Fix arguments handling in Python

mwydmuch · Sep 7, 2020 · 32c57c4 · 32c57c4
1 parent 5a8c20b
commit 32c57c4
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Showing 6 changed files with 17 additions and 9 deletions.
diff --git a/python/napkinxc/datasets.py b/python/napkinxc/datasets.py
@@ -236,7 +236,7 @@ def download_file(url, dest_path, overwrite=False, unzip=False, delete_zip=False
 
         if not path.exists(dest_path) or overwrite:
             if verbose:
-                print('Downloading {} into {}... '.format(url, dest_path))
+                print('Downloading {} into {} ... '.format(url, dest_path))
 
             session = requests.Session()
             response = session.get(GoogleDriveDownloader.DOWNLOAD_URL, params={'id': file_id}, stream=True)
@@ -250,7 +250,7 @@ def download_file(url, dest_path, overwrite=False, unzip=False, delete_zip=False
             if unzip:
                 try:
                     if verbose:
-                        print('Unzipping...')
+                        print('Unzipping ...')
 
                     with zipfile.ZipFile(dest_path, 'r') as z:
                         z.extractall(destination_directory)

diff --git a/python/napkinxc/models.py b/python/napkinxc/models.py
@@ -32,7 +32,7 @@ class Model():
     def __init__(self, **params):
         self._model = CPPModel() # In the feature more CPP classes may be available
         self._params = {}
-        self.set_params(**self._params)
+        self.set_params(**params)
 
     def fit(self, X, Y):
         """
@@ -175,7 +175,7 @@ def __init__(self,
                  output,
 
                  # Tree params
-                 tree_type='k-means',
+                 tree_type='hierarchicalKmeans',
                  arity=2,
                  max_leaves=100,
                  kmeans_eps=0.0001,
@@ -208,7 +208,7 @@ def __init__(self,
         Initialize Probabilistic Labels Trees
         :param output: directory where the model will be stored
         :type output: str
-        :param tree_type: tree type to construct {'hierachicalKmeans', 'balancedRandom', 'completeKaryRandom', 'huffman'}, defaults to 'hierachicalKmeans'
+        :param tree_type: tree type to construct {'hierarchicalKmeans', 'balancedRandom', 'completeKaryRandom', 'huffman'}, defaults to 'hierarchicalKmeans'
         :type tree_type: str
         :param arity: arity of tree nodes, k for k-means clustering used in hierarchical k-means tree building procedure, defaults to 2
         :type arity: int
@@ -266,7 +266,7 @@ def __init__(self,
                  output,
 
                  # Tree params
-                 tree_type='k-means',
+                 tree_type='hierarchicalKmeans',
                  arity=2,
                  max_leaves=100,
                  kmeans_eps=0.0001,
@@ -299,7 +299,7 @@ def __init__(self,
         Initialize Hierarchical Softmax
         :param output: directory where the model will be stored
         :type output: str
-        :param tree_type: tree type to construct {'hierachicalKmeans', 'balancedRandom', 'completeKaryRandom', 'huffman'}, defaults to 'hierachicalKmeans'
+        :param tree_type: tree type to construct {'hierarchicalKmeans', 'balancedRandom', 'completeKaryRandom', 'huffman'}, defaults to 'hierarchicalKmeans'
         :type tree_type: str
         :param arity: arity of tree nodes, k for k-means clustering used in hierarchical k-means tree building procedure, defaults to 2
         :type arity: int

diff --git a/src/args.cpp b/src/args.cpp
@@ -134,7 +134,11 @@ Args::Args() {
 
 // Parse args
 void Args::parseArgs(const std::vector<std::string>& args) {
+    LOG(CERR_DEBUG) << "Parsing args...\n";
+
     for (int ai = 0; ai < args.size(); ai += 2) {
+        LOG(CERR_DEBUG) << "  " << args[ai] << " " << args[ai + 1] << "\n";
+
         if (args[ai][0] != '-')
             throw std::invalid_argument("Provided argument without a dash: " + args[ai]);
 

diff --git a/src/ensemble.h b/src/ensemble.h
@@ -195,4 +195,5 @@ template <typename T> void Ensemble<T>::printInfo() {}
 
 template <typename T> void Ensemble<T>::predictWithThresholds(std::vector<Prediction>& prediction, Feature* features, Args& args){
     LOG(CERR) << "  Threshold prediction is not available for ensemble";
+    // TODO
 }
diff --git a/src/log.h b/src/log.h
@@ -32,7 +32,8 @@
 enum LogLevel {
     NONE,
     COUT,
-    CERR
+    CERR,
+    CERR_DEBUG,
 };
 
 // Log config
@@ -60,6 +61,7 @@ class LOG {
                 case NONE: break;
                 case COUT: std::cout << msg; opened = true; break;
                 case CERR: std::cerr << msg; opened = true; break;
+                case CERR_DEBUG: std::cerr << msg; opened = true; break;
             }
         }
         return *this;
@@ -80,6 +82,7 @@ class LOG {
             case NONE: break;
             case COUT: label = "COUT"; break;
             case CERR: label = "CERR"; break;
+            case CERR_DEBUG: label = "DEBUG"; break;
         }
         return label;
     }

diff --git a/src/model.cpp b/src/model.cpp
@@ -50,7 +50,7 @@
 std::shared_ptr<Model> Model::factory(Args& args) {
     std::shared_ptr<Model> model = nullptr;
 
-    if (args.ensemble > 0) {
+    if (args.ensemble > 1) {
         switch (args.modelType) {
         case hsm: model = std::static_pointer_cast<Model>(std::make_shared<Ensemble<HSM>>()); break;
         case plt: model = std::static_pointer_cast<Model>(std::make_shared<Ensemble<BatchPLT>>()); break;