Remove deprecated code in XGBoost and test suite

eland/ml/ml_model.py

@@ -196,7 +196,7 @@ class MLModel:
 
         # Return results as np.ndarray of float32 or int (consistent with sklearn/xgboost)
         if self.model_type == TYPE_CLASSIFICATION:
-            dt = np.int
+            dt = np.int_
         else:
             dt = np.float32
         return np.asarray(y, dtype=dt)

tests/dataframe/test_aggs_pytest.py

@@ -94,7 +94,7 @@ class TestDataFrameAggs(TestData):
         # Eland returns all float values for all metric aggs, pandas can return int
         # TODO - investigate this more
         pd_aggs = pd_aggs.astype("float64")
-        assert_frame_equal(pd_aggs, ed_aggs, check_exact=False, check_less_precise=2)
+        assert_frame_equal(pd_aggs, ed_aggs, check_exact=False, rtol=2)
 
     # If Aggregate is given a string then series is returned.
     @pytest.mark.parametrize("agg", ["mean", "min", "max"])

tests/dataframe/test_describe_pytest.py

@@ -34,7 +34,7 @@ class TestDataFrameDescribe(TestData):
             pd_describe.drop(["25%", "50%", "75%"], axis="index"),
             ed_describe.drop(["25%", "50%", "75%"], axis="index"),
             check_exact=False,
-            check_less_precise=True,
+            rtol=True,
         )
 
         # TODO - this fails for percentile fields as ES aggregations are approximate

tests/ml/test_ml_model_pytest.py

@@ -263,10 +263,12 @@ class TestMLModel:
             training_data = datasets.make_classification(
                 n_features=5, n_classes=3, n_informative=3
             )
-            classifier = XGBClassifier(booster="gbtree", objective="multi:softmax")
+            classifier = XGBClassifier(
+                booster="gbtree", objective="multi:softmax", use_label_encoder=False
+            )
         else:
             training_data = datasets.make_classification(n_features=5)
-            classifier = XGBClassifier(booster="gbtree")
+            classifier = XGBClassifier(booster="gbtree", use_label_encoder=False)
 
         # Train model
         classifier.fit(training_data[0], training_data[1])
@@ -303,10 +305,14 @@ class TestMLModel:
             training_data = datasets.make_classification(
                 n_features=5, n_classes=3, n_informative=3
             )
-            classifier = XGBClassifier(booster=booster, objective=objective)
+            classifier = XGBClassifier(
+                booster=booster, objective=objective, use_label_encoder=False
+            )
         else:
             training_data = datasets.make_classification(n_features=5)
-            classifier = XGBClassifier(booster=booster, objective=objective)
+            classifier = XGBClassifier(
+                booster=booster, objective=objective, use_label_encoder=False
+            )
 
         # Train model
         classifier.fit(training_data[0], training_data[1])

tests/series/test_arithmetics_pytest.py

@@ -50,7 +50,7 @@ class TestSeriesArithmetics(TestData):
             + ed_df["total_quantity"]
         )
 
-        assert_pandas_eland_series_equal(pd_series, ed_series, check_less_precise=True)
+        assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
     def test_ecommerce_series_simple_integer_addition(self):
         pd_df = self.pd_ecommerce().head(100)
@@ -59,7 +59,7 @@ class TestSeriesArithmetics(TestData):
         pd_series = pd_df["taxful_total_price"] + 5
         ed_series = ed_df["taxful_total_price"] + 5
 
-        assert_pandas_eland_series_equal(pd_series, ed_series, check_less_precise=True)
+        assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
     def test_ecommerce_series_simple_series_addition(self):
         pd_df = self.pd_ecommerce().head(100)
@@ -68,7 +68,7 @@ class TestSeriesArithmetics(TestData):
         pd_series = pd_df["taxful_total_price"] + pd_df["total_quantity"]
         ed_series = ed_df["taxful_total_price"] + ed_df["total_quantity"]
 
-        assert_pandas_eland_series_equal(pd_series, ed_series, check_less_precise=True)
+        assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
     def test_ecommerce_series_basic_arithmetics(self):
         pd_df = self.pd_ecommerce().head(100)
@@ -98,27 +98,19 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["total_quantity"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(10.56)
             ed_series = getattr(ed_df["taxful_total_price"], op)(10.56)
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(np.float32(1.879))
             ed_series = getattr(ed_df["taxful_total_price"], op)(np.float32(1.879))
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(int(8))
             ed_series = getattr(ed_df["taxful_total_price"], op)(int(8))
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
     def test_supported_series_dtypes_ops(self):
         pd_df = self.pd_ecommerce().head(100)
@@ -153,9 +145,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["taxless_total_price"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # int op float
         for op in numeric_ops:
@@ -165,9 +155,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["total_quantity"], op)(
                 ed_df["taxless_total_price"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # float op int
         for op in numeric_ops:
@@ -177,9 +165,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["total_quantity"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # str op int (throws)
         for op in non_string_numeric_ops:
@@ -227,27 +213,19 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["total_quantity"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(3.141)
             ed_series = getattr(ed_df["taxful_total_price"], op)(3.141)
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(np.float32(2.879))
             ed_series = getattr(ed_df["taxful_total_price"], op)(np.float32(2.879))
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
             pd_series = getattr(pd_df["taxful_total_price"], op)(int(6))
             ed_series = getattr(ed_df["taxful_total_price"], op)(int(6))
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
     def test_supported_series_dtypes_rops(self):
         pd_df = self.pd_ecommerce().head(100)
@@ -282,9 +260,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["taxless_total_price"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # int op float
         for op in numeric_ops:
@@ -294,9 +270,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["total_quantity"], op)(
                 ed_df["taxless_total_price"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # float op int
         for op in numeric_ops:
@@ -306,9 +280,7 @@ class TestSeriesArithmetics(TestData):
             ed_series = getattr(ed_df["taxful_total_price"], op)(
                 ed_df["total_quantity"]
             )
-            assert_pandas_eland_series_equal(
-                pd_series, ed_series, check_less_precise=True
-            )
+            assert_pandas_eland_series_equal(pd_series, ed_series, rtol=True)
 
         # str op int (throws)
         for op in non_string_numeric_ops: