feat - add an example for logistic regression

examples/pca_logistic.nni.yml

@@ -0,0 +1,56 @@
+sknni: 0.1
+
+# Datasource is how you specify which callable
+# sknni will invoke to get the data
+dataSource:
+    reader: sknni.datasource.NpzClassificationSource
+    params:
+        # Note - dir_path is a parameter name for NpzClassificationSource
+        # if you create another datasource then specify the arguments of the callable
+        # as per your datasource
+        dir_path: /Users/ksachdeva/Desktop/Dev/myoss/scikit-nni/examples/data/multiclass-classification
+
+# This is how you specify a pipeline and steps
+# In this example there are 3 steps - normalizer, pca and logistic regression
+# Please note that you would have to specify the fully qualified name for the classes
+sklearnPipeline:
+    name: normalizer_svc
+    steps:
+        normalizer: sklearn.preprocessing.Normalizer
+        pca: sklearn.decomposition.PCA
+        logistic_regression: sklearn.linear_model.LogisticRegression
+
+# This section is more or less compliant with the NNI's
+# way of specifying the hyper parameters except that you
+# should specify them with their corresponding step using the
+# same name as you used earlier in the pipeline definition
+nniConfigSearchSpace:
+    # do not need any params for normalizer as default is L2
+    # and for this classification task L2 is what is needed
+    - pca:
+        n_components:
+            _type: choice
+            _value: [64,128]
+    - logistic_regression:
+        C:
+            _type: uniform
+            _value: [0.1,1]
+
+
+# This is exactly same as the one that of NNI
+# except that you do not have to specify the command
+# and code fields. They are automatically added by the sknni generator
+nniConfig:
+    authorName: ksachdeva
+    experimentName: pca-logistic-regression
+    trialConcurrency: 1
+    maxExecDuration: 1h
+    maxTrialNum: 100
+    trainingServicePlatform: local
+    useAnnotation: false
+    tuner:
+        builtinTunerName: TPE
+        classArgs:
+            optimize_mode: maximize
+    trial:
+        gpuNum: 0

examples/pca_svc.nni.yml

@@ -11,7 +11,7 @@ dataSource:
         dir_path: /Users/ksachdeva/Desktop/Dev/myoss/scikit-nni/examples/data/multiclass-classification
 
 # This is how you specify a pipeline and steps
-# In this example there are two steps - normalizer and svc
+# In this example there are 3 steps - normalizer, pca and svc
 # Please note that you would have to specify the fully qualified name for the classes
 sklearnPipeline:
     name: normalizer_svc