Add support for Perceptron and tests for PassiveAggressiveClassifier (#…

…160)
BayesWitnesses · Feb 10, 2020 · f52dc13 · f52dc13
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commit f52dc13
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Showing 3 changed files with 12 additions and 3 deletions.
diff --git a/README.md b/README.md
@@ -41,7 +41,7 @@ pip install m2cgen
 
 |  | Classification | Regression |
 | --- | --- | --- |
-| **Linear** | <ul><li>LogisticRegression</li><li>LogisticRegressionCV</li><li>PassiveAggressiveClassifier</li><li>RidgeClassifier</li><li>RidgeClassifierCV</li><li>SGDClassifier</li></ul> | <ul><li>ARDRegression</li><li>BayesianRidge</li><li>ElasticNet</li><li>ElasticNetCV</li><li>HuberRegressor</li><li>Lars</li><li>LarsCV</li><li>Lasso</li><li>LassoCV</li><li>LassoLars</li><li>LassoLarsCV</li><li>LassoLarsIC</li><li>LinearRegression</li><li>OrthogonalMatchingPursuit</li><li>OrthogonalMatchingPursuitCV</li><li>PassiveAggressiveRegressor</li><li>Ridge</li><li>RidgeCV</li><li>SGDRegressor</li><li>TheilSenRegressor</li></ul> |
+| **Linear** | <ul><li>LogisticRegression</li><li>LogisticRegressionCV</li><li>PassiveAggressiveClassifier</li><li>Perceptron</li><li>RidgeClassifier</li><li>RidgeClassifierCV</li><li>SGDClassifier</li></ul> | <ul><li>ARDRegression</li><li>BayesianRidge</li><li>ElasticNet</li><li>ElasticNetCV</li><li>HuberRegressor</li><li>Lars</li><li>LarsCV</li><li>Lasso</li><li>LassoCV</li><li>LassoLars</li><li>LassoLarsCV</li><li>LassoLarsIC</li><li>LinearRegression</li><li>OrthogonalMatchingPursuit</li><li>OrthogonalMatchingPursuitCV</li><li>PassiveAggressiveRegressor</li><li>Ridge</li><li>RidgeCV</li><li>SGDRegressor</li><li>TheilSenRegressor</li></ul> |
 | **SVM** | <ul><li>LinearSVC</li><li>NuSVC</li><li>SVC</li></ul> | <ul><li>LinearSVR</li><li>NuSVR</li><li>SVR</li></ul> |
 | **Tree** | <ul><li>DecisionTreeClassifier</li><li>ExtraTreeClassifier</li></ul> | <ul><li>DecisionTreeRegressor</li><li>ExtraTreeRegressor</li></ul> |
 | **Random Forest** | <ul><li>ExtraTreesClassifier</li><li>LGBMClassifier(rf booster only)</li><li>RandomForestClassifier</li><li>XGBRFClassifier(binary only, multiclass is not supported yet)</li></ul> | <ul><li>ExtraTreesRegressor</li><li>LGBMRegressor(rf booster only)</li><li>RandomForestRegressor</li><li>XGBRFRegressor</li></ul> |

diff --git a/m2cgen/assemblers/__init__.py b/m2cgen/assemblers/__init__.py
@@ -60,10 +60,11 @@
     "SGDRegressor": LinearModelAssembler,
     "TheilSenRegressor": LinearModelAssembler,
 
-    # Logistic Regressors
+    # Linear Classifiers
     "LogisticRegression": LinearModelAssembler,
     "LogisticRegressionCV": LinearModelAssembler,
     "PassiveAggressiveClassifier": LinearModelAssembler,
+    "Perceptron": LinearModelAssembler,
     "RidgeClassifier": LinearModelAssembler,
     "RidgeClassifierCV": LinearModelAssembler,
     "SGDClassifier": LinearModelAssembler,

diff --git a/tests/e2e/test_e2e.py b/tests/e2e/test_e2e.py
@@ -218,11 +218,15 @@ def classification_binary_random(model):
         regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)),
         regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)),
 
-        # Logistic Regression
+        # Linear Classifiers
         classification(linear_model.LogisticRegression(
             random_state=RANDOM_SEED)),
         classification(linear_model.LogisticRegressionCV(
             random_state=RANDOM_SEED)),
+        classification(linear_model.PassiveAggressiveClassifier(
+            random_state=RANDOM_SEED)),
+        classification(linear_model.Perceptron(
+            random_state=RANDOM_SEED)),
         classification(linear_model.RidgeClassifier(random_state=RANDOM_SEED)),
         classification(linear_model.RidgeClassifierCV()),
         classification(linear_model.SGDClassifier(random_state=RANDOM_SEED)),
@@ -231,6 +235,10 @@ def classification_binary_random(model):
             random_state=RANDOM_SEED)),
         classification_binary(linear_model.LogisticRegressionCV(
             random_state=RANDOM_SEED)),
+        classification_binary(linear_model.PassiveAggressiveClassifier(
+            random_state=RANDOM_SEED)),
+        classification_binary(linear_model.Perceptron(
+            random_state=RANDOM_SEED)),
         classification_binary(linear_model.RidgeClassifier(
             random_state=RANDOM_SEED)),
         classification_binary(linear_model.RidgeClassifierCV()),