|
257 | 257 | }, |
258 | 258 | { |
259 | 259 | "data": { |
260 | | - "application/javascript": [ |
261 | | - "\n", |
262 | | - " setTimeout(function() {\n", |
263 | | - " var nbb_cell_id = 42;\n", |
264 | | - " var nbb_unformatted_code = \"!pytest pytest_benchmark_example.py \";\n", |
265 | | - " var nbb_formatted_code = \"!pytest pytest_benchmark_example.py\";\n", |
266 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
267 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
268 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
269 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
270 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
271 | | - " }\n", |
272 | | - " break;\n", |
273 | | - " }\n", |
274 | | - " }\n", |
275 | | - " }, 500);\n", |
276 | | - " " |
277 | | - ], |
| 260 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 42;\n var nbb_unformatted_code = \"!pytest pytest_benchmark_example.py \";\n var nbb_formatted_code = \"!pytest pytest_benchmark_example.py\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
278 | 261 | "text/plain": [ |
279 | 262 | "<IPython.core.display.Javascript object>" |
280 | 263 | ] |
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3570 | 3553 | }, |
3571 | 3554 | { |
3572 | 3555 | "data": { |
3573 | | - "application/javascript": [ |
3574 | | - "\n", |
3575 | | - " setTimeout(function() {\n", |
3576 | | - " var nbb_cell_id = 25;\n", |
3577 | | - " var nbb_unformatted_code = \"experience1 = {\\\"machine learning\\\": 2, \\\"python\\\": 3}\\nexperience2 = {\\\"ml\\\": 2, \\\"python\\\": 3}\\n\\nDeepDiff(\\n experience1,\\n experience2,\\n exclude_paths={\\\"root['ml']\\\", \\\"root['machine learning']\\\"},\\n)\";\n", |
3578 | | - " var nbb_formatted_code = \"experience1 = {\\\"machine learning\\\": 2, \\\"python\\\": 3}\\nexperience2 = {\\\"ml\\\": 2, \\\"python\\\": 3}\\n\\nDeepDiff(\\n experience1,\\n experience2,\\n exclude_paths={\\\"root['ml']\\\", \\\"root['machine learning']\\\"},\\n)\";\n", |
3579 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
3580 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
3581 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
3582 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
3583 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
3584 | | - " }\n", |
3585 | | - " break;\n", |
3586 | | - " }\n", |
3587 | | - " }\n", |
3588 | | - " }, 500);\n", |
3589 | | - " " |
3590 | | - ], |
| 3556 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 25;\n var nbb_unformatted_code = \"experience1 = {\\\"machine learning\\\": 2, \\\"python\\\": 3}\\nexperience2 = {\\\"ml\\\": 2, \\\"python\\\": 3}\\n\\nDeepDiff(\\n experience1,\\n experience2,\\n exclude_paths={\\\"root['ml']\\\", \\\"root['machine learning']\\\"},\\n)\";\n var nbb_formatted_code = \"experience1 = {\\\"machine learning\\\": 2, \\\"python\\\": 3}\\nexperience2 = {\\\"ml\\\": 2, \\\"python\\\": 3}\\n\\nDeepDiff(\\n experience1,\\n experience2,\\n exclude_paths={\\\"root['ml']\\\", \\\"root['machine learning']\\\"},\\n)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
3591 | 3557 | "text/plain": [ |
3592 | 3558 | "<IPython.core.display.Javascript object>" |
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3639 | 3605 | }, |
3640 | 3606 | { |
3641 | 3607 | "data": { |
3642 | | - "application/javascript": [ |
3643 | | - "\n", |
3644 | | - " setTimeout(function() {\n", |
3645 | | - " var nbb_cell_id = 34;\n", |
3646 | | - " var nbb_unformatted_code = \"num1 = 0.258\\nnum2 = 0.259\\n\\nDeepDiff(num1, num2, significant_digits=2)\";\n", |
3647 | | - " var nbb_formatted_code = \"num1 = 0.258\\nnum2 = 0.259\\n\\nDeepDiff(num1, num2, significant_digits=2)\";\n", |
3648 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
3649 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
3650 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
3651 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
3652 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
3653 | | - " }\n", |
3654 | | - " break;\n", |
3655 | | - " }\n", |
3656 | | - " }\n", |
3657 | | - " }, 500);\n", |
3658 | | - " " |
3659 | | - ], |
| 3608 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 34;\n var nbb_unformatted_code = \"num1 = 0.258\\nnum2 = 0.259\\n\\nDeepDiff(num1, num2, significant_digits=2)\";\n var nbb_formatted_code = \"num1 = 0.258\\nnum2 = 0.259\\n\\nDeepDiff(num1, num2, significant_digits=2)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
3660 | 3609 | "text/plain": [ |
3661 | 3610 | "<IPython.core.display.Javascript object>" |
3662 | 3611 | ] |
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3970 | 3919 | "outputs": [ |
3971 | 3920 | { |
3972 | 3921 | "data": { |
3973 | | - "application/javascript": [ |
3974 | | - "\n", |
3975 | | - " setTimeout(function() {\n", |
3976 | | - " var nbb_cell_id = 18;\n", |
3977 | | - " var nbb_unformatted_code = \"from deepchecks.checks.integrity.new_category import CategoryMismatchTrainTest\\nfrom deepchecks.base import Dataset\\nimport pandas as pd\";\n", |
3978 | | - " var nbb_formatted_code = \"from deepchecks.checks.integrity.new_category import CategoryMismatchTrainTest\\nfrom deepchecks.base import Dataset\\nimport pandas as pd\";\n", |
3979 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
3980 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
3981 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
3982 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
3983 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
3984 | | - " }\n", |
3985 | | - " break;\n", |
3986 | | - " }\n", |
3987 | | - " }\n", |
3988 | | - " }, 500);\n", |
3989 | | - " " |
3990 | | - ], |
| 3922 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 18;\n var nbb_unformatted_code = \"from deepchecks.checks.integrity.new_category import CategoryMismatchTrainTest\\nfrom deepchecks.base import Dataset\\nimport pandas as pd\";\n var nbb_formatted_code = \"from deepchecks.checks.integrity.new_category import CategoryMismatchTrainTest\\nfrom deepchecks.base import Dataset\\nimport pandas as pd\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
3991 | 3923 | "text/plain": [ |
3992 | 3924 | "<IPython.core.display.Javascript object>" |
3993 | 3925 | ] |
|
4015 | 3947 | "outputs": [ |
4016 | 3948 | { |
4017 | 3949 | "data": { |
4018 | | - "application/javascript": [ |
4019 | | - "\n", |
4020 | | - " setTimeout(function() {\n", |
4021 | | - " var nbb_cell_id = 19;\n", |
4022 | | - " var nbb_unformatted_code = \"train = pd.DataFrame({'col1': ['a', 'b', 'c']})\\ntest = pd.DataFrame({'col1': ['c', 'd', 'e']})\\n\\ntrain_ds = Dataset(train, cat_features=['col1'])\\ntest_ds = Dataset(test, cat_features=['col1'])\";\n", |
4023 | | - " var nbb_formatted_code = \"train = pd.DataFrame({\\\"col1\\\": [\\\"a\\\", \\\"b\\\", \\\"c\\\"]})\\ntest = pd.DataFrame({\\\"col1\\\": [\\\"c\\\", \\\"d\\\", \\\"e\\\"]})\\n\\ntrain_ds = Dataset(train, cat_features=[\\\"col1\\\"])\\ntest_ds = Dataset(test, cat_features=[\\\"col1\\\"])\";\n", |
4024 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
4025 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
4026 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
4027 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
4028 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
4029 | | - " }\n", |
4030 | | - " break;\n", |
4031 | | - " }\n", |
4032 | | - " }\n", |
4033 | | - " }, 500);\n", |
4034 | | - " " |
4035 | | - ], |
| 3950 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 19;\n var nbb_unformatted_code = \"train = pd.DataFrame({'col1': ['a', 'b', 'c']})\\ntest = pd.DataFrame({'col1': ['c', 'd', 'e']})\\n\\ntrain_ds = Dataset(train, cat_features=['col1'])\\ntest_ds = Dataset(test, cat_features=['col1'])\";\n var nbb_formatted_code = \"train = pd.DataFrame({\\\"col1\\\": [\\\"a\\\", \\\"b\\\", \\\"c\\\"]})\\ntest = pd.DataFrame({\\\"col1\\\": [\\\"c\\\", \\\"d\\\", \\\"e\\\"]})\\n\\ntrain_ds = Dataset(train, cat_features=[\\\"col1\\\"])\\ntest_ds = Dataset(test, cat_features=[\\\"col1\\\"])\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
4036 | 3951 | "text/plain": [ |
4037 | 3952 | "<IPython.core.display.Javascript object>" |
4038 | 3953 | ] |
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4117 | 4032 | }, |
4118 | 4033 | { |
4119 | 4034 | "data": { |
4120 | | - "application/javascript": [ |
4121 | | - "\n", |
4122 | | - " setTimeout(function() {\n", |
4123 | | - " var nbb_cell_id = 22;\n", |
4124 | | - " var nbb_unformatted_code = \"CategoryMismatchTrainTest().run(train_ds, test_ds)\";\n", |
4125 | | - " var nbb_formatted_code = \"CategoryMismatchTrainTest().run(train_ds, test_ds)\";\n", |
4126 | | - " var nbb_cells = Jupyter.notebook.get_cells();\n", |
4127 | | - " for (var i = 0; i < nbb_cells.length; ++i) {\n", |
4128 | | - " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", |
4129 | | - " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", |
4130 | | - " nbb_cells[i].set_text(nbb_formatted_code);\n", |
4131 | | - " }\n", |
4132 | | - " break;\n", |
4133 | | - " }\n", |
4134 | | - " }\n", |
4135 | | - " }, 500);\n", |
4136 | | - " " |
4137 | | - ], |
| 4035 | + "application/javascript": "\n setTimeout(function() {\n var nbb_cell_id = 22;\n var nbb_unformatted_code = \"CategoryMismatchTrainTest().run(train_ds, test_ds)\";\n var nbb_formatted_code = \"CategoryMismatchTrainTest().run(train_ds, test_ds)\";\n var nbb_cells = Jupyter.notebook.get_cells();\n for (var i = 0; i < nbb_cells.length; ++i) {\n if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n nbb_cells[i].set_text(nbb_formatted_code);\n }\n break;\n }\n }\n }, 500);\n ", |
4138 | 4036 | "text/plain": [ |
4139 | 4037 | "<IPython.core.display.Javascript object>" |
4140 | 4038 | ] |
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5011 | 4909 | "name": "python", |
5012 | 4910 | "nbconvert_exporter": "python", |
5013 | 4911 | "pygments_lexer": "ipython3", |
5014 | | - "version": "3.11.6" |
| 4912 | + "version": "3.11.2" |
5015 | 4913 | }, |
5016 | 4914 | "toc": { |
5017 | 4915 | "base_numbering": 1, |
|
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