From 0cec7064625a536b5cb75a00fc990e00f7081953 Mon Sep 17 00:00:00 2001 From: "Igoshev, Iaroslav" Date: Fri, 2 Feb 2024 19:22:30 +0000 Subject: [PATCH] FEAT-#6908: Remove the warning regarding engine initialization Signed-off-by: Igoshev, Iaroslav --- docs/getting_started/quickstart.rst | 1 + docs/getting_started/troubleshooting.rst | 5 +- .../using_modin/using_modin_locally.rst | 49 - docs/usage_guide/advanced_usage/index.rst | 15 + .../advanced_usage/modin_engines.rst | 73 + .../advanced_usage/modin_xgboost.rst | 2 + docs/usage_guide/benchmarking.rst | 2 + examples/jupyter/integrations/NLTK.ipynb | 1186 +-------------- examples/jupyter/integrations/altair.ipynb | 147 +- .../jupyter/integrations/huggingface.ipynb | 363 +---- .../jupyter/integrations/matplotlib.ipynb | 191 +-- examples/jupyter/integrations/plotly.ipynb | 703 +-------- examples/jupyter/integrations/sklearn.ipynb | 1331 +---------------- .../jupyter/integrations/statsmodels.ipynb | 469 +----- .../jupyter/integrations/tensorflow.ipynb | 579 +------ examples/jupyter/integrations/xgboost.ipynb | 66 +- examples/quickstart.ipynb | 1 + modin/core/execution/dask/common/utils.py | 9 - modin/core/execution/ray/common/utils.py | 9 - modin/core/execution/unidist/common/utils.py | 9 - 20 files changed, 369 insertions(+), 4841 deletions(-) create mode 100644 docs/usage_guide/advanced_usage/modin_engines.rst diff --git a/docs/getting_started/quickstart.rst b/docs/getting_started/quickstart.rst index dc6661bc7ab..c91693bade6 100644 --- a/docs/getting_started/quickstart.rst +++ b/docs/getting_started/quickstart.rst @@ -61,6 +61,7 @@ For the purpose of demonstration, we will load in modin as ``pd`` and pandas as ############################################# import time import ray + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init() ############################################# diff --git a/docs/getting_started/troubleshooting.rst b/docs/getting_started/troubleshooting.rst index 85d3b7f4ad0..75f4fc17b6f 100644 --- a/docs/getting_started/troubleshooting.rst +++ b/docs/getting_started/troubleshooting.rst @@ -215,6 +215,7 @@ once Python interpreter is started in them so that to avoid a race condition in import modin.pandas as pd import modin.config as cfg + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}}) pandas_df = pandas.DataFrame( @@ -357,7 +358,9 @@ or cfg.Engine.put("dask") if __name__ == "__main__": - client = Client() # Explicit Dask Client creation. + # Explicit Dask Client creation. + # Look at the Dask Distributed documentation with respect to the Client configuration suited to you most. + client = Client() df = pd.DataFrame([0, 1, 2, 3]) print(df) diff --git a/docs/getting_started/using_modin/using_modin_locally.rst b/docs/getting_started/using_modin/using_modin_locally.rst index d69cf7a6b1e..4d68ef6d8b2 100644 --- a/docs/getting_started/using_modin/using_modin_locally.rst +++ b/docs/getting_started/using_modin/using_modin_locally.rst @@ -23,55 +23,6 @@ just like you would pandas, since the API is identical to pandas. **That's it. You're ready to use Modin on your previous pandas workflows!** -Optional Configurations ------------------------ - -When using Modin locally on a single machine or laptop (without a cluster), Modin will -automatically create and manage a local Dask or Ray cluster for the executing your -code. So when you run an operation for the first time with Modin, you will see a -message like this, indicating that a Modin has automatically initialized a local -cluster for you: - -.. code-block:: python - - df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - -.. code-block:: text - - UserWarning: Ray execution environment not yet initialized. Initializing... - To remove this warning, run the following python code before doing dataframe operations: - - import ray - ray.init() - - If you prefer to use Dask over Ray as your execution backend, you can use the - following code to modify the default configuration: - -.. code-block:: python - - import modin - modin.config.Engine.put("Dask") - -.. code-block:: python - - df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) - - -.. code-block:: text - - UserWarning: Dask execution environment not yet initialized. Initializing... - To remove this warning, run the following python code before doing dataframe operations: - - from distributed import Client - - client = Client() - -Finally, if you already have an Ray or Dask engine initialized, Modin will -automatically attach to whichever engine is available. If you are interested in using -Modin with HDK engine, please refer to :doc:`these instructions `. -For additional information on other settings you can configure, see -:doc:`Modin's config ` page for more details. - Advanced: Configuring the resources Modin uses ---------------------------------------------- diff --git a/docs/usage_guide/advanced_usage/index.rst b/docs/usage_guide/advanced_usage/index.rst index 7263036d5d0..35c910acb01 100644 --- a/docs/usage_guide/advanced_usage/index.rst +++ b/docs/usage_guide/advanced_usage/index.rst @@ -12,6 +12,7 @@ Advanced Usage modin_xgboost modin_logging batch + modin_engines .. meta:: :description lang=en: @@ -22,6 +23,16 @@ integrated toolkit for data scientists. We are actively developing data science such as DataFrame spreadsheet integration, DataFrame algebra, progress bars, SQL queries on DataFrames, and more. Join us on `Slack`_ and `Discourse`_ for the latest updates! +Modin engines +------------- + +Modin supports a series of execution engines such as Ray_, Dask_, `MPI through unidist`_, `HDK`_, +each of which might be a more beneficial choice for a specific scenario. When doing the first operation +with Modin it automatically initializes one of the engines to further perform distributed/parallel computation. +If you are familiar with a concrete execution engine, it is possible to initialize the engine on your own and +Modin will automatically attach to it. Refer to :doc:`Modin engines ` page +for more details. + Experimental APIs ----------------- @@ -118,3 +129,7 @@ downloaded as an artifact from the GitHub Actions tab for further inspection. Se .. _`tqdm`: https://github.com/tqdm/tqdm .. _`distributed XGBoost`: https://medium.com/intel-analytics-software/distributed-xgboost-with-modin-on-ray-fc17edef7720 .. _`fuzzydata`: https://github.com/suhailrehman/fuzzydata +.. _Ray: https://github.com/ray-project/ray +.. _Dask: https://github.com/dask/distributed +.. _`MPI through unidist`: https://github.com/modin-project/unidist +.. _HDK: https://github.com/intel-ai/hdk diff --git a/docs/usage_guide/advanced_usage/modin_engines.rst b/docs/usage_guide/advanced_usage/modin_engines.rst new file mode 100644 index 00000000000..ead2cd8cf60 --- /dev/null +++ b/docs/usage_guide/advanced_usage/modin_engines.rst @@ -0,0 +1,73 @@ +Modin engines +============= + +As a rule, you don't have to worry about initialization of an execution engine as +Modin itself automatically initializes one when performing the first operation. +Also, Modin has a broad range of :doc:`configuration settings `, which +you can use to configure an execution engine. If there is a reason to initialize an execution engine +on your own and you are sure what to do, Modin will automatically attach to whichever engine is available. +Below, you can find some examples on how to initialize a specific execution engine on your own. + +Ray +--- + +You can initialize Ray engine with a specific number of CPUs (worker processes) to perform computation. + +.. code-block:: python + + import ray + import modin.config as modin_cfg + + ray.init(num_cpus=) + modin_cfg.Engine.put("ray") # Modin will use Ray engine + +To get more details on all possible parameters for initialization refer to `Ray documentation`_. + +Dask +---- + +You can initialize Dask engine with a specific number of worker processes and threads per worker to perform computation. + +.. code-block:: python + + from distributed import Client + import modin.config as modin_cfg + + client = Client(n_workers=, threads_per_worker=) + modin_cfg.Engine.put("dask") # # Modin will use Dask engine + +To get more details on all possible parameters for initialization refer to `Dask Distributed documentation`_. + +MPI through unidist +------------------- + +You can initialize MPI thought unidist engine with a specific number of CPUs (worker processes) to perform computation. + +.. code-block:: python + + import unidist + import unidist.config as unidist_cfg + import modin.config as modin_cfg + + unidist_cfg.Backend.put("mpi") + unidist_cfg.CpuCount.put() + unidist.init() + + modin_cfg.Engine.put("unidist") # # Modin will use MPI through unidist engine + +To get more details on all possible parameters for initialization refer to `unidist documentation`_. + +HDK +--- + +For now it is not possible to initialize HDK beforehand. Modin itself initializes it with the required configuration. + +.. code-block:: python + + import modin.config as modin_cfg + + modin_cfg.StorageFormat.put("hdk") # # Modin will use HDK engine + +.. _`Ray documentation`: https://docs.ray.io/en/latest +.. _Dask Distributed documentation: https://distributed.dask.org/en/latest +.. _`unidist documentation`: https://unidist.readthedocs.io/en/latest diff --git a/docs/usage_guide/advanced_usage/modin_xgboost.rst b/docs/usage_guide/advanced_usage/modin_xgboost.rst index e77a464a528..af62158e437 100644 --- a/docs/usage_guide/advanced_usage/modin_xgboost.rst +++ b/docs/usage_guide/advanced_usage/modin_xgboost.rst @@ -55,6 +55,7 @@ To start the Ray runtime on a single node: .. code-block:: python import ray + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init() If you already had the Ray cluster you can connect to it by next way: @@ -78,6 +79,7 @@ All processing will be in a `single node` mode. from sklearn import datasets import ray + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init() # Start the Ray runtime for single-node import modin.pandas as pd diff --git a/docs/usage_guide/benchmarking.rst b/docs/usage_guide/benchmarking.rst index 551c9950ae7..f26a9dac3ec 100644 --- a/docs/usage_guide/benchmarking.rst +++ b/docs/usage_guide/benchmarking.rst @@ -35,6 +35,7 @@ Consider the following ipython script: import time import ray + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init() df = pd.DataFrame(list(range(MinPartitionSize.get() * 2))) %time result = df.map(lambda x: time.sleep(0.1) or x) @@ -146,6 +147,7 @@ That will typically block on any asynchronous computation: time.sleep(10) return x + 1 + # Look at the Ray documentation with respect to the Ray configuration suited to you most. ray.init() df1 = pd.DataFrame(list(range(10_000)), columns=['A']) result = df1.map(slow_add_one) diff --git a/examples/jupyter/integrations/NLTK.ipynb b/examples/jupyter/integrations/NLTK.ipynb index 0b9a945de38..504d56bcae9 100644 --- a/examples/jupyter/integrations/NLTK.ipynb +++ b/examples/jupyter/integrations/NLTK.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -29,96 +29,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. 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" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modin_df['text_string_fdist'] = modin_df['text_token'].apply(lambda x: ' '.join([item for item in x if fdist[item] >= 1 ]))\n", "modin_df[['text', 'text_token', 'text_string', 'text_string_fdist']].head()" @@ -716,32 +147,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[nltk_data] Downloading package wordnet to\n", - "[nltk_data] /Users/labanyamukhopadhyay/nltk_data...\n", - "[nltk_data] Package wordnet is already up-to-date!\n", - "[nltk_data] Downloading package omw-1.4 to\n", - "[nltk_data] /Users/labanyamukhopadhyay/nltk_data...\n", - "[nltk_data] Package omw-1.4 is already up-to-date!\n" - ] - }, - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#lemmatization\n", "nltk.download('wordnet')\n", @@ -750,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -773,21 +181,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True 5\n", - "Name: is_equal, dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# show level count\n", "modin_df.is_equal.value_counts()" @@ -795,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -804,22 +200,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ], - "text/plain": [ - " text \\\n", - "0 it’s despicable, it’s dangerous — and it needs... \n", - "1 we need to repudiate in the strongest terms th... \n", - "2 this weekend’s shootings in buffalo offer a tr... \n", - "3 i’m proud to announce the voyager scholarship ... \n", - "4 across the country, americans are standing up ... \n", - "\n", - " text_token \\\n", - "0 [despicable, dangerous, needs, stop, co, 0ch2z... \n", - "1 [need, repudiate, strongest, terms, politician... \n", - "2 [weekend, shootings, buffalo, offer, tragic, r... \n", - "3 [proud, announce, voyager, scholarship, friend... \n", - "4 [across, country, americans, standing, abortio... \n", - "\n", - " text_string \n", - "0 despicable dangerous needs stop 0ch2zosmhb \n", - "1 need repudiate strongest terms politicians med... \n", - "2 weekend shootings buffalo offer tragic reminde... \n", - "3 proud announce voyager scholarship friend bche... \n", - "4 across country americans standing abortion rig... " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pandas_df['text_string'] = pandas_df['text_token'].apply(lambda x: ' '.join([item for item in x if len(item)>2]))\n", "pandas_df[['text', 'text_token', 'text_string']].head()" @@ -1226,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1236,20 +290,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "FreqDist({'need': 2, 'americans': 2, 'proud': 2, 'despicable': 1, 'dangerous': 1, 'needs': 1, 'stop': 1, '0ch2zosmhb': 1, 'repudiate': 1, 'strongest': 1, ...})" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from nltk.probability import FreqDist\n", "\n", @@ -1259,111 +302,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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texttext_tokentext_stringtext_string_fdist
0it’s despicable, it’s dangerous — and it needs...[despicable, dangerous, needs, stop, co, 0ch2z...despicable dangerous needs stop 0ch2zosmhbdespicable dangerous needs stop 0ch2zosmhb
1we need to repudiate in the strongest terms th...[need, repudiate, strongest, terms, politician...need repudiate strongest terms politicians med...need repudiate strongest terms politicians med...
2this weekend’s shootings in buffalo offer a tr...[weekend, shootings, buffalo, offer, tragic, r...weekend shootings buffalo offer tragic reminde...weekend shootings buffalo offer tragic reminde...
3i’m proud to announce the voyager scholarship ...[proud, announce, voyager, scholarship, friend...proud announce voyager scholarship friend bche...proud announce voyager scholarship friend bche...
4across the country, americans are standing up ...[across, country, americans, standing, abortio...across country americans standing abortion rig...across country americans standing abortion rig...
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" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pandas_df['text_string_fdist'] = pandas_df['text_token'].apply(lambda x: ' '.join([item for item in x if fdist[item] >= 1 ]))\n", "pandas_df[['text', 'text_token', 'text_string', 'text_string_fdist']].head()" @@ -1371,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1384,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1394,21 +335,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True 5\n", - "Name: is_equal, dtype: int64" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# show level count\n", "pandas_df.is_equal.value_counts()" @@ -1416,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1425,22 +354,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", diff --git a/examples/jupyter/integrations/altair.ipynb b/examples/jupyter/integrations/altair.ipynb index 406aaed007b..8502334bb75 100644 --- a/examples/jupyter/integrations/altair.ipynb +++ b/examples/jupyter/integrations/altair.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,24 +20,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-04-06 12:15:19,701\tINFO worker.py:1553 -- Started a local Ray instance.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "from vega_datasets import data\n", "pandas_cars = data.cars()\n", @@ -46,50 +31,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: data of type not recognized\n", - "UserWarning: `DataFrame.to_dict` is not currently supported by PandasOnRay, defaulting to pandas implementation.\n", - "Please refer to https://modin.readthedocs.io/en/stable/supported_apis/defaulting_to_pandas.html for explanation.\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Origin encoding field is specified without a type; the type cannot be automatically inferred because the data is not specified as a pandas.DataFrame.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - 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"\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTopLevelMixin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 385\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mjsonschema\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mValidationError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[0mdct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, validate, ignore, context)\u001b[0m\n\u001b[1;32m 324\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msub_validate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m result = _todict(\n\u001b[0m\u001b[1;32m 327\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mignore\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msub_validate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m_todict\u001b[0;34m(obj, validate, context)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m return {\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m return {\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/utils/schemapi.py\u001b[0m in \u001b[0;36m_todict\u001b[0;34m(obj, validate, context)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;34m\"\"\"Convert an object to a dict representation.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSchemaBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_todict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.local/lib/python3.9/site-packages/altair/vegalite/v4/schema/channels.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, validate, ignore, context)\u001b[0m\n\u001b[1;32m 42\u001b[0m \"match any column in the data.\".format(shorthand))\n\u001b[1;32m 43\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m raise ValueError(\"{} encoding field is specified without a type; \"\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;34m\"the type cannot be automatically inferred because \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;34m\"the data is not specified as a pandas.DataFrame.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Origin encoding field is specified without a type; the type cannot be automatically inferred because the data is not specified as a pandas.DataFrame." - ] - }, - { - "data": { - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "alt.Chart(modin_cars).mark_point().encode(\n", @@ -101,84 +45,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "alt.Chart(pandas_cars).mark_point().encode(\n", @@ -208,7 +77,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.18" }, "orig_nbformat": 4 }, diff --git a/examples/jupyter/integrations/huggingface.ipynb b/examples/jupyter/integrations/huggingface.ipynb index 69370054deb..ebb011c699b 100644 --- a/examples/jupyter/integrations/huggingface.ipynb +++ b/examples/jupyter/integrations/huggingface.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -31,20 +31,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('imdb.csv', )" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import urllib.request\n", "url_path = \"https://modin-datasets.intel.com/testing/IMDB_Dataset.csv\"\n", @@ -53,31 +42,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-04-11 10:27:18,363\tINFO worker.py:1553 -- Started a local Ray instance.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 575 ms, sys: 261 ms, total: 836 ms\n", - "Wall time: 8.58 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "modin_df = pd.read_csv(\"imdb.csv\")" @@ -85,159 +52,34 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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reviewsentiment
0One of the other reviewers has mentioned that ...positive
1A wonderful little production. <br /><br />The...positive
2I thought this was a wonderful way to spend ti...positive
3Basically there's a family where a little boy ...negative
4Petter Mattei's \"Love in the Time of Money\" is...positive
\n", - "
" - ], - "text/plain": [ - " review sentiment\n", - "0 One of the other reviewers has mentioned that ... positive\n", - "1 A wonderful little production.

The... positive\n", - "2 I thought this was a wonderful way to spend ti... positive\n", - "3 Basically there's a family where a little boy ... negative\n", - "4 Petter Mattei's \"Love in the Time of Money\" is... positive" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modin_df.head()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "modin.pandas.dataframe.DataFrame" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(modin_df)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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reviewsentiment
30204Jack Lemmon was one of our great actors. His p...negative
\n", - "
" - ], - "text/plain": [ - " review sentiment\n", - "30204 Jack Lemmon was one of our great actors. His p... negative" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modin_df.sample()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -246,22 +88,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-04-11 10:27:24.824712: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n", - "\n", - "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n", - "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" - ] - } - ], + "outputs": [], "source": [ "# Loading the BERT Classifier and Tokenizer along with Input module\n", "from transformers import InputExample, InputFeatures\n", @@ -272,38 +101,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"tf_bert_for_sequence_classification\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " bert (TFBertMainLayer) multiple 109482240 \n", - " \n", - " dropout_37 (Dropout) multiple 0 \n", - " \n", - " classifier (Dense) multiple 1538 \n", - " \n", - "=================================================================\n", - "Total params: 109,483,778\n", - "Trainable params: 109,483,778\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], + "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -322,18 +129,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['in', 'this', 'ka', '##ggle', 'notebook', ',', 'i', 'will', 'do', 'sentiment', 'analysis', 'using', 'bert', 'with', 'hugging', '##face']\n", - "[1999, 2023, 10556, 24679, 14960, 1010, 1045, 2097, 2079, 15792, 4106, 2478, 14324, 2007, 17662, 12172]\n" - ] - } - ], + "outputs": [], "source": [ "# But first see BERT tokenizer exmaples and other required stuff!\n", "\n", @@ -346,27 +144,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "modin.pandas.dataframe.DataFrame" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(train)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -386,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -438,49 +225,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 InputExample(guid=None, text_a=\"One of the oth...\n", - "1 InputExample(guid=None, text_a='A wonderful li...\n", - "2 InputExample(guid=None, text_a='I thought this...\n", - "3 InputExample(guid=None, text_a=\"Basically ther...\n", - "4 InputExample(guid=None, text_a='Petter Mattei\\...\n", - " ... \n", - "44995 InputExample(guid=None, text_a=\"I watched this...\n", - "44996 InputExample(guid=None, text_a=\"I am a sucker ...\n", - "44997 InputExample(guid=None, text_a=\"I am a college...\n", - "44998 InputExample(guid=None, text_a=\"huge Ramones f...\n", - "44999 InputExample(guid=None, text_a='I rented this ...\n", - "Length: 45000, dtype: object" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_InputExamples" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/45000 [00:00\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1382\u001b[0m _r=1):\n\u001b[1;32m 1383\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1385\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1386\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 914\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 917\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 945\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 946\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 948\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2954\u001b[0m (graph_function,\n\u001b[1;32m 2955\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 2957\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1851\u001b[0m and executing_eagerly):\n\u001b[1;32m 1852\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1854\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1855\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 499\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 500\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 55\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 56\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "model.fit(train_data, epochs=2, validation_data=validation_data)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -566,18 +284,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "worst movie of my life, will never watch movies from this series : Negative\n", - "Wow, blew my mind, what a movie by Marvel, animation and story is amazing : Positive\n" - ] - } - ], + "outputs": [], "source": [ "tf_batch = tokenizer(pred_sentences, max_length=128, padding=True, truncation=True, return_tensors='tf') # we are tokenizing before sending into our trained model\n", "tf_outputs = model(tf_batch) \n", diff --git a/examples/jupyter/integrations/matplotlib.ipynb b/examples/jupyter/integrations/matplotlib.ipynb index 4c1e53a4dbf..8c2a5dec3c7 100644 --- a/examples/jupyter/integrations/matplotlib.ipynb +++ b/examples/jupyter/integrations/matplotlib.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,37 +21,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-01-06 09:40:24,085\tINFO worker.py:1529 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8267 \u001b[39m\u001b[22m\n", - "UserWarning: Distributing object. This may take some time.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "# Example modified from https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/xcorr_acorr_demo.html#sphx-glr-gallery-lines-bars-and-markers-xcorr-acorr-demo-py\n", @@ -74,22 +46,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "# Example modified from https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/xcorr_acorr_demo.html#sphx-glr-gallery-lines-bars-and-markers-xcorr-acorr-demo-py\n", @@ -112,29 +71,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", @@ -158,22 +97,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", @@ -196,29 +122,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", @@ -242,22 +148,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", @@ -279,22 +172,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(9, 3))\n", "\n", @@ -306,29 +186,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", @@ -352,22 +212,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "# Example modified from https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py\n", diff --git a/examples/jupyter/integrations/plotly.ipynb b/examples/jupyter/integrations/plotly.ipynb index 6fd3b5ae185..ea37e589c29 100644 --- a/examples/jupyter/integrations/plotly.ipynb +++ b/examples/jupyter/integrations/plotly.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,30 +30,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/labanyamukhopadhyay/opt/anaconda3/lib/python3.9/site-packages/modin/error_message.py:108: UserWarning:\n", - "\n", - "Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "\n", - "2023-04-06 11:28:25,243\tINFO worker.py:1553 -- Started a local Ray instance.\n", - "/Users/labanyamukhopadhyay/opt/anaconda3/lib/python3.9/site-packages/modin/pandas/dataframe.py:170: UserWarning:\n", - "\n", - "Distributing object. This may take some time.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "modin_df = pd.DataFrame(dict(a=[1,3,2,4], b=[3,2,1,0]))\n", "pandas_df = pandas.DataFrame(dict(a=[1,3,2,4], b=[3,2,1,0]))" @@ -61,129 +40,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig2 = px.bar(modin_df)\n", @@ -193,43 +52,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig2 = px.bar(pandas_df)\n", @@ -238,43 +63,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.line(modin_df)\n", @@ -283,43 +74,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.line(pandas_df)\n", @@ -328,43 +85,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.area(modin_df)\n", @@ -373,43 +96,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.area(pandas_df)\n", @@ -418,43 +107,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.area(modin_df)\n", @@ -463,43 +118,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.area(pandas_df)\n", @@ -508,43 +129,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.violin(modin_df)\n", @@ -553,43 +140,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.violin(pandas_df)\n", @@ -598,43 +151,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.box(modin_df)\n", @@ -643,43 +162,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.box(pandas_df)\n", @@ -688,43 +173,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "fig = px.histogram(modin_df, opacity=0.5, orientation='h', nbins=5)\n", @@ -733,43 +184,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "fig = px.histogram(pandas_df, opacity=0.5, orientation='h', nbins=5)\n", @@ -778,25 +195,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Value of 'locations' is not the name of a column in 'data_frame'. Expected one of [0, 1] but received: fips", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/qj/jybppsbd2jl75s8y2q8s2xx80000gn/T/ipykernel_5361/4179859770.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m modin_df = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv\",\n\u001b[1;32m 9\u001b[0m dtype={\"fips\": str})\n\u001b[0;32m---> 10\u001b[0;31m fig = px.choropleth(modin_df, geojson=counties, locations='fips', color='unemp',\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0mcolor_continuous_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Viridis\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mrange_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_chart_types.py\u001b[0m in \u001b[0;36mchoropleth\u001b[0;34m(data_frame, lat, lon, locations, locationmode, geojson, featureidkey, color, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, projection, scope, center, fitbounds, basemap_visible, title, template, width, height)\u001b[0m\n\u001b[1;32m 1075\u001b[0m \u001b[0mcolored\u001b[0m \u001b[0mregion\u001b[0m \u001b[0mmark\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1076\u001b[0m \"\"\"\n\u001b[0;32m-> 1077\u001b[0;31m return make_figure(\n\u001b[0m\u001b[1;32m 1078\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1079\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChoropleth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mmake_figure\u001b[0;34m(args, constructor, trace_patch, layout_patch)\u001b[0m\n\u001b[1;32m 1943\u001b[0m \u001b[0mapply_default_cascade\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1945\u001b[0;31m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconstructor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1946\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconstructor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTreemap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSunburst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIcicle\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"path\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1947\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_dataframe_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mbuild_dataframe\u001b[0;34m(args, constructor)\u001b[0m\n\u001b[1;32m 1403\u001b[0m \u001b[0;31m# now that things have been prepped, we do the systematic rewriting of `args`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1405\u001b[0;31m df_output, wide_id_vars = process_args_into_dataframe(\n\u001b[0m\u001b[1;32m 1406\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwide_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1407\u001b[0m )\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py\u001b[0m in \u001b[0;36mprocess_args_into_dataframe\u001b[0;34m(args, wide_mode, var_name, value_name)\u001b[0m\n\u001b[1;32m 1205\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margument\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"index\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[0merr_msg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"\\n To use the index, pass it in directly as `df.index`.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1207\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1208\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlength\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_input\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1209\u001b[0m raise ValueError(\n", - "\u001b[0;31mValueError\u001b[0m: Value of 'locations' is not the name of a column in 'data_frame'. Expected one of [0, 1] but received: fips" - ] - } - ], + "outputs": [], "source": [ "# Create a visualization with Modin df\n", "# Example from https://plotly.com/python/mapbox-county-choropleth/#choropleth-map-using-plotlyexpress-and-carto-base-map-no-token-needed\n", @@ -819,43 +220,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a visualization with pandas df\n", "# Example from https://plotly.com/python/mapbox-county-choropleth/#choropleth-map-using-plotlyexpress-and-carto-base-map-no-token-needed\n", diff --git a/examples/jupyter/integrations/sklearn.ipynb b/examples/jupyter/integrations/sklearn.ipynb index 41b305e5c85..7088c92ebb1 100644 --- a/examples/jupyter/integrations/sklearn.ipynb +++ b/examples/jupyter/integrations/sklearn.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -26,23 +26,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-01-03 11:03:39,350\tINFO worker.py:1529 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8266 \u001b[39m\u001b[22m\n" - ] - } - ], + "outputs": [], "source": [ "# From https://www.ritchieng.com/pandas-scikit-learn/\n", "\n", @@ -52,159 +38,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" - ], - "text/plain": [ - " PassengerId Survived Pclass \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - "\n", - " Name Sex Age SibSp \\\n", - "0 Braund, Mr. Owen Harris male 22.0 1 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", - "2 Heikkinen, Miss. Laina female 26.0 0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", - "4 Allen, Mr. William Henry male 35.0 0 \n", - "\n", - " Parch Ticket Fare Cabin Embarked \n", - "0 0 A/5 21171 7.2500 NaN S \n", - "1 0 PC 17599 71.2833 C85 C \n", - "2 0 STON/O2. 3101282 7.9250 NaN S \n", - "3 0 113803 53.1000 C123 S \n", - "4 0 373450 8.0500 NaN S " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train.head()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -235,20 +78,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression()" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# 1. import\n", "from sklearn.linear_model import LogisticRegression\n", @@ -262,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -272,139 +104,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
08923Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
18933Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
28942Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
38953Wirz, Mr. Albertmale27.0003151548.6625NaNS
48963Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
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" - ], - "text/plain": [ - " PassengerId Pclass Name Sex \\\n", - "0 892 3 Kelly, Mr. James male \n", - "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", - "2 894 2 Myles, Mr. Thomas Francis male \n", - "3 895 3 Wirz, Mr. Albert male \n", - "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", - "\n", - " Age SibSp Parch Ticket Fare Cabin Embarked \n", - "0 34.5 0 0 330911 7.8292 NaN Q \n", - "1 47.0 1 0 363272 7.0000 NaN S \n", - "2 62.0 0 0 240276 9.6875 NaN Q \n", - "3 27.0 0 0 315154 8.6625 NaN S \n", - "4 22.0 1 1 3101298 12.2875 NaN S " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# missing Survived column because we are predicting\n", "test.head()" @@ -412,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -421,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -431,17 +133,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "# kaggle wants 2 columns\n", "# new_pred_class\n", @@ -455,18 +149,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: `to_pickle` is not currently supported by PandasOnRay, defaulting to pandas implementation.\n", - "Please refer to https://modin.readthedocs.io/en/stable/supported_apis/defaulting_to_pandas.html for explanation.\n" - ] - } - ], + "outputs": [], "source": [ "# save train data to disk using pickle\n", "train.to_pickle('train.pkl')" @@ -474,270 +159,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: `read_pickle` is not currently supported by PandasOnRay, defaulting to pandas implementation.\n" - ] - }, - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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" - ], - "text/plain": [ - " PassengerId Survived Pclass \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - ".. ... ... ... \n", - "886 887 0 2 \n", - "887 888 1 1 \n", - "888 889 0 3 \n", - "889 890 1 1 \n", - "890 891 0 3 \n", - "\n", - " Name Sex Age SibSp \\\n", - "0 Braund, Mr. Owen Harris male 22.0 1 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", - "2 Heikkinen, Miss. Laina female 26.0 0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", - "4 Allen, Mr. William Henry male 35.0 0 \n", - ".. ... ... ... ... \n", - "886 Montvila, Rev. Juozas male 27.0 0 \n", - "887 Graham, Miss. Margaret Edith female 19.0 0 \n", - "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", - "889 Behr, Mr. Karl Howell male 26.0 0 \n", - "890 Dooley, Mr. Patrick male 32.0 0 \n", - "\n", - " Parch Ticket Fare Cabin Embarked \n", - "0 0 A/5 21171 7.2500 NaN S \n", - "1 0 PC 17599 71.2833 C85 C \n", - "2 0 STON/O2. 3101282 7.9250 NaN S \n", - "3 0 113803 53.1000 C123 S \n", - "4 0 373450 8.0500 NaN S \n", - ".. ... ... ... ... ... \n", - "886 0 211536 13.0000 NaN S \n", - "887 0 112053 30.0000 B42 S \n", - "888 2 W./C. 6607 23.4500 NaN S \n", - "889 0 111369 30.0000 C148 C \n", - "890 0 370376 7.7500 NaN Q \n", - "\n", - "[891 rows x 12 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# read data\n", "pd.read_pickle('train.pkl')" @@ -745,28 +169,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[0. , 1. , 0.5, 0.5],\n", - " [0.5, 0.5, 0. , 1. ]])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# From https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html\n", "\n", @@ -786,17 +191,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.feature_extraction import FeatureHasher\n", "from sklearn.preprocessing import MinMaxScaler\n", @@ -812,27 +209,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 7. 2. 3. ]\n", - " [ 4. 3.5 6. ]\n", - " [10. 3.5 9. ]]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "# From https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html\n", "\n", @@ -847,28 +226,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - }, - { - "data": { - "text/plain": [ - "[0, 1, 2, 3, 4]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# From https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\n", "\n", @@ -881,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -891,46 +251,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "modin.pandas.dataframe.DataFrame" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(X_train)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0 0\n", - " 1 1\n", - " 2 2\n", - " dtype: int64,\n", - " 3 3\n", - " 4 4\n", - " dtype: int64]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train_test_split(y, shuffle=False)" ] @@ -944,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -953,17 +285,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "tips = sns.load_dataset(\"tips\")\n", "tips = pd.DataFrame(tips)" @@ -971,221 +295,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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total_billtipsizesex_Femalesmoker_Noday_Friday_Satday_Suntime_Dinner
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" - ], - "text/plain": [ - " total_bill tip size sex_Female smoker_No day_Fri day_Sat day_Sun \\\n", - "0 16.99 1.01 2 1 1 0 0 1 \n", - "1 10.34 1.66 3 0 1 0 0 1 \n", - "2 21.01 3.50 3 0 1 0 0 1 \n", - "3 23.68 3.31 2 0 1 0 0 1 \n", - "4 24.59 3.61 4 1 1 0 0 1 \n", - ".. ... ... ... ... ... ... ... ... \n", - "239 29.03 5.92 3 0 1 0 1 0 \n", - "240 27.18 2.00 2 1 0 0 1 0 \n", - "241 22.67 2.00 2 0 0 0 1 0 \n", - "242 17.82 1.75 2 0 1 0 1 0 \n", - "243 18.78 3.00 2 1 1 0 0 0 \n", - "\n", - " time_Dinner \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - ".. ... \n", - "239 1 \n", - "240 1 \n", - "241 1 \n", - "242 1 \n", - "243 1 \n", - "\n", - "[244 rows x 9 columns]" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.get_dummies(tips, drop_first=True)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1194,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1204,20 +323,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression()" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# 2. fit the model object\n", "lr.fit(X=tips[[\"total_bill\", \"size\"]], y=tips[\"tip\"])" @@ -1225,20 +333,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.09271334, 0.19259779])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# look at the coefficients\n", "lr.coef_" @@ -1246,20 +343,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6689447408125027" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# look at the intercept\n", "lr.intercept_" @@ -1267,129 +353,9 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tiptotal_billsmoker_No
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............
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244 rows x 3 columns

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" - ], - "text/plain": [ - " tip total_bill smoker_No\n", - "0 1.01 16.99 1\n", - "1 1.66 10.34 1\n", - "2 3.50 21.01 1\n", - "3 3.31 23.68 1\n", - "4 3.61 24.59 1\n", - ".. ... ... ...\n", - "239 5.92 29.03 1\n", - "240 2.00 27.18 0\n", - "241 2.00 22.67 0\n", - "242 1.75 17.82 1\n", - "243 3.00 18.78 1\n", - "\n", - "[244 rows x 3 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tips_dummy = pd.get_dummies(tips, drop_first=True)[[\"tip\", \"total_bill\", \"smoker_No\"]]\n", "tips_dummy" @@ -1397,20 +363,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LinearRegression()" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lr2 = linear_model.LinearRegression()\n", "lr2.fit(X=tips_dummy.iloc[:, 1:], y=tips_dummy[\"tip\"])" @@ -1418,98 +373,18 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0.10572239, 0.14892431]), 0.8142993000217928)" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lr2.coef_, lr2.intercept_" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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total_billsmoker_No
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total_billsmoker_Nopredicted_tips
23929.0314.032345
24027.1803.687834
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" - ], - "text/plain": [ - " total_bill smoker_No predicted_tips\n", - "239 29.03 1 4.032345\n", - "240 27.18 0 3.687834\n", - "241 22.67 0 3.211026\n", - "242 17.82 1 2.847197\n", - "243 18.78 1 2.948690" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "new_data" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "modin.pandas.dataframe.DataFrame" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(new_data)" ] diff --git a/examples/jupyter/integrations/statsmodels.ipynb b/examples/jupyter/integrations/statsmodels.ipynb index 27d4038e59d..51bf90136a5 100644 --- a/examples/jupyter/integrations/statsmodels.ipynb +++ b/examples/jupyter/integrations/statsmodels.ipynb @@ -10,20 +10,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/labanyamukhopadhyay/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n", - "/Users/labanyamukhopadhyay/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n" - ] - } - ], + "outputs": [], "source": [ "import statsmodels.api as sm\n", "import pandas\n", @@ -40,24 +29,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-04-06 11:48:00,894\tINFO worker.py:1553 -- Started a local Ray instance.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "df = sm.datasets.get_rdataset(\"Guerry\", \"HistData\").data\n", "modin_df = pd.DataFrame(df)" @@ -65,96 +39,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DepartmentLotteryLiteracyWealthRegion
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DepartmentLotteryLiteracyWealthRegion
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" - ], - "text/plain": [ - " Department Lottery Literacy Wealth Region\n", - "80 Vendee 68 28 56 W\n", - "81 Vienne 40 25 68 W\n", - "82 Haute-Vienne 55 13 67 C\n", - "83 Vosges 14 62 82 E\n", - "84 Yonne 51 47 30 C" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modin_df = modin_df.dropna()\n", "\n", @@ -263,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -272,17 +72,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "y = pd.DataFrame(y)\n", "X = pd.DataFrame(X)" @@ -290,50 +82,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "unrecognized data structures: / ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/qj/jybppsbd2jl75s8y2q8s2xx80000gn/T/ipykernel_5691/1699330070.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Describe model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m 870\u001b[0m def __init__(self, endog, exog=None, missing='none', hasconst=None,\n\u001b[1;32m 871\u001b[0m **kwargs):\n\u001b[0;32m--> 872\u001b[0;31m super(OLS, self).__init__(endog, exog, missing=missing,\n\u001b[0m\u001b[1;32m 873\u001b[0m hasconst=hasconst, **kwargs)\n\u001b[1;32m 874\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"weights\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m super(WLS, self).__init__(endog, exog, missing=missing,\n\u001b[0m\u001b[1;32m 704\u001b[0m weights=weights, hasconst=hasconst, **kwargs)\n\u001b[1;32m 705\u001b[0m \u001b[0mnobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/regression/linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m 188\u001b[0m \"\"\"\n\u001b[1;32m 189\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRegressionModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 191\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_attr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pinv_wexog'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'weights'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLikelihoodModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0mmissing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'missing'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0mhasconst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hasconst'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m self.data = self._handle_data(endog, exog, missing, hasconst,\n\u001b[0m\u001b[1;32m 78\u001b[0m **kwargs)\n\u001b[1;32m 79\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_constant\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk_constant\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36m_handle_data\u001b[0;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_handle_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0;31m# kwargs arrays could have changed, easier to just attach here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py\u001b[0m in \u001b[0;36mhandle_data\u001b[0;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0mexog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 671\u001b[0;31m \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandle_data_class_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 672\u001b[0m return klass(endog, exog=exog, missing=missing, hasconst=hasconst,\n\u001b[1;32m 673\u001b[0m **kwargs)\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/statsmodels/base/data.py\u001b[0m in \u001b[0;36mhandle_data_class_factory\u001b[0;34m(endog, exog)\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModelData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m raise ValueError('unrecognized data structures: %s / %s' %\n\u001b[0m\u001b[1;32m 660\u001b[0m (type(endog), type(exog)))\n\u001b[1;32m 661\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: unrecognized data structures: / " - ] - } - ], + "outputs": [], "source": [ "mod = sm.OLS(y, X) # Describe model" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'mod' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/qj/jybppsbd2jl75s8y2q8s2xx80000gn/T/ipykernel_5691/3877149832.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fit model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'mod' is not defined" - ] - } - ], + "outputs": [], "source": [ "res = mod.fit() # Fit model\n", "\n", @@ -356,37 +116,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "modin_df = pd.DataFrame({\"A\": [10,20,30,40,50], \"B\": [20, 30, 10, 40, 50], \"C\": [32, 234, 23, 23, 42523]})" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intercept 14.952480\n", - "B 0.401182\n", - "C 0.000352\n", - "dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "import statsmodels.formula.api as sm\n", "result = sm.ols(formula=\"A ~ B + C\", data=modin_df).fit()\n", @@ -395,51 +136,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: A R-squared: 0.579\n", - "Model: OLS Adj. R-squared: 0.158\n", - "Method: Least Squares F-statistic: 1.375\n", - "Date: Thu, 06 Apr 2023 Prob (F-statistic): 0.421\n", - "Time: 11:48:10 Log-Likelihood: -18.178\n", - "No. Observations: 5 AIC: 42.36\n", - "Df Residuals: 2 BIC: 41.19\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 14.9525 17.764 0.842 0.489 -61.481 91.386\n", - "B 0.4012 0.650 0.617 0.600 -2.394 3.197\n", - "C 0.0004 0.001 0.650 0.583 -0.002 0.003\n", - "==============================================================================\n", - "Omnibus: nan Durbin-Watson: 1.061\n", - "Prob(Omnibus): nan Jarque-Bera (JB): 0.498\n", - "Skew: -0.123 Prob(JB): 0.780\n", - "Kurtosis: 1.474 Cond. No. 5.21e+04\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "[2] The condition number is large, 5.21e+04. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n" - ] - } - ], + "outputs": [], "source": [ "print(result.summary())" ] @@ -453,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -465,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -476,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -485,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -494,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -504,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -513,46 +212,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Lottery R-squared: 0.338\n", - "Model: OLS Adj. R-squared: 0.287\n", - "Method: Least Squares F-statistic: 6.636\n", - "Date: Thu, 06 Apr 2023 Prob (F-statistic): 1.07e-05\n", - "Time: 11:48:36 Log-Likelihood: -375.30\n", - "No. Observations: 85 AIC: 764.6\n", - "Df Residuals: 78 BIC: 781.7\n", - "Df Model: 6 \n", - "Covariance Type: nonrobust \n", - "===============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "-------------------------------------------------------------------------------\n", - "Intercept 38.6517 9.456 4.087 0.000 19.826 57.478\n", - "Region[T.E] -15.4278 9.727 -1.586 0.117 -34.793 3.938\n", - "Region[T.N] -10.0170 9.260 -1.082 0.283 -28.453 8.419\n", - "Region[T.S] -4.5483 7.279 -0.625 0.534 -19.039 9.943\n", - "Region[T.W] -10.0913 7.196 -1.402 0.165 -24.418 4.235\n", - "Literacy -0.1858 0.210 -0.886 0.378 -0.603 0.232\n", - "Wealth 0.4515 0.103 4.390 0.000 0.247 0.656\n", - "==============================================================================\n", - "Omnibus: 3.049 Durbin-Watson: 1.785\n", - "Prob(Omnibus): 0.218 Jarque-Bera (JB): 2.694\n", - "Skew: -0.340 Prob(JB): 0.260\n", - "Kurtosis: 2.454 Cond. No. 371.\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" - ] - } - ], + "outputs": [], "source": [ "res = mod.fit() # Fit model\n", "\n", @@ -568,37 +230,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "pandas_df = pd.DataFrame({\"A\": [10,20,30,40,50], \"B\": [20, 30, 10, 40, 50], \"C\": [32, 234, 23, 23, 42523]})" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Intercept 14.952480\n", - "B 0.401182\n", - "C 0.000352\n", - "dtype: float64\n" - ] - } - ], + "outputs": [], "source": [ "import statsmodels.formula.api as sm\n", "result = sm.ols(formula=\"A ~ B + C\", data=pandas_df).fit()\n", @@ -607,51 +250,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: A R-squared: 0.579\n", - "Model: OLS Adj. R-squared: 0.158\n", - "Method: Least Squares F-statistic: 1.375\n", - "Date: Thu, 06 Apr 2023 Prob (F-statistic): 0.421\n", - "Time: 11:48:58 Log-Likelihood: -18.178\n", - "No. Observations: 5 AIC: 42.36\n", - "Df Residuals: 2 BIC: 41.19\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 14.9525 17.764 0.842 0.489 -61.481 91.386\n", - "B 0.4012 0.650 0.617 0.600 -2.394 3.197\n", - "C 0.0004 0.001 0.650 0.583 -0.002 0.003\n", - "==============================================================================\n", - "Omnibus: nan Durbin-Watson: 1.061\n", - "Prob(Omnibus): nan Jarque-Bera (JB): 0.498\n", - "Skew: -0.123 Prob(JB): 0.780\n", - "Kurtosis: 1.474 Cond. No. 5.21e+04\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "[2] The condition number is large, 5.21e+04. This might indicate that there are\n", - "strong multicollinearity or other numerical problems.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n" - ] - } - ], + "outputs": [], "source": [ "print(result.summary())" ] diff --git a/examples/jupyter/integrations/tensorflow.ipynb b/examples/jupyter/integrations/tensorflow.ipynb index dee3f9c0dc6..2702149e604 100644 --- a/examples/jupyter/integrations/tensorflow.ipynb +++ b/examples/jupyter/integrations/tensorflow.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -21,170 +21,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-04-06 11:54:12,027\tINFO worker.py:1553 -- Started a local Ray instance.\n" - ] - }, - { - "data": { - "text/html": [ - "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
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" - ], - "text/plain": [ - " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", - "0 63 1 1 145 233 1 2 150 0 2.3 3 \n", - "1 67 1 4 160 286 0 2 108 1 1.5 2 \n", - "2 67 1 4 120 229 0 2 129 1 2.6 2 \n", - "3 37 1 3 130 250 0 0 187 0 3.5 3 \n", - "4 41 0 2 130 204 0 2 172 0 1.4 1 \n", - "\n", - " ca thal target \n", - "0 0 fixed 0 \n", - "1 3 normal 1 \n", - "2 2 reversible 0 \n", - "3 0 normal 0 \n", - "4 0 normal 0 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "SHUFFLE_BUFFER = 500\n", "BATCH_SIZE = 2\n", @@ -197,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -206,96 +45,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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agethalachtrestbpschololdpeak
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" - ], - "text/plain": [ - " age thalach trestbps chol oldpeak\n", - "0 63 150 145 233 2.3\n", - "1 67 108 160 286 1.5\n", - "2 67 129 120 229 2.6\n", - "3 37 187 130 250 3.5\n", - "4 41 172 130 204 1.4" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "numeric_feature_names = ['age', 'thalach', 'trestbps', 'chol', 'oldpeak']\n", "numeric_features = modin_df[numeric_feature_names]\n", @@ -304,60 +56,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-04-06 11:54:16.000875: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tf.convert_to_tensor(numeric_features)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Failed to find data adapter that can handle input: , ", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/qj/jybppsbd2jl75s8y2q8s2xx80000gn/T/ipykernel_5722/2210982900.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mnormalizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNormalization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnormalizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madapt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/layers/preprocessing/normalization.py\u001b[0m in \u001b[0;36madapt\u001b[0;34m(self, data, batch_size, steps)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0margument\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msupported\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0marray\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \"\"\"\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madapt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mupdate_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/engine/base_preprocessing_layer.py\u001b[0m in \u001b[0;36madapt\u001b[0;34m(self, data, batch_size, steps)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m data_handler = data_adapter.DataHandler(\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_steps_per_execution\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msteps_per_execution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m \u001b[0madapter_cls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_data_adapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1149\u001b[0m self._adapter = adapter_cls(\n\u001b[1;32m 1150\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36mselect_data_adapter\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 982\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0madapter_cls\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[0;31m# TODO(scottzhu): This should be a less implementation-specific error.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 984\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 985\u001b[0m \u001b[0;34m\"Failed to find data adapter that can handle \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 986\u001b[0m \"input: {}, {}\".format(\n", - "\u001b[0;31mValueError\u001b[0m: Failed to find data adapter that can handle input: , " - ] - } - ], + "outputs": [], "source": [ "normalizer = tf.keras.layers.Normalization(axis=-1)\n", "normalizer.adapt(numeric_features)" @@ -372,157 +82,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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agesexcptrestbpscholfbsrestecgthalachexangoldpeakslopecathaltarget
063111452331215002.330fixed0
167141602860210811.523normal1
267141202290212912.622reversible0
337131302500018703.530normal0
441021302040217201.410normal0
\n", - "
" - ], - "text/plain": [ - " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", - "0 63 1 1 145 233 1 2 150 0 2.3 3 \n", - "1 67 1 4 160 286 0 2 108 1 1.5 2 \n", - "2 67 1 4 120 229 0 2 129 1 2.6 2 \n", - "3 37 1 3 130 250 0 0 187 0 3.5 3 \n", - "4 41 0 2 130 204 0 2 172 0 1.4 1 \n", - "\n", - " ca thal target \n", - "0 0 fixed 0 \n", - "1 3 normal 1 \n", - "2 2 reversible 0 \n", - "3 0 normal 0 \n", - "4 0 normal 0 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "SHUFFLE_BUFFER = 500\n", "BATCH_SIZE = 2\n", @@ -535,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -544,96 +106,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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agethalachtrestbpschololdpeak
0631501452332.3
1671081602861.5
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" - ], - "text/plain": [ - " age thalach trestbps chol oldpeak\n", - "0 63 150 145 233 2.3\n", - "1 67 108 160 286 1.5\n", - "2 67 129 120 229 2.6\n", - "3 37 187 130 250 3.5\n", - "4 41 172 130 204 1.4" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "numeric_feature_names = ['age', 'thalach', 'trestbps', 'chol', 'oldpeak']\n", "numeric_features = pandas_df[numeric_feature_names]\n", @@ -642,34 +117,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tf.convert_to_tensor(numeric_features)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/jupyter/integrations/xgboost.ipynb b/examples/jupyter/integrations/xgboost.ipynb index dda5e774240..10f452e7d23 100644 --- a/examples/jupyter/integrations/xgboost.ipynb +++ b/examples/jupyter/integrations/xgboost.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -27,25 +27,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Ray execution environment not yet initialized. Initializing...\n", - "To remove this warning, run the following python code before doing dataframe operations:\n", - "\n", - " import ray\n", - " ray.init(runtime_env={'env_vars': {'__MODIN_AUTOIMPORT_PANDAS__': '1'}})\n", - "\n", - "2023-01-03 12:19:34,877\tINFO worker.py:1529 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32m127.0.0.1:8269 \u001b[39m\u001b[22m\n", - "UserWarning: Distributing object. This may take some time.\n", - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "data_train = pd.DataFrame(np.arange(36).reshape((12,3)), columns=['a', 'b', 'c'])\n", "label_train = pd.DataFrame(np.random.randint(2, size=12))\n", @@ -54,17 +38,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UserWarning: Distributing object. This may take some time.\n" - ] - } - ], + "outputs": [], "source": [ "data_test = pd.DataFrame(np.arange(12).reshape((4,3)), columns=['a', 'b', 'c'])\n", "label_test = pd.DataFrame(np.random.randint(2, size=4))\n", @@ -73,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -84,33 +60,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[1]\ttrain-auc:0.82857\teval-auc:0.50000\n", - "[2]\ttrain-auc:0.82857\teval-auc:0.50000\n", - "[3]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[4]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[5]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[6]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[7]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[8]\ttrain-auc:0.85714\teval-auc:0.50000\n", - "[9]\ttrain-auc:0.85714\teval-auc:0.50000\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "FutureWarning: Pass `evals` as keyword args.\n" - ] - } - ], + "outputs": [], "source": [ "evallist = [(dtrain, 'train'), (dtest, 'eval')]\n", "num_round = 10\n", @@ -119,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/quickstart.ipynb b/examples/quickstart.ipynb index 395e7cd9f7f..0fe229f2cea 100644 --- a/examples/quickstart.ipynb +++ b/examples/quickstart.ipynb @@ -70,6 +70,7 @@ "#############################################\n", "import time\n", "import ray\n", + "# Look at the Ray documentation with respect to the Ray configuration suited to you most.\n", "ray.init()\n", "from IPython.display import Markdown, display\n", "def printmd(string):\n", diff --git a/modin/core/execution/dask/common/utils.py b/modin/core/execution/dask/common/utils.py index 3eda2a50375..160a58bbc8f 100644 --- a/modin/core/execution/dask/common/utils.py +++ b/modin/core/execution/dask/common/utils.py @@ -44,15 +44,6 @@ def _disable_warnings(): except ValueError: from distributed import Client - # The indentation here is intentional, we want the code to be indented. - ErrorMessage.not_initialized( - "Dask", - """ - from distributed import Client - - client = Client() -""", - ) num_cpus = CpuCount.get() memory_limit = Memory.get() worker_memory_limit = memory_limit // num_cpus if memory_limit else "auto" diff --git a/modin/core/execution/ray/common/utils.py b/modin/core/execution/ray/common/utils.py index f24be8fe2cf..3d954b578de 100644 --- a/modin/core/execution/ray/common/utils.py +++ b/modin/core/execution/ray/common/utils.py @@ -118,15 +118,6 @@ def initialize_ray( **extra_init_kw, ) else: - # This string is intentionally formatted this way. We want it indented in - # the warning message. - ErrorMessage.not_initialized( - "Ray", - f""" - import ray - ray.init({', '.join([f'{k}={v}' for k,v in extra_init_kw.items()])}) -""", - ) object_store_memory = _get_object_store_memory() ray_init_kwargs = { "num_cpus": CpuCount.get(), diff --git a/modin/core/execution/unidist/common/utils.py b/modin/core/execution/unidist/common/utils.py index 30d735945e5..c6d2b95ba22 100644 --- a/modin/core/execution/unidist/common/utils.py +++ b/modin/core/execution/unidist/common/utils.py @@ -36,15 +36,6 @@ def initialize_unidist(): modin_cfg.CpuCount.subscribe( lambda cpu_count: unidist_cfg.CpuCount.put(cpu_count.get()) ) - # This string is intentionally formatted this way. We want it indented in - # the warning message. - ErrorMessage.not_initialized( - "unidist", - """ - import unidist - unidist.init() - """, - ) unidist_cfg.MpiRuntimeEnv.put( {"env_vars": {"PYTHONWARNINGS": "ignore::FutureWarning"}} )