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Add epochs, dropout rate, learning rate options to MLP and example code #10

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Add epochs, dropout rate, learning rate options to MLP and example code #10

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113 changes: 66 additions & 47 deletions src/scicloj/ml/smile/mlp.clj
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Original file line number Diff line number Diff line change
Expand Up @@ -8,36 +8,39 @@
[tech.v3.datatype.errors :as errors]

[scicloj.ml.smile.classification :as classification])
(:import smile.classification.MLP
[smile.base.mlp HiddenLayerBuilder OutputLayerBuilder ActivationFunction OutputFunction Cost LayerBuilder]))
(:import
smile.classification.MLP
[smile.base.mlp HiddenLayerBuilder OutputLayerBuilder ActivationFunction OutputFunction Cost LayerBuilder]
smile.math.TimeFunction))


(defn train [feature-ds target-ds options]

"Training function of MLP model. "

(defn train
"Training function of MLP model."
[feature-ds target-ds {:keys [dropout-rate epochs learning-rate]
:or {epochs 1}
:as options}]
(let [mlp (MLP. (ds/column-count feature-ds)
(into-array LayerBuilder (:layer-builders options)))
;;

train-data (into-array
(map
double-array
(ds/value-reader feature-ds)))
y (int-array (seq (get target-ds (first (ds-mod/inference-target-column-names target-ds)))))])
(.update mlp train-data y)
mlp)
y (int-array (seq (get target-ds (first (ds-mod/inference-target-column-names target-ds)))))]
(when (number? dropout-rate)
(.setMomentum mlp (TimeFunction/constant (- 1.0 dropout-rate))))
(when (number? learning-rate)
(.setLearningRate mlp (TimeFunction/constant learning-rate)))
(doseq [x (range epochs)]
(println (format "Training...%d of %d " x epochs))
(.update mlp train-data y))
mlp))






(defn predict
"Predict function for MLP model"
[feature-ds thawed-model {:keys [target-columns
target-categorical-maps
options]}]
_options]}]
(errors/when-not-error target-categorical-maps "target-categorical-maps not found. Target column need to be categorical.")
(let [
target-colname (first target-columns)
Expand All @@ -46,7 +49,7 @@
count)
_ (errors/when-not-error (pos? n-labels) "n-labels equals 0. Something is wrong with the :lookup-table")

predictions (classification/double-array-predict-posterior (:model-data model) feature-ds {} n-labels)
predictions (classification/double-array-predict-posterior (:model-data thawed-model) feature-ds {} n-labels)
finalised-predictions
(-> predictions
(dtt/->tensor)
Expand All @@ -57,26 +60,33 @@
(-> (ds-mod/probability-distributions->label-column finalised-predictions target-colname)
(ds/update-column target-colname
#(vary-meta % assoc :column-type :prediction)))]

mapped-predictions))


(ml/define-model!
:smile.classification/mlp
train
predict
{
:options [{:name :layer-builders
:type :seq
:default []
:description "Sequence of type smile.base.mlp.LayerBuilder describing the layers of the neural network "}]


:documentation {:javadoc "https://haifengl.github.io/api/java/smile/classification/MLP.html"
{:options [{:name :layer-builders
:type :seq
:default []
:description "Sequence of type smile.base.mlp.LayerBuilder describing the layers of the neural network "}
{:name :dropout-rate
:type :float
:default 0.0
:description "Inverse of the Momentum, a simplified variant of Weight Decay."}
{:name :learning-rate
:type :float
:default 0.1
:description "The learning rate"}
{:name :epochs
:type :integer
:default 1
:description "The number of training cycles/weight adjustments to run when calling train."}]

:documentation {:javadoc "https://haifengl.github.io/api/java/smile/classification/MLP.html"
:user-guide "https://haifengl.github.io/classification.html#neural-network"}})




(comment
(do
Expand All @@ -85,23 +95,32 @@
(require '[scicloj.metamorph.ml.loss :as loss])

(def hidden-layer-builder
(HiddenLayerBuilder. 1 (ActivationFunction/linear)))

(def output-layer-builder
(OutputLayerBuilder. 3 (OutputFunction/LINEAR) (Cost/MEAN_SQUARED_ERROR)))

(def src-ds (ds/->dataset "test/data/iris.csv"))
(def ds (-> src-ds
(ds/categorical->number cf/categorical)
(ds-mod/set-inference-target "species")))
(def feature-ds (cf/feature ds))
(def split-data (ds-mod/train-test-split ds))
(def train-ds (:train-ds split-data))
(def test-ds (:test-ds split-data))
(def model (ml/train train-ds {:model-type :smile.classification/mlp
:layer-builders [hidden-layer-builder output-layer-builder]}))
(def prediction
(-> (ml/predict test-ds model)
(ds-cat/reverse-map-categorical-xforms))))

(HiddenLayerBuilder. 1 (ActivationFunction/linear)))

(def output-layer-builder
(OutputLayerBuilder. 3 (OutputFunction/LINEAR) (Cost/MEAN_SQUARED_ERROR)))
;; or, for softmax
(comment
(OutputLayerBuilder. 3 (OutputFunction/SOFTMAX) (Cost/LIKELIHOOD)))

(def dropout-rate 0.45) ;; 0.0 - 1.0, default 0.0 (no momentum)
(def learning-rate 0.1) ;; 0.0 - 1.0
(def epochs 200)

(def src-ds (ds/->dataset "test/data/iris.csv"))
(def ds (-> src-ds
(ds/categorical->number cf/categorical)
(ds-mod/set-inference-target "species")))
(def feature-ds (cf/feature ds))
(def split-data (ds-mod/train-test-split ds))
(def train-ds (:train-ds split-data))
(def test-ds (:test-ds split-data))
(def model (ml/train train-ds {:model-type :smile.classification/mlp
:dropout-rate dropout-rate
:learning-rate learning-rate
:epochs epochs
:layer-builders [hidden-layer-builder output-layer-builder]}))
(def prediction
(-> (ml/predict test-ds model)
(ds-cat/reverse-map-categorical-xforms))))
:ok)