Sklearn decisiontree

.github/pull_request_template.md

@@ -22,8 +22,21 @@ Close #
 
 - [ ] Did you add a function?
 - [ ] Did you add the tests?
+- [ ] Did you run your tests and are your tests passing?
+- [ ] Did pre-commit not fail on any check?
 - [ ] Did you follow the steps we provided?
 
+<!--
+Please mark your PR as a draft if you realise after the fact that your tests are not passing or
+that your pre-commit check has some failures.
+
+Here are some relevant resources regarding tests and pre-commit:
+
+https://unify.ai/docs/ivy/overview/deep_dive/ivy_tests.html
+https://unify.ai/docs/ivy/overview/deep_dive/formatting.html#pre-commit
+
+-->
+
 ### Socials:
 
 <!--

.github/workflows/auto-comment.yml → .github/workflows/welcome-message.yml

@@ -1,62 +1,55 @@
-name: Check Semantic and welcome new contributors
-
-on:
-  pull_request_target:
-    types:
-      - opened
-      - edited
-      - synchronize
-      - reopened
-  workflow_call:
-
-permissions:
-  pull-requests: write
-
-jobs:
-  semantics:
-    name: Semantics
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    steps:
-      - uses: amannn/[email protected]
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-
-  pr-compliance-checks:
-    name: PR Compliance Checks
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    steps:
-      - uses: mtfoley/[email protected]
-        with:
-          body-auto-close: false
-          protected-branch-auto-close: false
-          body-comment: >
-            ## Issue Reference
-
-            In order to be considered for merging, the pull request description must refer to a
-            specific issue number. This is described in our
-            [contributing guide](https://unify.ai/docs/ivy/overview/contributing/the_basics.html#todo-list-issues) and our PR template.
-
-            This check is looking for a phrase similar to: "Fixes #XYZ" or "Resolves #XYZ" where XYZ is the issue
-            number that this PR is meant to address.
-
-  welcome:
-    name: Welcome
-    runs-on: ubuntu-latest
-    timeout-minutes: 10
-    needs: semantics
-    if: github.event.action == 'opened'
-    steps:
-      - uses: actions/first-interaction@v1
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          pr-message: |-
-            Congrats on making your first Pull Request and thanks for supporting Ivy! 🎉
-            Joing the conversation in our [Discord](https://discord.com/invite/sXyFF8tDtm)
-
-            Here are some notes to understand our tests:
-            - We have merged all the tests in one file called \`display_test_results\` job. 👀 It contains the following two sections:
-              - **Combined Test Results:** This shows the results of all the ivy tests that ran on the PR. ✔️
-              - **New Failures Introduced:** This lists the tests that are passing on main, but fail on the PR Fork.
-            Please try to make sure that there are no such tests. 💪
+name: Check Semantic and welcome new contributors
+
+on:
+  pull_request_target:
+    types:
+      - opened
+      - edited
+      - synchronize
+      - reopened
+  workflow_call:
+
+permissions:
+  pull-requests: write
+
+jobs:
+  pr-compliance-checks:
+    name: PR Compliance Checks
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    steps:
+      - uses: mtfoley/[email protected]
+        with:
+          body-auto-close: false
+          protected-branch-auto-close: false
+          body-comment: >
+            ## Issue Reference
+
+            In order to be considered for merging, the pull request description must refer to a
+            specific issue number. This is described in our
+            [contributing guide](https://unify.ai/docs/ivy/overview/contributing/the_basics.html#todo-list-issues) and our PR template.
+
+            This check is looking for a phrase similar to: "Fixes #XYZ" or "Resolves #XYZ" where XYZ is the issue
+            number that this PR is meant to address.
+
+  welcome:
+    name: Welcome
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    if: github.event.action == 'opened'
+    steps:
+      - uses: actions/first-interaction@v1
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          pr-message: |-
+            Congrats on making your first Pull Request and thanks for supporting Ivy! 🎉
+            Join the conversation in our [Discord](https://discord.com/invite/sXyFF8tDtm)
+
+            Here are some notes to understand our tests:
+            - We have merged all the tests in one file called \`display_test_results\` job. 👀 It contains the following two sections:
+              - **Combined Test Results:** This shows the results of all the ivy tests that ran on the PR. ✔️
+              - **New Failures Introduced:** This lists the tests that fails on this PR.
+
+            Please make sure they are passing. 💪
+
+            Keep in mind that we will assign an engineer for this task and they will look at it based on the workload that they have, so be patient.

docs/overview/contributing/error_handling.rst

@@ -10,7 +10,7 @@ Error Handling
 
 This section, "Error Handling" aims to assist you in navigating through some common errors you might encounter while working with the Ivy's Functional API. We'll go through some common errors which you might encounter while working as a contributor or a developer.
 
-#. This is the case where we pass in a dtype to `torch` which is not actually supported by the torch's native framework itself. The function which was
+#. This is the case where we pass in a dtype to `torch` which is not actually supported by the torch's native framework itself.
 
    .. code-block:: python
 
@@ -64,7 +64,7 @@ This section, "Error Handling" aims to assist you in navigating through some com
         E
         E       You can reproduce this example by temporarily adding @reproduce_failure('6.82.4', b'AXicY2BAABYQwQgiAABDAAY=') as a decorator on your test case
 
-#. This is a similar assertion as stated in point 2 but with torch and ground-truth tensorflow not matching but the matrices are quite different so there should be an issue in the backends rather than a numerical instability here:
+#. This is a similar assertion as stated in point 2 but with torch and ground-truth tensorflow not matching but the matrices are quite different so there should be an issue in the backends rather than a numerical instability here.
 
    .. code-block:: python
 

ivy/data_classes/array/experimental/activations.py

@@ -123,14 +123,23 @@ def prelu(
         """
         return ivy.prelu(self._data, slope, out=out)
 
-    def relu6(self, /, *, out: Optional[ivy.Array] = None) -> ivy.Array:
+    def relu6(
+        self,
+        /,
+        *,
+        complex_mode: Literal["split", "magnitude", "jax"] = "jax",
+        out: Optional[ivy.Array] = None,
+    ) -> ivy.Array:
         """
         Apply the rectified linear unit 6 function element-wise.
 
         Parameters
         ----------
         self
             input array
+        complex_mode
+            optional specifier for how to handle complex data types. See
+            ``ivy.func_wrapper.handle_complex_input`` for more detail.
         out
             optional output array, for writing the result to.
             It must have a shape that the inputs broadcast to.
@@ -156,7 +165,7 @@ def relu6(self, /, *, out: Optional[ivy.Array] = None) -> ivy.Array:
         >>> print(y)
         ivy.array([0., 0., 1., 2., 3., 4., 5., 6., 6.])
         """
-        return ivy.relu6(self._data, out=out)
+        return ivy.relu6(self._data, complex_mode=complex_mode, out=out)
 
     def logsigmoid(
         self: ivy.Array,

ivy/data_classes/container/experimental/activations.py

@@ -329,6 +329,7 @@ def static_relu6(
         to_apply: Union[bool, ivy.Container] = True,
         prune_unapplied: Union[bool, ivy.Container] = False,
         map_sequences: Union[bool, ivy.Container] = False,
+        complex_mode: Literal["split", "magnitude", "jax"] = "jax",
         out: Optional[ivy.Container] = None,
     ) -> ivy.Container:
         """
@@ -351,6 +352,9 @@ def static_relu6(
         map_sequences
             Whether to also map method to sequences (lists, tuples).
             Default is ``False``.
+        complex_mode
+            optional specifier for how to handle complex data types. See
+            ``ivy.func_wrapper.handle_complex_input`` for more detail.
         out
             optional output container, for writing the result to. It must have a shape
             that the inputs broadcast to.
@@ -379,6 +383,7 @@ def static_relu6(
             to_apply=to_apply,
             prune_unapplied=prune_unapplied,
             map_sequences=map_sequences,
+            complex_mode=complex_mode,
             out=out,
         )
 
@@ -390,6 +395,7 @@ def relu6(
         to_apply: Union[bool, ivy.Container] = True,
         prune_unapplied: Union[bool, ivy.Container] = False,
         map_sequences: Union[bool, ivy.Container] = False,
+        complex_mode: Literal["split", "magnitude", "jax"] = "jax",
         out: Optional[ivy.Container] = None,
     ) -> ivy.Container:
         """
@@ -412,6 +418,9 @@ def relu6(
         map_sequences
             Whether to also map method to sequences (lists, tuples).
             Default is ``False``.
+        complex_mode
+            optional specifier for how to handle complex data types. See
+            ``ivy.func_wrapper.handle_complex_input`` for more detail.
         out
             optional output container, for writing the result to. It must have a shape
             that the inputs broadcast to.
@@ -439,6 +448,7 @@ def relu6(
             to_apply=to_apply,
             prune_unapplied=prune_unapplied,
             map_sequences=map_sequences,
+            complex_mode=complex_mode,
             out=out,
         )
 

ivy/functional/backends/jax/experimental/activations.py

@@ -23,7 +23,9 @@ def logit(
     return jnp.log(x / (1 - x))
 
 
-def relu6(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
+def relu6(
+    x: JaxArray, /, *, complex_mode="jax", out: Optional[JaxArray] = None
+) -> JaxArray:
     relu6_func = jax.nn.relu6
 
     # sets gradient at 0 and 6 to 0 instead of 0.5

ivy/functional/backends/numpy/experimental/activations.py

@@ -44,7 +44,9 @@ def thresholded_relu(
 
 
 @_scalar_output_to_0d_array
-def relu6(x: np.ndarray, /, *, out: Optional[np.ndarray] = None) -> np.ndarray:
+def relu6(
+    x: np.ndarray, /, *, complex_mode="jax", out: Optional[np.ndarray] = None
+) -> np.ndarray:
     return np.minimum(np.maximum(x, 0, dtype=x.dtype), 6, out=out, dtype=x.dtype)
 
 

ivy/functional/backends/paddle/experimental/activations.py

@@ -54,7 +54,12 @@ def thresholded_relu(
     )
 
 
-def relu6(x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None) -> paddle.Tensor:
+@with_unsupported_device_and_dtypes(
+    {"2.5.1 and below": {"cpu": ("bfloat16",)}}, backend_version
+)
+def relu6(
+    x: paddle.Tensor, /, *, complex_mode="jax", out: Optional[paddle.Tensor] = None
+) -> paddle.Tensor:
     if x.dtype in [paddle.float32, paddle.float64]:
         return F.relu6(x)
     if paddle.is_complex(x):

ivy/functional/backends/tensorflow/experimental/activations.py

@@ -38,8 +38,7 @@ def thresholded_relu(
     return tf.cast(tf.where(x > threshold, x, 0), x.dtype)
 
 
-@with_unsupported_dtypes({"2.13.0 and below": ("complex",)}, backend_version)
-def relu6(x: Tensor, /, *, out: Optional[Tensor] = None) -> Tensor:
+def relu6(x: Tensor, /, *, complex_mode="jax", out: Optional[Tensor] = None) -> Tensor:
     return tf.nn.relu6(x)
 
 

ivy/functional/backends/torch/experimental/activations.py

@@ -34,7 +34,9 @@ def thresholded_relu(
 
 
 @with_unsupported_dtypes({"2.0.1 and below": ("float16",)}, backend_version)
-def relu6(x: torch.Tensor, /, *, out: Optional[torch.Tensor] = None) -> torch.Tensor:
+def relu6(
+    x: torch.Tensor, /, *, complex_mode="jax", out: Optional[torch.Tensor] = None
+) -> torch.Tensor:
     return torch.nn.functional.relu6(x)
 
 

ivy/functional/frontends/jax/nn/non_linear_activations.py

@@ -273,7 +273,7 @@ def relu(x):
 
 @to_ivy_arrays_and_back
 def relu6(x):
-    res = ivy.relu6(x)
+    res = ivy.relu6(x, complex_mode="jax")
     return _type_conversion_64(res)
 
 

ivy/functional/frontends/numpy/__init__.py

@@ -565,6 +565,7 @@ def promote_types_of_numpy_inputs(
     _sin,
     _tan,
     _degrees,
+    _arctan2,
 )
 
 from ivy.functional.frontends.numpy.mathematical_functions.handling_complex_numbers import (  # noqa
@@ -672,6 +673,7 @@ def promote_types_of_numpy_inputs(
 arccos = ufunc("_arccos")
 arcsin = ufunc("_arcsin")
 arctan = ufunc("_arctan")
+arctan2 = ufunc("_arctan2")
 cos = ufunc("_cos")
 deg2rad = ufunc("_deg2rad")
 rad2deg = ufunc("_rad2deg")

ivy/functional/frontends/numpy/mathematical_functions/trigonometric_functions.py

@@ -103,6 +103,32 @@ def _arctan(
     return ret
 
 
+# arctan2
+
+
+@handle_numpy_out
+@handle_numpy_dtype
+@to_ivy_arrays_and_back
+@handle_numpy_casting
+@from_zero_dim_arrays_to_scalar
+def _arctan2(
+    x1,
+    x2,
+    /,
+    out=None,
+    *,
+    where=True,
+    casting="same_kind",
+    order="K",
+    dtype=None,
+    subok=True,
+):
+    ret = ivy.atan2(x1, x2, out=out)
+    if ivy.is_array(where):
+        ret = ivy.where(where, ret, ivy.default(out, ivy.zeros_like(ret)), out=out)
+    return ret
+
+
 @handle_numpy_out
 @handle_numpy_dtype
 @to_ivy_arrays_and_back