Remove all Dask-ML uses (#886)

* initial pass * remove all dask ml references * remove use_dask * fix failing test * wrap in try/except * add gpu test * separate gpu test * use gpu_client * remove imports Co-authored-by: Ayush Dattagupta <[email protected]>
dask-contrib · Nov 29, 2022 · f2913c8 · f2913c8
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diff --git a/.github/workflows/test-upstream.yml b/.github/workflows/test-upstream.yml
@@ -73,11 +73,10 @@ jobs:
           mamba install -c conda-forge "sasl>=0.3.1"
           docker pull bde2020/hive:2.3.2-postgresql-metastore
           docker pull bde2020/hive-metastore-postgresql:2.3.0
-      - name: Install upstream dev Dask / dask-ml
+      - name: Install upstream dev Dask
         if: env.which_upstream == 'Dask'
         run: |
           mamba update dask
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: Test with pytest
         run: |
           pytest --junitxml=junit/test-results.xml --cov-report=xml -n auto tests --dist loadfile
@@ -112,11 +111,10 @@ jobs:
           which python
           pip list
           mamba list
-      - name: Install upstream dev dask-ml
+      - name: Install upstream dev Dask
         if: env.which_upstream == 'Dask'
         run: |
           mamba update dask
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: run a dask cluster
         run: |
           if [[ $which_upstream == "Dask" ]]; then
@@ -161,12 +159,11 @@ jobs:
           which python
           pip list
           mamba list
-      - name: Install upstream dev Dask / dask-ml
+      - name: Install upstream dev Dask
         if: env.which_upstream == 'Dask'
         run: |
           python -m pip install --no-deps git+https://github.com/dask/dask
           python -m pip install --no-deps git+https://github.com/dask/distributed
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: Try to import dask-sql
         run: |
           python -c "import dask_sql; print('ok')"

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
@@ -64,11 +64,10 @@ jobs:
           mamba install -c conda-forge "sasl>=0.3.1"
           docker pull bde2020/hive:2.3.2-postgresql-metastore
           docker pull bde2020/hive-metastore-postgresql:2.3.0
-      - name: Optionally install upstream dev Dask / dask-ml
+      - name: Optionally install upstream dev Dask
         if: needs.detect-ci-trigger.outputs.triggered == 'true'
         run: |
           mamba update dask
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: Test with pytest
         run: |
           pytest --junitxml=junit/test-results.xml --cov-report=xml -n auto tests --dist loadfile
@@ -108,11 +107,10 @@ jobs:
           which python
           pip list
           mamba list
-      - name: Optionally install upstream dev dask-ml
+      - name: Optionally install upstream dev Dask
         if: needs.detect-ci-trigger.outputs.triggered == 'true'
         run: |
           mamba update dask
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: run a dask cluster
         env:
           UPSTREAM: ${{ needs.detect-ci-trigger.outputs.triggered }}
@@ -153,12 +151,11 @@ jobs:
           which python
           pip list
           mamba list
-      - name: Optionally install upstream dev Dask / dask-ml
+      - name: Optionally install upstream dev Dask
         if: needs.detect-ci-trigger.outputs.triggered == 'true'
         run: |
           python -m pip install --no-deps git+https://github.com/dask/dask
           python -m pip install --no-deps git+https://github.com/dask/distributed
-          python -m pip install --no-deps git+https://github.com/dask/dask-ml
       - name: Try to import dask-sql
         run: |
           python -c "import dask_sql; print('ok')"
diff --git a/continuous_integration/environment-3.10-dev.yaml b/continuous_integration/environment-3.10-dev.yaml
@@ -3,7 +3,6 @@ channels:
 - conda-forge
 - nodefaults
 dependencies:
-- dask-ml>=2022.1.22
 - dask>=2022.3.0
 - fastapi>=0.69.0
 - fugue>=0.7.0

diff --git a/continuous_integration/environment-3.8-dev.yaml b/continuous_integration/environment-3.8-dev.yaml
@@ -3,7 +3,6 @@ channels:
 - conda-forge
 - nodefaults
 dependencies:
-- dask-ml=2022.1.22
 - dask=2022.3.0
 - fastapi=0.69.0
 - fugue=0.7.0

diff --git a/continuous_integration/environment-3.9-dev.yaml b/continuous_integration/environment-3.9-dev.yaml
@@ -3,7 +3,6 @@ channels:
 - conda-forge
 - nodefaults
 dependencies:
-- dask-ml>=2022.1.22
 - dask>=2022.3.0
 - fastapi>=0.69.0
 - fugue>=0.7.0

diff --git a/continuous_integration/gpuci/environment.yaml b/continuous_integration/gpuci/environment.yaml
@@ -6,7 +6,6 @@ channels:
 - conda-forge
 - nodefaults
 dependencies:
-- dask-ml>=2022.1.22
 - dask>=2022.3.0
 - fastapi>=0.69.0
 - fugue>=0.7.0

diff --git a/dask_sql/physical/rel/custom/predict.py b/dask_sql/physical/rel/custom/predict.py
@@ -2,6 +2,9 @@
 import uuid
 from typing import TYPE_CHECKING
 
+import dask.dataframe as dd
+import pandas as pd
+
 from dask_sql.datacontainer import ColumnContainer, DataContainer
 from dask_sql.physical.rel.base import BaseRelPlugin
 
@@ -30,8 +33,7 @@ class PredictModelPlugin(BaseRelPlugin):
     Please note however, that it will need to act on Dask dataframes. If you
     are using a model not optimized for this, it might be that you run out of memory if
     your data is larger than the RAM of a single machine.
-    To prevent this, have a look into the dask-ml package,
-    especially the [ParallelPostFit](https://ml.dask.org/meta-estimators.html)
+    To prevent this, have a look into the dask_sql.physical.rel.custom.wrappers.ParallelPostFit
     meta-estimator. If you are using a model trained with `CREATE MODEL`
     and the `wrap_predict` flag, this is done automatically.
 
@@ -59,8 +61,21 @@ def convert(self, rel: "LogicalPlan", context: "dask_sql.Context") -> DataContai
 
         model, training_columns = context.schema[schema_name].models[model_name]
         df = context.sql(sql_select)
-        prediction = model.predict(df[training_columns])
-        predicted_df = df.assign(target=prediction)
+        try:
+            prediction = model.predict(df[training_columns])
+            predicted_df = df.assign(target=prediction)
+        except TypeError:
+            df = df.set_index(df.columns[0], drop=False)
+            prediction = model.predict(df[training_columns])
+            # Convert numpy.ndarray to Dask Series
+            prediction = dd.from_pandas(
+                pd.Series(prediction, index=df.index),
+                npartitions=df.npartitions,
+            )
+            predicted_df = df.assign(target=prediction)
+            # Need to drop first column to reset index
+            # because the first column is equal to the index
+            predicted_df = predicted_df.drop(columns=[df.columns[0]]).reset_index()
 
         # Create a temporary context, which includes the
         # new "table" so that we can use the normal

diff --git a/docker/conda.txt b/docker/conda.txt
@@ -16,7 +16,6 @@ uvicorn>=0.13.4
 pyarrow>=6.0.1
 prompt_toolkit>=3.0.8
 pygments>=2.7.1
-dask-ml>=2022.1.22
 scikit-learn>=1.0.0
 intake>=0.6.0
 pre-commit>=2.11.1

diff --git a/docker/main.dockerfile b/docker/main.dockerfile
@@ -27,7 +27,6 @@ RUN mamba install -y \
     nest-asyncio \
     # additional dependencies
     "pyarrow>=6.0.1" \
-    "dask-ml>=2022.1.22" \
     "scikit-learn>=1.0.0" \
     "intake>=0.6.0" \
     && conda clean -ay

diff --git a/docs/source/machine_learning.rst b/docs/source/machine_learning.rst
@@ -125,8 +125,7 @@ following sql statements
 Want to increase the performance of your model by tuning the
 parameters? Use the hyperparameter tuning directly
 in SQL using below SQL syntax, choose different tuners
-from the dask_ml package based on memory and compute constraints and
-for more details refer to the `dask ml documentation <https://ml.dask.org/hyper-parameter-search.html#incremental-hyperparameter-optimization>`_
+based on memory and compute constraints.
 
 ..
     TODO - add a GPU section to these examples once we have working CREATE EXPERIMENT tests for GPU
@@ -135,7 +134,7 @@ for more details refer to the `dask ml documentation <https://ml.dask.org/hyper-
 
  CREATE EXPERIMENT my_exp WITH (
     model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-    experiment_class = 'dask_ml.model_selection.GridSearchCV',
+    experiment_class = 'sklearn.model_selection.GridSearchCV',
     tune_parameters = (n_estimators = ARRAY [16, 32, 2],
                     learning_rate = ARRAY [0.1,0.01,0.001],
                    max_depth = ARRAY [3,4,5,10]
@@ -258,7 +257,6 @@ and the boolean target ``label``.
     SELECT * FROM training_data
 
     -- We can now train a model from the sklearn package.
-    -- Make sure to install it together with dask-ml with conda or pip.
     CREATE OR REPLACE MODEL my_model WITH (
         model_class = 'sklearn.ensemble.GradientBoostingClassifier',
         wrap_predict = True,
@@ -282,7 +280,7 @@ and the boolean target ``label``.
     -- experiment to tune different hyperparameters
     CREATE EXPERIMENT my_exp WITH(
     model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-    experiment_class = 'dask_ml.model_selection.GridSearchCV',
+    experiment_class = 'sklearn.model_selection.GridSearchCV',
     tune_parameters = (n_estimators = ARRAY [16, 32, 2],
                     learning_rate = ARRAY [0.1,0.01,0.001],
                    max_depth = ARRAY [3,4,5,10]

diff --git a/docs/source/sql/ml.rst b/docs/source/sql/ml.rst
@@ -127,8 +127,7 @@ A model can be anything which has a ``predict`` function.
 Please note however, that it will need to act on Dask dataframes. If you
 are using a model not optimized for this, it might be that you run out of memory if
 your data is larger than the RAM of a single machine.
-To prevent this, have a look into the dask-ml package,
-especially the `ParallelPostFit <https://ml.dask.org/meta-estimators.html>`_
+To prevent this, have a look into the `dask_sql.physical.rel.custom.wrappers.ParallelPostFit`
 meta-estimator. If you are using a model trained with ``CREATE MODEL``
 and the ``wrap_predict`` flag set to true, this is done automatically.
 

diff --git a/notebooks/Feature Overview.ipynb b/notebooks/Feature Overview.ipynb
@@ -590,7 +590,7 @@
    "metadata": {},
    "source": [
     "- Tune single model with different Hyperparameters \n",
-    "  - install **dask_ml** for tunning\n",
+    "  - install **sklearn** for tuning\n",
     "- Tune multiple model with different Hyperparameters\n",
     "  - install **tpot** for Automl"
    ]
@@ -604,7 +604,7 @@
     "%%sql\n",
     "CREATE EXPERIMENT my_exp WITH (\n",
     "        model_class = 'sklearn.ensemble.GradientBoostingClassifier',\n",
-    "        experiment_class = 'dask_ml.model_selection.GridSearchCV',\n",
+    "        experiment_class = 'sklearn.model_selection.GridSearchCV',\n",
     "        tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],\n",
     "                           max_depth = ARRAY [3,4,5,10]),\n",
     "        target_column = 'species'\n",

diff --git a/setup.py b/setup.py
@@ -59,7 +59,6 @@
             "mock>=4.0.3",
             "sphinx>=3.2.1",
             "pyarrow>=6.0.1",
-            "dask-ml>=2022.1.22",
             "scikit-learn>=1.0.0",
             "intake>=0.6.0",
             "pre-commit",

diff --git a/tests/integration/test_model.py b/tests/integration/test_model.py
@@ -18,8 +18,6 @@
     xgboost = None
     dask_cudf = None
 
-pytest.importorskip("dask_ml")
-
 
 def check_trained_model(c, model_name=None):
     if model_name is None:
@@ -157,7 +155,24 @@ def test_clustering_and_prediction(c, training_df):
     c.sql(
         """
         CREATE MODEL my_model WITH (
-            model_class = 'dask_ml.cluster.KMeans'
+            model_class = 'sklearn.cluster.KMeans'
+        ) AS (
+            SELECT x, y
+            FROM timeseries
+            LIMIT 100
+        )
+    """
+    )
+
+    check_trained_model(c)
+
+
+@pytest.mark.gpu
+def test_gpu_clustering_and_prediction(c, gpu_training_df, gpu_client):
+    c.sql(
+        """
+        CREATE MODEL my_model WITH (
+            model_class = 'cuml.dask.cluster.KMeans'
         ) AS (
             SELECT x, y
             FROM timeseries
@@ -244,7 +259,7 @@ def test_show_models(c, training_df):
     c.sql(
         """
         CREATE MODEL my_model2 WITH (
-            model_class = 'dask_ml.cluster.KMeans'
+            model_class = 'sklearn.cluster.KMeans'
         ) AS (
             SELECT x, y
             FROM timeseries
@@ -691,7 +706,7 @@ def test_ml_experiment(c, client, training_df):
         c.sql(
             """
         CREATE EXPERIMENT my_exp WITH (
-            experiment_class = 'dask_ml.model_selection.GridSearchCV',
+            experiment_class = 'sklearn.model_selection.GridSearchCV',
             tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                                max_depth = ARRAY [3,4,5,10]),
             target_column = 'target'
@@ -731,7 +746,7 @@ def test_ml_experiment(c, client, training_df):
             """
             CREATE EXPERIMENT IF NOT EXISTS my_exp WITH (
             model_class = 'that.is.not.a.python.class',
-            experiment_class = 'dask_ml.model_selection.GridSearchCV',
+            experiment_class = 'sklearn.model_selection.GridSearchCV',
             tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                                max_depth = ARRAY [3,4,5,10]),
             target_column = 'target'
@@ -794,7 +809,7 @@ def test_ml_experiment(c, client, training_df):
         """
         CREATE EXPERIMENT my_exp WITH (
         model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-        experiment_class = 'dask_ml.model_selection.GridSearchCV',
+        experiment_class = 'sklearn.model_selection.GridSearchCV',
         tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                            max_depth = ARRAY [3,4,5,10]),
         target_column = 'target'
@@ -816,7 +831,7 @@ def test_ml_experiment(c, client, training_df):
             """
             CREATE EXPERIMENT my_exp WITH (
             model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-            experiment_class = 'dask_ml.model_selection.GridSearchCV',
+            experiment_class = 'sklearn.model_selection.GridSearchCV',
             tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                                max_depth = ARRAY [3,4,5,10]),
             target_column = 'target'
@@ -831,7 +846,7 @@ def test_ml_experiment(c, client, training_df):
         """
         CREATE EXPERIMENT IF NOT EXISTS my_exp WITH (
             model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-            experiment_class = 'dask_ml.model_selection.GridSearchCV',
+            experiment_class = 'sklearn.model_selection.GridSearchCV',
             tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                                max_depth = ARRAY [3,4,5,10]),
             target_column = 'target'
@@ -847,7 +862,7 @@ def test_ml_experiment(c, client, training_df):
         """
         CREATE OR REPLACE EXPERIMENT my_exp WITH (
             model_class = 'sklearn.ensemble.GradientBoostingClassifier',
-            experiment_class = 'dask_ml.model_selection.GridSearchCV',
+            experiment_class = 'sklearn.model_selection.GridSearchCV',
             tune_parameters = (n_estimators = ARRAY [16, 32, 2],learning_rate = ARRAY [0.1,0.01,0.001],
                                max_depth = ARRAY [3,4,5,10]),
             target_column = 'target'
@@ -867,8 +882,8 @@ def test_ml_experiment(c, client, training_df):
         c.sql(
             """
             CREATE EXPERIMENT my_exp1 WITH (
-                model_class = 'dask_ml.cluster.KMeans',
-                experiment_class = 'dask_ml.model_selection.RandomizedSearchCV',
+                model_class = 'sklearn.cluster.KMeans',
+                experiment_class = 'sklearn.model_selection.RandomizedSearchCV',
                 tune_parameters = (n_clusters = ARRAY [3,4,16],tol = ARRAY [0.1,0.01,0.001],
                                    max_iter = ARRAY [3,4,5,10])
             ) AS (
@@ -889,7 +904,7 @@ def test_experiment_automl_classifier(c, client, training_df):
         """
         CREATE EXPERIMENT my_automl_exp1 WITH (
             automl_class = 'tpot.TPOTClassifier',
-            automl_kwargs = (population_size = 2 ,generations=2,cv=2,n_jobs=-1,use_dask=True),
+            automl_kwargs = (population_size=2, generations=2, cv=2, n_jobs=-1),
             target_column = 'target'
         ) AS (
             SELECT x, y, x*y > 0 AS target
@@ -914,11 +929,10 @@ def test_experiment_automl_regressor(c, client, training_df):
         """
         CREATE EXPERIMENT my_automl_exp2 WITH (
             automl_class = 'tpot.TPOTRegressor',
-            automl_kwargs = (population_size = 2,
+            automl_kwargs = (population_size=2,
             generations=2,
             cv=2,
             n_jobs=-1,
-            use_dask=True,
             max_eval_time_mins=1),
 
             target_column = 'target'