "28"
Available since 1.0.0.
Time complexity: O(N*M) where N is the number of rows and M is the number of columns
ML.MATRIX.SET key n m entry11 .. entrynm
Sets a matrix.
Sets key to store a matrix of n rows,m columns and double-precision float entries ranging from entry11 to entrynm.
Simple string reply
Available since 1.0.0.
Time complexity: O(N*M) where N is the number of rows and M is the number of columns
ML.MATRIX.GET key
Get a matrix. Returns the matrix's dimensions and entries.
The first two elements in the returned array are the matrix's rows and columns, respectively, followed by the entries.
Available since 1.0.0.
Time complexity: O(N*M) where N is the number of rows and M is the number of columns
ML.MATRIX.ADD matrix1 matrix2 sum
Adds matrices.
The result of adding the two matrices stored in matrix1 and matrix2 is set in sum.
Simple string reply
Available since 1.0.0.
Time complexity: O(N*M*P) where N and M are numbers of rows and columns inmatrix1, and P is the number of columns inmatrix2
ML.MATRIX.MULTIPLY matrix1 matrix2 product
Multiplies matrices.
The result of multiplying the two matrices stored in matrix1 and matrix2 is set in product.
Simple string reply
Available since 1.0.0.
Time complexity: O(N*M) where N is the number of rows and M is the number of columns
ML.MATRIX.SCALE key scalar
Scales a matrix.
Updates the entries of the matrix stored in key by multiplying them with scalar.
Simple string reply
Setting up a K-means model in key k with 2 clusters and 3 dimensions. The cluster centers are 1, 1, 2 and 2, 5, 4:
redis> ML.KMEANS.SET k 2 3 1 1 2 2 5 4
OK
Predicting the cluster of feature vector 1, 3, 5:
redis> ML.KMEANS.predict k 1 3 5
(integer) 1
Available since 1.0.0.
Time complexity: O(N) where N is the number of coefficients
ML.KMEANS.SET key k dimensions centers [...]
Create/update a K-means model.
This command creates or updates the K-means model that's stored in key. The number of classes is specified by k, the number of features is set by dimensions .
Simple string reply
Available since 1.0.0.
Time complexity: O(N) where N is the number of features
ML.KMEANS.PREDICT key feature [...]
Predicts the result for a set of features.
The K-means model stored in key is used for predicting the result based on one or more features that are given by the feature argument(s).
Integer reply: the predicted result for the feature set
Issue reports, pull and feature requests are welcome.
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