Issue 159 add keras.sequential callbacks

Makefile

@@ -86,16 +86,13 @@ install-develop: clean-build clean-pyc ## install the package in editable mode a
 
 .PHONY: lint
 lint: ## check style with flake8 and isort
-	find pipelines -name '*.json' | xargs -n1 -I{} bash -c "diff -q {} <(python -m json.tool {})"
-	find mlprimitives/jsons -name '*.json' | xargs -n1 -I{} bash -c "diff -q {} <(python -m json.tool {})"
 	flake8 mlprimitives tests
 	isort -c --recursive mlprimitives tests
+	find pipelines -name '*.json' | xargs -n1 -I{} bash -c "diff -q {} <(python -m json.tool {})"
+	find mlprimitives/jsons -name '*.json' | xargs -n1 -I{} bash -c "diff -q {} <(python -m json.tool {})"
 
 .PHONY: fix-lint
 fix-lint: ## fix lint issues using autoflake, autopep8, and isort
-	find pipelines -name '*.json' | xargs -n1 -I{} bash -c "python -m json.tool {} {}.tmp && mv {}.tmp {}"
-	find mlprimitives/jsons -name '*.json' | xargs -n1 -I{} bash -c "python -m json.tool {} {}.tmp && mv {}.tmp {}"
-
 	find mlprimitives -name '*.py' | xargs autoflake --in-place --remove-all-unused-imports --remove-unused-variables
 	autopep8 --in-place --recursive --aggressive mlprimitives
 	isort --apply --atomic --recursive mlprimitives
@@ -104,6 +101,9 @@ fix-lint: ## fix lint issues using autoflake, autopep8, and isort
 	autopep8 --in-place --recursive --aggressive tests
 	isort --apply --atomic --recursive tests
 
+	find pipelines -name '*.json' | xargs -n1 -I{} bash -c "python -m json.tool {} {}.tmp && mv {}.tmp {}"
+	find mlprimitives/jsons -name '*.json' | xargs -n1 -I{} bash -c "python -m json.tool {} {}.tmp && mv {}.tmp {}"
+
 
 # TEST TARGETS
 

mlprimitives/adapters/keras.py

@@ -50,8 +50,9 @@ def _build_model(self, **kwargs):
 
         return model
 
-    def __init__(self, layers, loss, optimizer, classification,
-                 metrics=None, epochs=10, verbose=False, **hyperparameters):
+    def __init__(self, layers, loss, optimizer, classification, callbacks=tuple(),
+                 metrics=None, epochs=10, verbose=False, validation_split=0, batch_size=32,
+                 shuffle=True, **hyperparameters):
 
         self.layers = list()
         for layer in layers:
@@ -67,17 +68,32 @@ def __init__(self, layers, loss, optimizer, classification,
         self.verbose = verbose
         self.classification = classification
         self.hyperparameters = hyperparameters
+        self.validation_split = validation_split
+        self.batch_size = batch_size
+        self.shuffle = shuffle
+
+        for callback in callbacks:
+            callback['class'] = import_object(callback['class'])
+
+        self.callbacks = callbacks
 
     def fit(self, X, y, **kwargs):
         self.model = self._build_model(**kwargs)
 
         if self.classification:
             y = keras.utils.to_categorical(y)
 
-        self.model.fit(X, y, epochs=self.epochs, verbose=self.verbose)
+        callbacks = [
+            callback['class'](**callback.get('args', dict()))
+            for callback in self.callbacks
+        ]
+
+        self.model.fit(X, y, epochs=self.epochs, verbose=self.verbose, callbacks=callbacks,
+                       validation_split=self.validation_split, batch_size=self.batch_size,
+                       shuffle=self.shuffle)
 
     def predict(self, X):
-        y = self.model.predict(X)
+        y = self.model.predict(X, batch_size=self.batch_size, verbose=self.verbose)
 
         if self.classification:
             y = np.argmax(y, axis=1)

mlprimitives/cli.py

@@ -17,7 +17,7 @@
 
 def _logging_setup(verbosity=1):
     logger = logging.getLogger()
-    log_level = (3 - verbosity) * 10
+    log_level = (4 - verbosity) * 10
     fmt = '%(asctime)s - %(levelname)s - %(message)s'
     formatter = logging.Formatter(fmt)
     logger.setLevel(log_level)

mlprimitives/jsons/keras.Sequential.LSTMTextClassifier.json

@@ -59,6 +59,10 @@
                 "type": "bool",
                 "default": true
             },
+            "verbose": {
+                "type": "bool",
+                "default": false
+            },
             "conv_activation": {
                 "type": "str",
                 "default": "relu"
@@ -85,6 +89,18 @@
                 "type": "int",
                 "default": 10
             },
+            "callbacks": {
+                "type": "list",
+                "default": []
+            },
+            "validation_split": {
+                "type": "float",
+                "default": 0.0
+            },
+            "bastch_size": {
+                "type": "int",
+                "default": 32
+            },
             "layers": {
                 "type": "list",
                 "default": [

pipelines/keras.Sequential.LSTMTextClassifier.json

@@ -0,0 +1,57 @@
+{
+    "metadata": {
+        "name": "keras.Sequential.LSTMTextClassifier",
+        "data_type": "text",
+        "task_type": "classification"
+    },
+    "validation": {
+        "dataset": "newsgroups"
+    },
+    "primitives": [
+        "mlprimitives.custom.counters.UniqueCounter",
+        "mlprimitives.custom.text.TextCleaner",
+        "mlprimitives.custom.counters.VocabularyCounter",
+        "keras.preprocessing.text.Tokenizer",
+        "keras.preprocessing.sequence.pad_sequences",
+        "keras.Sequential.LSTMTextClassifier"
+    ],
+    "input_names": {
+        "mlprimitives.custom.counters.UniqueCounter#1": {
+            "X": "y"
+        }
+    },
+    "output_names": {
+        "mlprimitives.custom.counters.UniqueCounter#1": {
+            "counts": "classes"
+        },
+        "mlprimitives.custom.counters.VocabularyCounter#1": {
+            "counts": "vocabulary_size"
+        }
+    },
+    "init_params": {
+        "mlprimitives.custom.counters.VocabularyCounter#1": {
+            "add": 1
+        },
+        "mlprimitives.custom.text.TextCleaner#1": {
+            "language": "en"
+        },
+        "keras.preprocessing.sequence.pad_sequences#1": {
+            "maxlen": 100
+        },
+        "keras.Sequential.LSTMTextClassifier#1": {
+            "input_length": 100,
+            "verbose": true,
+            "validation_split": 0.2,
+            "callbacks": [
+                {
+                    "class": "keras.callbacks.EarlyStopping",
+                    "args": {
+                        "monitor": "val_acc",
+                        "patience": 1,
+                        "min_delta": 0.01
+                    }
+                }
+            ]
+        }
+    }
+}