refactor templates

Author: agrimagsrl

micromlgen/decisiontree.py

@@ -1,20 +1,20 @@
-from sklearn.tree import DecisionTreeClassifier
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
 def is_decisiontree(clf):
     """Test if classifier can be ported"""
-    return isinstance(clf, DecisionTreeClassifier)
+    return check_type(clf, 'DecisionTreeClassifier')
 
 
 def port_decisiontree(clf, **kwargs):
     """Port sklearn's DecisionTreeClassifier"""
-    kwargs['classname'] = kwargs['classname'] or 'DecisionTree'
     return jinja('decisiontree/decisiontree.jinja', {
         'left': clf.tree_.children_left,
         'right': clf.tree_.children_right,
         'features': clf.tree_.feature,
         'thresholds': clf.tree_.threshold,
         'classes': clf.tree_.value,
         'i': 0
+    }, {
+        'classname': 'DecisionTree'
     }, **kwargs)

micromlgen/gaussiannb.py

@@ -1,19 +1,19 @@
-from sklearn.naive_bayes import GaussianNB
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
 def is_gaussiannb(clf):
     """Test if classifier can be ported"""
-    return isinstance(clf, GaussianNB)
+    return check_type(clf, 'GaussianNB')
 
 
 def port_gaussiannb(clf, **kwargs):
-    """Port sklearn's DecisionTreeClassifier"""
-    kwargs['classname'] = kwargs['classname'] or 'GaussianNB'
+    """Port sklearn's GaussianNB"""
     return jinja('gaussiannb/gaussiannb.jinja', {
         'sigma': clf.sigma_,
         'theta': clf.theta_,
         'prior': clf.class_prior_,
         'classes': clf.classes_,
         'n_classes': len(clf.classes_)
+    }, {
+        'classname': 'GaussianNB'
     }, **kwargs)

micromlgen/logisticregression.py

@@ -1,18 +1,18 @@
-from sklearn.linear_model import LogisticRegression
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
 def is_logisticregression(clf):
     """Test if classifier can be ported"""
-    return isinstance(clf, LogisticRegression)
+    return check_type(clf, 'LogisticRegression')
 
 
 def port_logisticregression(clf, **kwargs):
-    """Port sklearn's DecisionTreeClassifier"""
-    kwargs['classname'] = kwargs['classname'] or 'LogisticRegression'
+    """Port sklearn's LogisticRegressionClassifier"""
     return jinja('logisticregression/logisticregression.jinja', {
         'weights': clf.coef_,
         'intercept': clf.intercept_,
         'classes': clf.classes_,
         'n_classes': len(clf.classes_)
+    }, {
+        'classname': 'LogisticRegression'
     }, **kwargs)

micromlgen/micromlgen.py

@@ -1,24 +1,12 @@
-from sklearn.decomposition import PCA
-from sklearn.svm import SVC, LinearSVC, OneClassSVM
-
-try:
-    from skbayes.rvm_ard_models import RVC
-except ImportError:
-    from micromlgen.patches import RVC
-try:
-    from sefr import SEFR
-except ImportError:
-    from micromlgen.patches import SEFR
-
 from micromlgen import platforms
-from micromlgen.svm import port_svm
-from micromlgen.rvm import port_rvm
-from micromlgen.sefr import port_sefr
+from micromlgen.svm import is_svm, port_svm
+from micromlgen.rvm import is_rvm, port_rvm
+from micromlgen.sefr import is_sefr, port_sefr
 from micromlgen.decisiontree import is_decisiontree, port_decisiontree
 from micromlgen.randomforest import is_randomforest, port_randomforest
 from micromlgen.logisticregression import is_logisticregression, port_logisticregression
 from micromlgen.gaussiannb import is_gaussiannb, port_gaussiannb
-from micromlgen.pca import port_pca
+from micromlgen.pca import is_pca, port_pca
 
 
 def port(
@@ -29,13 +17,11 @@ def port(
         precision=None):
     """Port a classifier to plain C++"""
     assert platform in platforms.ALL, 'Unknown platform %s. Use one of %s' % (platform, ', '.join(platforms.ALL))
-    if isinstance(clf, (SVC, LinearSVC, OneClassSVM)):
+    if is_svm(clf):
         return port_svm(**locals())
-    elif isinstance(clf, RVC):
+    elif is_rvm(clf):
         return port_rvm(**locals())
-    elif isinstance(clf, PCA):
-        return port_pca(pca=clf, **locals())
-    elif isinstance(clf, SEFR):
+    elif is_sefr(clf):
         return port_sefr(**locals())
     elif is_decisiontree(clf):
         return port_decisiontree(**locals())
@@ -45,4 +31,6 @@ def port(
         return port_logisticregression(**locals())
     elif is_gaussiannb(clf):
         return port_gaussiannb(**locals())
+    elif is_pca(clf):
+        return port_pca(**locals())
     raise TypeError('clf MUST be one of SVC, LinearSVC, OneClassSVC, RVC, DecisionTree, RandomForest, LogisticRegression, GaussianNB, SEFR, PCA')

micromlgen/patches.py

@@ -1,13 +1,18 @@
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
-def port_pca(pca, classname=None, **kwargs):
+def is_pca(clf):
+    """Test if classifier can be ported"""
+    return check_type(clf, 'PCA')
+
+
+def port_pca(clf, **kwargs):
     """Port a PCA"""
-    template_data = {
+    return jinja('pca/pca.jinja', {
         'arrays': {
-            'components': pca.components_,
-            'mean': pca.mean_
+            'components': clf.components_,
+            'mean': clf.mean_
         },
-        'classname': classname if classname is not None else 'PCA'
-    }
-    return jinja('pca/pca.jinja', template_data)
+    }, {
+        'classname': 'PCA'
+    }, **kwargs)

micromlgen/pca.py

@@ -1,15 +1,13 @@
-from sklearn.ensemble import RandomForestClassifier
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
 def is_randomforest(clf):
     """Test if classifier can be ported"""
-    return isinstance(clf, RandomForestClassifier)
+    return check_type(clf, 'RandomForestClassifier')
 
 
 def port_randomforest(clf, **kwargs):
     """Port sklearn's RandomForestClassifier"""
-    kwargs['classname'] = kwargs['classname'] or 'RandomForest'
     return jinja('randomforest/randomforest.jinja', {
         'n_classes': clf.n_classes_,
         'trees': [{
@@ -19,4 +17,6 @@ def port_randomforest(clf, **kwargs):
             'thresholds': clf.tree_.threshold,
             'classes': clf.tree_.value,
         } for clf in clf.estimators_]
-    }, **locals())
+    }, {
+        'classname': 'RandomForest'
+    }, **kwargs)

micromlgen/randomforest.py

@@ -1,19 +1,23 @@
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
 
 
-def port_rvm(clf, classname, **kwargs):
+def is_rvm(clf):
+    """Test if classifier can be ported"""
+    return check_type(clf, 'RVC')
+
+
+def port_rvm(clf, **kwargs):
     """Port a RVM classifier"""
-    assert classname is None or len(classname) > 0, 'Invalid class name'
-    template_data = {
-        **kwargs,
+    return jinja('rvm/rvm.jinja', {
+        'n_classes': len(clf.intercept_),
         'kernel': {
             'type': clf.kernel,
             'gamma': clf.gamma,
             'coef0': clf.coef0,
             'degree': clf.degree
         },
         'sizes': {
-            'features': len(clf.relevant_vectors_[0]),
+            'features': clf.relevant_vectors_[0].shape[1],
         },
         'arrays': {
             'vectors': clf.relevant_vectors_,
@@ -23,6 +27,6 @@ def port_rvm(clf, classname, **kwargs):
             'mean': clf._x_mean,
             'std': clf._x_std
         },
-        'classname': classname if classname is not None else 'RVM',
-    }
-    return jinja('rvm/rvm.jinja', template_data)
+    }, {
+        'classname': 'RVC'
+    }, **kwargs)

micromlgen/rvm.py

@@ -1,12 +1,17 @@
-from micromlgen.utils import jinja
+from micromlgen.utils import jinja, check_type
+
+
+def is_sefr(clf):
+    """Test if classifier can be ported"""
+    return check_type(clf, 'SEFR')
 
 
 def port_sefr(clf, classname=None, **kwargs):
     """Port SEFR classifier"""
-    kwargs.update({
+    return jinja('sefr/sefr.jinja', {
         'weights': clf.weights,
         'bias': clf.bias,
         'dimension': len(clf.weights),
-        'classname': classname or 'SEFR'
-    })
-    return jinja('sefr/sefr.jinja', kwargs)
+    }, {
+        'classname': 'SEFR'
+    }, **kwargs)

micromlgen/sefr.py

@@ -1,18 +1,17 @@
-from sklearn.svm import OneClassSVM
+from micromlgen.utils import jinja, check_type
 
-from micromlgen.utils import jinja
 
+def is_svm(clf):
+    """Test if classifier can be ported"""
+    return check_type(clf, 'SVC', 'LinearSVC', 'OneClassSVM')
 
-def port_svm(clf, classname=None, **kwargs):
+
+def port_svm(clf, **kwargs):
     """Port a SVC / LinearSVC classifier"""
     assert isinstance(clf.gamma, float), 'You probably didn\'t set an explicit value for gamma: 0.001 is a good default'
-    assert classname is None or len(classname) > 0, 'Invalid class name'
-    if classname is None:
-        classname = 'OneClassSVM' if isinstance(clf, OneClassSVM) else 'SVM'
     support_v = clf.support_vectors_
     n_classes = len(clf.n_support_)
-    template_data = {
-        **kwargs,
+    return jinja('svm/svm.jinja', {
         'kernel': {
             'type': clf.kernel,
             'gamma': clf.gamma,
@@ -30,7 +29,7 @@ def port_svm(clf, classname=None, **kwargs):
             'supports': support_v,
             'intercepts': clf.intercept_,
             'coefs': clf.dual_coef_
-        },
-        'classname': classname
-    }
-    return jinja('svm/svm.jinja', template_data)
+        }
+    }, {
+        'classname': 'OneClassSVM' if check_type(clf, 'OneClassSVM') else 'SVM'
+    }, **kwargs)

micromlgen/svm.py

@@ -0,0 +1,26 @@
+#pragma once
+
+namespace Eloquent {
+    namespace ML {
+        namespace Port {
+            class {{ classname }} {
+                public:
+
+                    /**
+                     * Predict class for features vector
+                     */
+                    int predict(float *x) {
+                        {% block predict %}{% endblock %}
+                    }
+
+                    {% include 'classmap.jinja' %}
+
+                    {% block public %}{% endblock %}
+
+                protected:
+
+                    {% block protected %}{% endblock %}
+            };
+        }
+    }
+}

micromlgen/templates/_skeleton.jinja

@@ -1,21 +1,5 @@
-#pragma once
+{% extends '_skeleton.jinja' %}
 
-namespace Eloquent {
-    namespace ML {
-        namespace Port {
-
-            class {{ classname }} {
-                public:
-
-                    /**
-                     * Predict class for features vector
-                     */
-                    int predict(float *x) {
-                        {% include 'decisiontree/tree.jinja' %}
-                    }
-
-                    {% include 'classmap.jinja' %}
-            };
-        }
-    }
-}
+{% block predict %}
+    {% include 'decisiontree/tree.jinja' %}
+{% endblock %}