kernel_id
stringclasses 4
values | code
stringlengths 1
1.59k
| output
stringlengths 0
70.5M
| execution_time
float64 0
60
| memory_bytes
int64 50.7M
10.7B
| runtime_variables
dict | hash_index
stringlengths 32
32
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0,inplace=True)
test_features.fillna(0,inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
| 0.194007
| 132,894,720
|
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|
201c0cafbfc6160333215ff621876c7f
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features
|
Out[1]:
filename cell_type ... save_results comment_only
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2 nb_54880.ipynb code ... 0 0
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... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
filename cell_type ... save_results comment_only
0 nb_54880.ipynb code ... 0 0
1 nb_54880.ipynb code ... 0 0
2 nb_54880.ipynb code ... 0 0
3 nb_54880.ipynb code ... 0 0
4 nb_54880.ipynb code ... 0 0
... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
| 0.033076
| 134,991,872
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|
67706e33ce5deb9e7987dcfbacf9a72c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features
type(train_feature)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-a115130d9478>:3[0m
[1;32m 1[0m train_features
[0;32m----> 3[0m [38;5;28mtype[39m([43mtrain_feature[49m)
[0;31mNameError[0m: name 'train_feature' is not defined
Error: name 'train_feature' is not defined
| 0.103024
| 141,406,208
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|
d62e871850f75eebf021361f6d6989e3
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features
type(train_features)
|
Out[1]: pandas.core.frame.DataFrame
<class 'pandas.core.frame.DataFrame'>
| 0.004909
| 141,406,208
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141b4a2c4e601f749a64c4ee1063ed06
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features
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1c987ec71827805977bbfbe6534ce6e7
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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!pip install sklearn
!pip install lightgbm as lbg
|
Collecting sklearn
Using cached sklearn-0.0.post12.tar.gz (2.6 kB)
Preparing metadata (setup.py) ... [?25l- error
[1;31merror[0m: [1msubprocess-exited-with-error[0m
[31m×[0m [32mpython setup.py egg_info[0m did not run successfully.
[31m│[0m exit code: [1;36m1[0m
[31m╰─>[0m [31m[15 lines of output][0m
[31m [0m The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
[31m [0m rather than 'sklearn' for pip commands.
[31m [0m
[31m [0m Here is how to fix this error in the main use cases:
[31m [0m - use 'pip install scikit-learn' rather than 'pip install sklearn'
[31m [0m - replace 'sklearn' by 'scikit-learn' in your pip requirements files
[31m [0m (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
[31m [0m - if the 'sklearn' package is used by one of your dependencies,
[31m [0m it would be great if you take some time to track which package uses
[31m [0m 'sklearn' instead of 'scikit-learn' and report it to their issue tracker
[31m [0m - as a last resort, set the environment variable
[31m [0m SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
[31m [0m
[31m [0m More information is available at
[31m [0m https://github.com/scikit-learn/sklearn-pypi-package
[31m [0m [31m[end of output][0m
[1;35mnote[0m: This error originates from a subprocess, and is likely not a problem with pip.
[1;31merror[0m: [1mmetadata-generation-failed[0m
[31m×[0m Encountered error while generating package metadata.
[31m╰─>[0m See above for output.
[1;35mnote[0m: This is an issue with the package mentioned above, not pip.
[1;36mhint[0m: See above for details.
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
[?25hRequirement already satisfied: lightgbm in /usr/local/lib/python3.9/site-packages (4.5.0)
[31mERROR: Could not find a version that satisfies the requirement as (from versions: none)[0m[31m
[0m[31mERROR: No matching distribution found for as[0m[31m
[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
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3e01ee2a6b103aefe548045db047bf67
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.columns
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Out[1]:
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.columns
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85e05b1bc24514afa12ce1eb67d5c13f
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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391dd78a289c5e851457f2774a964c17
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|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.drop(columns=["filename"])
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9d75d48b7ecfe4a0ee4f1bc57bf4f1d2
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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clf = lgb.LGBMClassifier()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e597d21fc49d>:1[0m
[0;32m----> 1[0m clf [38;5;241m=[39m [43mlgb[49m[38;5;241m.[39mLGBMClassifier()
[0;31mNameError[0m: name 'lgb' is not defined
Error: name 'lgb' is not defined
| 0.01199
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592b6cc09515ff26c5ca278ae654dc28
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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!pip install sklearn
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Collecting sklearn
Using cached sklearn-0.0.post12.tar.gz (2.6 kB)
Preparing metadata (setup.py) ... [?25l- error
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[31m×[0m [32mpython setup.py egg_info[0m did not run successfully.
[31m│[0m exit code: [1;36m1[0m
[31m╰─>[0m [31m[15 lines of output][0m
[31m [0m The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
[31m [0m rather than 'sklearn' for pip commands.
[31m [0m
[31m [0m Here is how to fix this error in the main use cases:
[31m [0m - use 'pip install scikit-learn' rather than 'pip install sklearn'
[31m [0m - replace 'sklearn' by 'scikit-learn' in your pip requirements files
[31m [0m (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
[31m [0m - if the 'sklearn' package is used by one of your dependencies,
[31m [0m it would be great if you take some time to track which package uses
[31m [0m 'sklearn' instead of 'scikit-learn' and report it to their issue tracker
[31m [0m - as a last resort, set the environment variable
[31m [0m SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
[31m [0m
[31m [0m More information is available at
[31m [0m https://github.com/scikit-learn/sklearn-pypi-package
[31m [0m [31m[end of output][0m
[1;35mnote[0m: This error originates from a subprocess, and is likely not a problem with pip.
[1;31merror[0m: [1mmetadata-generation-failed[0m
[31m×[0m Encountered error while generating package metadata.
[31m╰─>[0m See above for output.
[1;35mnote[0m: This is an issue with the package mentioned above, not pip.
[1;36mhint[0m: See above for details.
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
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[?25hRequirement already satisfied: lightgbm in /usr/local/lib/python3.9/site-packages (4.5.0)
[31mERROR: Could not find a version that satisfies the requirement as (from versions: none)[0m[31m
[0m[31mERROR: No matching distribution found for as[0m[31m
[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
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eb1755538fd9b2396a224c2c901ac790
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
!pip install sklearn
!pip install lightgbm
|
Collecting sklearn
Using cached sklearn-0.0.post12.tar.gz (2.6 kB)
Preparing metadata (setup.py) ... [?25l- error
[1;31merror[0m: [1msubprocess-exited-with-error[0m
[31m×[0m [32mpython setup.py egg_info[0m did not run successfully.
[31m│[0m exit code: [1;36m1[0m
[31m╰─>[0m [31m[15 lines of output][0m
[31m [0m The 'sklearn' PyPI package is deprecated, use 'scikit-learn'
[31m [0m rather than 'sklearn' for pip commands.
[31m [0m
[31m [0m Here is how to fix this error in the main use cases:
[31m [0m - use 'pip install scikit-learn' rather than 'pip install sklearn'
[31m [0m - replace 'sklearn' by 'scikit-learn' in your pip requirements files
[31m [0m (requirements.txt, setup.py, setup.cfg, Pipfile, etc ...)
[31m [0m - if the 'sklearn' package is used by one of your dependencies,
[31m [0m it would be great if you take some time to track which package uses
[31m [0m 'sklearn' instead of 'scikit-learn' and report it to their issue tracker
[31m [0m - as a last resort, set the environment variable
[31m [0m SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True to avoid this error
[31m [0m
[31m [0m More information is available at
[31m [0m https://github.com/scikit-learn/sklearn-pypi-package
[31m [0m [31m[end of output][0m
[1;35mnote[0m: This error originates from a subprocess, and is likely not a problem with pip.
[1;31merror[0m: [1mmetadata-generation-failed[0m
[31m×[0m Encountered error while generating package metadata.
[31m╰─>[0m See above for output.
[1;35mnote[0m: This is an issue with the package mentioned above, not pip.
[1;36mhint[0m: See above for details.
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
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[?25hRequirement already satisfied: lightgbm in /usr/local/lib/python3.9/site-packages (4.5.0)
Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.9/site-packages (from lightgbm) (1.26.4)
Requirement already satisfied: scipy in /usr/local/lib/python3.9/site-packages (from lightgbm) (1.13.1)
[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv[0m[33m
[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m25.0[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
| 1.267826
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401633d321a74dca7f418c2f673aacf5
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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clf = lgb.LGBMClassifier()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e597d21fc49d>:1[0m
[0;32m----> 1[0m clf [38;5;241m=[39m [43mlgb[49m[38;5;241m.[39mLGBMClassifier()
[0;31mNameError[0m: name 'lgb' is not defined
Error: name 'lgb' is not defined
| 0.013636
| 142,454,784
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|
f03fe1cde183b0520738397da8a3c2dd
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf = lgb.LGBMClassifier()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e597d21fc49d>:1[0m
[0;32m----> 1[0m clf [38;5;241m=[39m [43mlgb[49m[38;5;241m.[39mLGBMClassifier()
[0;31mNameError[0m: name 'lgb' is not defined
Error: name 'lgb' is not defined
| 0.010982
| 142,454,784
|
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|
eeae56585cdd8d4f9e25581c8103ba2c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.columns
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Out[1]:
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'text/plain', 'image/png', 'text/html', 'execute_result',
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| 0.005477
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|
65daf2a2386f3698db6aaa763bcfd209
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf.train(train[['cell_type', 'cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']])
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-a2c2fc20951a>:1[0m
[0;32m----> 1[0m [43mclf[49m[38;5;241m.[39mtrain(train[[[38;5;124m'[39m[38;5;124mcell_type[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mcell_number[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mexecution_count[39m[38;5;124m'[39m,
[1;32m 2[0m [38;5;124m'[39m[38;5;124mlinesofcomment[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mlinesofcode[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mvariable_count[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mfunction_count[39m[38;5;124m'[39m]])
[0;31mNameError[0m: name 'clf' is not defined
Error: name 'clf' is not defined
| 0.011059
| 142,848,000
|
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|
d259db0840ae11502cf84d8b623daf43
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf = lgb.LGBMClassifier()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e597d21fc49d>:1[0m
[0;32m----> 1[0m clf [38;5;241m=[39m [43mlgb[49m[38;5;241m.[39mLGBMClassifier()
[0;31mNameError[0m: name 'lgb' is not defined
Error: name 'lgb' is not defined
| 0.010958
| 142,848,000
|
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|
96c552c3b7573959e01aec29fbe42454
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
| 0.290246
| 235,937,792
|
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}
|
a5ad4ad81a2dd9aa089b97b772ff2a89
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf = lgb.LGBMClassifier()
| 0.005449
| 235,937,792
|
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bb6f70f84b148b2a2d36ae2820b297ff
|
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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clf.train(train[['cell_type', 'cell_number', 'execution_count',
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[0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-a2c2fc20951a>:1[0m
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[0;31mAttributeError[0m: 'LGBMClassifier' object has no attribute 'train'
Error: 'LGBMClassifier' object has no attribute 'train'
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|
7c0d7d86287768aab34d6876fd93d3c9
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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clf.fit(train_features[['cell_type', 'cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']], target)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-36d4750cad1d>:1[0m
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[1;32m 2[0m [43m [49m[38;5;124;43m'[39;49m[38;5;124;43mlinesofcomment[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mlinesofcode[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mvariable_count[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mfunction_count[39;49m[38;5;124;43m'[39;49m[43m][49m[43m][49m[43m,[49m[43m [49m[43mtarget[49m[43m)[49m
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[1;32m 1293[0m [43m [49m[43meval_init_score[49m[38;5;241;43m=[39;49m[43meval_init_score[49m[43m,[49m
[1;32m 1294[0m [43m [49m[43meval_metric[49m[38;5;241;43m=[39;49m[43meval_metric[49m[43m,[49m
[1;32m 1295[0m [43m [49m[43mfeature_name[49m[38;5;241;43m=[39;49m[43mfeature_name[49m[43m,[49m
[1;32m 1296[0m [43m [49m[43mcategorical_feature[49m[38;5;241;43m=[39;49m[43mcategorical_feature[49m[43m,[49m
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File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/sklearn.py:955[0m, in [0;36mLGBMModel.fit[0;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, feature_name, categorical_feature, callbacks, init_model)[0m
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File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/engine.py:282[0m, in [0;36mtrain[0;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks)[0m
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File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/basic.py:3637[0m, in [0;36mBooster.__init__[0;34m(self, params, train_set, model_file, model_str)[0m
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[1;32m 3636[0m [38;5;66;03m# construct booster object[39;00m
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File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/basic.py:2576[0m, in [0;36mDataset.construct[0;34m(self)[0m
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[1;32m 2574[0m [38;5;28;01melse[39;00m:
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[1;32m 787[0m )
[0;31mValueError[0m: pandas dtypes must be int, float or bool.
Fields with bad pandas dtypes: cell_type: object
Error: pandas dtypes must be int, float or bool.
Fields with bad pandas dtypes: cell_type: object
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afc00ece715a191cb4585fc2bf672fdf
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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clf.fit(train_features[['cell_number', 'execution_count',
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Out[1]: LGBMClassifier()
LGBMClassifier()
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|
8fe1d64afbd59ac7a745b59d5a3c7e77
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf.fit(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']], target)
|
Out[1]: LGBMClassifier()
LGBMClassifier()
| 1.050409
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9e1697bbf301a9c17ec6c6ce6d99b381
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
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[0;36m File [0;32m<ipython-input-1-c806046091db>:2[0;36m[0m
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d0392ef2c011e9e34ea0dedecde6b050
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
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|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-6ec66d383ec0>:1[0m
[0;32m----> 1[0m [43maccuracy_score[49m(clf[38;5;241m.[39mpredict(train_features[[[38;5;124m'[39m[38;5;124mcell_number[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mexecution_count[39m[38;5;124m'[39m,
[1;32m 2[0m [38;5;124m'[39m[38;5;124mlinesofcomment[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mlinesofcode[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mvariable_count[39m[38;5;124m'[39m, [38;5;124m'[39m[38;5;124mfunction_count[39m[38;5;124m'[39m]]), target)
[0;31mNameError[0m: name 'accuracy_score' is not defined
Error: name 'accuracy_score' is not defined
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
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"weekday_df": null,
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"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
57a722591a4a5c2dce5ece54bc5965bc
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
| 0.077509
| 282,116,096
|
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"Timestamp": null,
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"name": "accuracy_score",
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"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
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"f_statistic": null,
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"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
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"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"name": "train_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
c151186e6981034d84257ed0086a8e5e
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]), target)
|
Out[1]: 0.797531287502143
0.797531287502143
| 0.040105
| 283,426,816
|
{
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},
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"name": "clf",
"size": 48,
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"value": "LGBMClassifier()"
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},
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"target": {
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"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"train_features": {
"name": "train_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
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"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
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"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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}
|
0ca43413ea161c1e3511db7a7f43840a
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]), target)
|
Out[1]: 0.797531287502143
0.797531287502143
| 0.037235
| 283,426,816
|
{
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"MultinomialNB": null,
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},
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"clf": {
"name": "clf",
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"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
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"cnt": null,
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},
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"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
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},
"test_features": {
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"size": 1546797,
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},
"text": null,
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"type": "DataFrame",
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},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
22c34da5662945830f579b2ffb794851
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
clf = lgb.LGBMClassifier()
| 0.004447
| 283,426,816
|
{
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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d88608dfc195a5c3c74f6ffe8391cf5e
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|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_columns = ['cell_number', 'execution_count', 'linesofcomment', 'linesofcode',
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clf.fit(train_features[train_columns], target)
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Out[1]: LGBMClassifier()
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|
9791f5f3890226ec9c47e1a648ac2db2
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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accuracy_score(clf.predict(train_features[train_columns]), target)
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Out[1]: 0.8400480027430138
0.8400480027430138
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92f9845971518f06100a04b0d119a180
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
target.value_counts()
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Out[1]:
primary_label
data_exploration 1664
data_preprocessing 1396
modelling 922
helper_functions 467
load_data 434
result_visualization 292
evaluation 233
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|
a8ddef30b7247b3ed05e8c7d5d81ba69
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
target.value_counts(normalize=True)
|
Out[1]:
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|
828f07fc32d8a3a2539ed586e31eebcf
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
target.value_counts(normalize=True)
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Out[1]:
primary_label
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prediction 0.030859
comment_only 0.023144
save_results 0.018858
Name: proportion, dtype: float64
| 0.006949
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}
|
9d15feeff411f64b54540e658e716b6e
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(test_features[train_columns]), test_features["primary_label"])
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mKeyError[0m Traceback (most recent call last)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexes/base.py:3805[0m, in [0;36mIndex.get_loc[0;34m(self, key)[0m
[1;32m 3804[0m [38;5;28;01mtry[39;00m:
[0;32m-> 3805[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_engine[49m[38;5;241;43m.[39;49m[43mget_loc[49m[43m([49m[43mcasted_key[49m[43m)[49m
[1;32m 3806[0m [38;5;28;01mexcept[39;00m [38;5;167;01mKeyError[39;00m [38;5;28;01mas[39;00m err:
File [0;32mindex.pyx:167[0m, in [0;36mpandas._libs.index.IndexEngine.get_loc[0;34m()[0m
File [0;32mindex.pyx:196[0m, in [0;36mpandas._libs.index.IndexEngine.get_loc[0;34m()[0m
File [0;32mpandas/_libs/hashtable_class_helper.pxi:7081[0m, in [0;36mpandas._libs.hashtable.PyObjectHashTable.get_item[0;34m()[0m
File [0;32mpandas/_libs/hashtable_class_helper.pxi:7089[0m, in [0;36mpandas._libs.hashtable.PyObjectHashTable.get_item[0;34m()[0m
[0;31mKeyError[0m: 'primary_label'
The above exception was the direct cause of the following exception:
[0;31mKeyError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-54ea9b48c1cc>:1[0m
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File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py:4102[0m, in [0;36mDataFrame.__getitem__[0;34m(self, key)[0m
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|
9a424ea348b564df03afa869be4bd98d
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"])
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Out[1]: 0.5365853658536586
0.5365853658536586
| 0.018844
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5e761cde4a9be16cd38f668aaf3c0bea
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"])
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Out[1]: 0.5365853658536586
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a41635c6659cbd9aec0fa8fe487d4cae
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"])
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Out[1]: 0.5365853658536586
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|
93af7d980c64a0e6764f9ec85b9ab5e8
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"])
|
Out[1]: 0.5365853658536586
0.5365853658536586
| 0.021425
| 308,031,488
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|
06428dc58437cded19ee2568dacfd6f4
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"])
|
Out[1]: 0.5365853658536586
0.5365853658536586
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a1396408444015dd3203a2e6b4be29d7
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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target.value_counts(normalize=True)
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Out[1]:
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
e54559aabc969f4e5fdf692776d01b55
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# accuracy_score(, validation_features["primary_label"])
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.021889
| 308,162,560
|
{
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": null,
"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
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"action_time": null,
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"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": null,
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
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},
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"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
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},
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"session_durations": null,
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"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"top_user_daily_actions": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
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},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
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"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
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"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
07b8b0f801d0860349972d209ca0a797
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# accuracy_score(, validation_features["primary_label"])
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.022657
| 308,162,560
|
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"MultinomialNB": null,
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},
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"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
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"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"top_user_daily_actions": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
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"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
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"size": 136,
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},
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"name": "validation_features",
"size": 1826459,
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
c33d6a6cf87f59c027a314f78e8bef08
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# accuracy_score(, validation_features["primary_label"])
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.023017
| 308,162,560
|
{
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"MultinomialNB": null,
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"Timestamp": null,
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"accuracy_score": {
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},
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"data": null,
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"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
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"f1_score": {
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},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": null,
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"top_user_daily_actions": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
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},
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"txt": null,
"user_daily_actions": null,
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"validation_features": {
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
ad5ae01f42a6b65c8c9a236ddb868548
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# accuracy_score(, validation_features["primary_label"])
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.020862
| 308,162,560
|
{
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": null,
"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
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"ax": null,
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"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
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"clf_c": null,
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"cnt": null,
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"cv": null,
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"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
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"f1_score": {
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"type": "function",
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"size": 60,
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"median_duration": null,
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"pred": {
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},
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"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"top_user_daily_actions": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
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},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
14322add7f9545e00f0ed810e93a125b
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# accuracy_score(, validation_features["primary_label"])
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.02148
| 308,162,560
|
{
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"MultinomialNB": null,
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"Readliner": null,
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"Timestamp": null,
"X": null,
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"X3": null,
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"X_columns_text": null,
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"accuracy_score": {
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},
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},
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},
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"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
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},
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"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
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"name": "train_test_split",
"size": 136,
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},
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"user_daily_actions": null,
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"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": null,
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"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
c687ff4a52d3dc195947059008d775d8
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.022594
| 308,162,560
|
{
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"MultinomialNB": null,
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"accuracy_score": {
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},
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"type": "LGBMClassifier",
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"daily_actions": null,
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"datetime": null,
"day": null,
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"deltas": null,
"df": null,
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"f1_score": {
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},
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},
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"pred": {
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},
"target_drop": {
"name": "target_drop",
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},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
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},
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"txt": null,
"user_daily_actions": null,
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|
ff535105a2176276389aaa0b6de09b4c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.02127
| 308,162,560
|
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|
28b82541aa4ebe81e80f7546f7cd0e14
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.021063
| 308,293,632
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|
422eb68cdd9e28352cb07eb67187c2a9
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
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|
1118462be9a83fd20b97f4a7628496e1
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.021122
| 308,424,704
|
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|
fa06a2ca420cdf8fb5c22c62f4ae07ba
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.04944
| 308,424,704
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|
d0973457b5f801447efec0d0925a32d9
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average="weighted")
|
Out[1]: 0.5479133178319338
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|
ab9d56904607096c37253bc3b93de7dc
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average=None)
|
Out[1]:
array([0.97674419, 0.68326848, 0.53424658, 0.10869565, 0.68401487,
0.416 , 0.35075885, 0. , 0.2826087 , 0.04761905])
array([0.97674419, 0.68326848, 0.53424658, 0.10869565, 0.68401487,
0.416 , 0.35075885, 0. , 0.2826087 , 0.04761905])
| 0.019805
| 308,817,920
|
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},
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}
|
b032f7dbde804af9c66ab0bf0706696a
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
f1_score(pred, validation_features["primary_label"], average=None)
|
Out[1]:
array([0.97674419, 0.68326848, 0.53424658, 0.10869565, 0.68401487,
0.416 , 0.35075885, 0. , 0.2826087 , 0.04761905])
array([0.97674419, 0.68326848, 0.53424658, 0.10869565, 0.68401487,
0.416 , 0.35075885, 0. , 0.2826087 , 0.04761905])
| 0.021076
| 308,817,920
|
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}
|
28aca0392965acaa209de1fb1c7b9dd6
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
pred = clf.predict(validation_features[train_columns])
from sklearn.metrics import f1_score
# f1_score(pred, validation_features["primary_label"], average=None)
f1_score(pred, validation_features["primary_label"], average='weighted')
|
Out[1]: 0.5479133178319338
0.5479133178319338
| 0.020088
| 309,211,136
|
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}
|
1e720e0f06fb3c077dd188385af86810
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
corpus = "I want some pitsaa"
X = vectorizer.fit_transform(corpus)
# X = vectorizer.fit_transform(corpus)
X
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-68374230477f>:6[0m
[1;32m 4[0m vectorizer [38;5;241m=[39m TfidfVectorizer()
[1;32m 5[0m corpus [38;5;241m=[39m [38;5;124m"[39m[38;5;124mI want some pitsaa[39m[38;5;124m"[39m
[0;32m----> 6[0m X [38;5;241m=[39m [43mvectorizer[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mcorpus[49m[43m)[49m
[1;32m 7[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[1;32m 8[0m X
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:2104[0m, in [0;36mTfidfVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 2097[0m [38;5;28mself[39m[38;5;241m.[39m_check_params()
[1;32m 2098[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf [38;5;241m=[39m TfidfTransformer(
[1;32m 2099[0m norm[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mnorm,
[1;32m 2100[0m use_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39muse_idf,
[1;32m 2101[0m smooth_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msmooth_idf,
[1;32m 2102[0m sublinear_tf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msublinear_tf,
[1;32m 2103[0m )
[0;32m-> 2104[0m X [38;5;241m=[39m [38;5;28;43msuper[39;49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mraw_documents[49m[43m)[49m
[1;32m 2105[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf[38;5;241m.[39mfit(X)
[1;32m 2106[0m [38;5;66;03m# X is already a transformed view of raw_documents so[39;00m
[1;32m 2107[0m [38;5;66;03m# we set copy to False[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/base.py:1389[0m, in [0;36m_fit_context.<locals>.decorator.<locals>.wrapper[0;34m(estimator, *args, **kwargs)[0m
[1;32m 1382[0m estimator[38;5;241m.[39m_validate_params()
[1;32m 1384[0m [38;5;28;01mwith[39;00m config_context(
[1;32m 1385[0m skip_parameter_validation[38;5;241m=[39m(
[1;32m 1386[0m prefer_skip_nested_validation [38;5;129;01mor[39;00m global_skip_validation
[1;32m 1387[0m )
[1;32m 1388[0m ):
[0;32m-> 1389[0m [38;5;28;01mreturn[39;00m [43mfit_method[49m[43m([49m[43mestimator[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[43margs[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1354[0m, in [0;36mCountVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 1350[0m [38;5;66;03m# We intentionally don't call the transform method to make[39;00m
[1;32m 1351[0m [38;5;66;03m# fit_transform overridable without unwanted side effects in[39;00m
[1;32m 1352[0m [38;5;66;03m# TfidfVectorizer.[39;00m
[1;32m 1353[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(raw_documents, [38;5;28mstr[39m):
[0;32m-> 1354[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m(
[1;32m 1355[0m [38;5;124m"[39m[38;5;124mIterable over raw text documents expected, string object received.[39m[38;5;124m"[39m
[1;32m 1356[0m )
[1;32m 1358[0m [38;5;28mself[39m[38;5;241m.[39m_validate_ngram_range()
[1;32m 1359[0m [38;5;28mself[39m[38;5;241m.[39m_warn_for_unused_params()
[0;31mValueError[0m: Iterable over raw text documents expected, string object received.
Error: Iterable over raw text documents expected, string object received.
| 0.0739
| 314,585,088
|
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},
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"name": "target_drop",
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},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
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},
"train_features": {
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},
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},
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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}
|
32b825c6ab38bdac29d036938e2f5529
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
corpus = ["I want some pitsa"]
X = vectorizer.fit_transform(corpus)
# X = vectorizer.fit_transform(corpus)
X
|
Out[1]:
<1x3 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
<1x3 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
| 0.005047
| 314,982,400
|
{
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"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
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},
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"type": "csr_matrix",
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},
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},
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},
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"corpus": {
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},
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"f": null,
"f1_score": {
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"type": "function",
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},
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},
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"pred": {
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},
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},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
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},
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},
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},
"vectorizer": {
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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}
|
a6606593fd9d7898f65be7c8916de3ff
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
corpus = ["I want some pitsa"]
X = vectorizer.fit_transform(corpus)
# X = vectorizer.fit_transform(corpus)
X.to_array()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-79dc4f2d01be>:8[0m
[1;32m 6[0m X [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(corpus)
[1;32m 7[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mX[49m[38;5;241;43m.[39;49m[43mto_array[49m()
[0;31mAttributeError[0m: 'csr_matrix' object has no attribute 'to_array'
Error: 'csr_matrix' object has no attribute 'to_array'
| 0.012339
| 315,244,544
|
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},
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"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
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|
f10e4785878466027c9ca0b6ef879e4b
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
corpus = ["I want some pitsa"]
X = vectorizer.fit_transform(corpus)
# X = vectorizer.fit_transform(corpus)
X.to_list()
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-b18237769b50>:8[0m
[1;32m 6[0m X [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(corpus)
[1;32m 7[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mX[49m[38;5;241;43m.[39;49m[43mto_list[49m()
[0;31mAttributeError[0m: 'csr_matrix' object has no attribute 'to_list'
Error: 'csr_matrix' object has no attribute 'to_list'
| 0.011412
| 315,244,544
|
{
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
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"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
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},
"act": null,
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"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
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"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
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"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
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"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
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"lines2": null,
"mask": null,
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"median_duration": null,
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"parse": null,
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"partial": null,
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"pattern": null,
"pivot_counts": null,
"pred": {
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"size": 15528,
"type": "ndarray",
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},
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"stats": null,
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"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
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},
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"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
05b82bd3deacc9eecc3bc8cf330ed1c0
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
corpus = ["I want some pitsa"]
X = vectorizer.fit_transform(corpus)
# X = vectorizer.fit_transform(corpus)
X
|
Out[1]:
<1x3 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
<1x3 sparse matrix of type '<class 'numpy.float64'>'
with 3 stored elements in Compressed Sparse Row format>
| 0.004158
| 315,375,616
|
{
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
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},
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"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
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"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
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"prepare_y": null,
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"session_df": null,
"session_durations": null,
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},
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},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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6465c27241d2d441bb9330acd2ea3cf6
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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34138f7c4c3397b46410f896bbaa5dc7
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.sample(20)
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Out[1]:
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9e9fa424d5d0ab48bd8e092dc3d91d0c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.sample(20)[["code_line_before"]]
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Out[1]:
code_line_before
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3710 [from model_saver import load_model_w_weights]
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2131 [sixth_data_frame]
4697 [valid_features, valid_labels = pickle.load(op...
5092 [dataset_training['Class'] = dataset_training....
2774 [print (after_pca.n_components)]
4507 [np.random.seed(42)]
323 [import numpy as np]
4090 [s_award_name = s_won.str.split('(\d+ )').str[...
2921 [return (weights)]
5358 [fig = draw(g, font_size=8)]
4936 [)]
2813 [Calls.head()]
1248 [num_features = num_features.columns.values]
3348 [tests.test_con_pool(conv2d_maxpool)]
code_line_before
5390 [plt.show()]
2239 [SDG_Targets.head(1)]
2423 [inertia.append(kmeans.inertia_)]
3710 [from model_saver import load_model_w_weights]
2301 ["New Sample, Class Prediction: {}\n".forma...
407 [display(arr_to_img(val_out[sample_i, :, :, :3...
716 [%matplotlib inline]
2131 [sixth_data_frame]
4697 [valid_features, valid_labels = pickle.load(op...
5092 [dataset_training['Class'] = dataset_training....
2774 [print (after_pca.n_components)]
4507 [np.random.seed(42)]
323 [import numpy as np]
4090 [s_award_name = s_won.str.split('(\d+ )').str[...
2921 [return (weights)]
5358 [fig = draw(g, font_size=8)]
4936 [)]
2813 [Calls.head()]
1248 [num_features = num_features.columns.values]
3348 [tests.test_con_pool(conv2d_maxpool)]
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48834e65ba8f95258c628e42637043c1
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.sample(20)[["code_line_before"]]
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Out[1]:
code_line_before
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4591 [print(boston.target.shape)]
2195 [plt.plot(data3_diff)]
5383 [plt.show()]
5484 [pixel_coordinates = {"latitude":25.228928,"lo...
3102 [sentences = [[stem(word) for word in sentence...
1196 [train_step = tf.train.GradientDescentOptimize...
5541 [get_ipython().magic('matplotlib inline')]
5567 [print(prob)]
1592 [plt.imshow(img_d)]
1389 [wifi_data.head()]
5162 [('classifier', LinearSVC(C=100))])]
2034 0
4585 [boston.keys()]
3672 [print(aedesaegypti.size)]
5513 [del df_test, df_train]
373 [severityDf = pd.read_csv("{}/{}".format(path,...
5589 [tpot.export('tpot_iris_pipeline.py')]
777 [plt.show()]
229 [print("Within Set Sum of Squared Errors = " +...
code_line_before
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5383 [plt.show()]
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3102 [sentences = [[stem(word) for word in sentence...
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5541 [get_ipython().magic('matplotlib inline')]
5567 [print(prob)]
1592 [plt.imshow(img_d)]
1389 [wifi_data.head()]
5162 [('classifier', LinearSVC(C=100))])]
2034 0
4585 [boston.keys()]
3672 [print(aedesaegypti.size)]
5513 [del df_test, df_train]
373 [severityDf = pd.read_csv("{}/{}".format(path,...
5589 [tpot.export('tpot_iris_pipeline.py')]
777 [plt.show()]
229 [print("Within Set Sum of Squared Errors = " +...
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82b965e7a86ad0be85b88e0838d9cbb8
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.sample(30)[["code_line_before"]]
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Out[1]:
code_line_before
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4624 [sklearn.metrics.mean_squared_error(bos.PRICE,...
270 [tests.test_generator(generator, tf)]
4310 [isolation(yemen_data)]
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1724 [len(X_train_ridge)]
655 [sin(pi)]
5541 [get_ipython().magic('matplotlib inline')]
3188 [df_stations['traffic'] = df_stations.net_entr...
4834 [l_rnn.output_shape]
814 [return group]
5731 [idps_table]
3590 [plt.imshow(x0_train[5555], cmap="Wistia")]
166 [plt.show()]
1616 [inm]
3401 [topicDict[2][:5]]
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4332 [x_train_reduced.shape, x_test_reduced.shape]
3170 [from treeinterpreter import treeinterpreter a...
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270 [tests.test_generator(generator, tf)]
4310 [isolation(yemen_data)]
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1724 [len(X_train_ridge)]
655 [sin(pi)]
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| 0.013448
| 315,506,688
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0d2dc212bdba7375161db31bf4557a3c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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1553 [n_comps = 1 + np.argmax(vc > 0.95), data_scal...
3563 [import pandas as pd, import matplotlib.pyplot...
3444 [%%time, last_activity = np.max(events.time.va...
4037 [df_filter['Awards'].str.split('.').str.len()....
1482 [from keras import applications, from keras.mo...
29 [len(filter_movies.index.unique())]
2868 [test_X = dft1[columns]]
4950 [ax = df.Income.hist(bins=25), _ = (ax.set_tit...
1208 [W_fc2 = weight_variable([1024, 10]), b_fc2 = ...
791 [folds = 3, batch_size_nn = 16, batch_size_lst...
4405 [iris = datasets.load_iris(), X = iris.data, y...
5711 [df = pd.read_csv('refugee_data.csv')]
3623 [df.shape]
5189 [velo1_17 = pab17.groupby(['gameday_link','pit...
3235 [import csv, import itertools, with open('./Da...
2945 [f2 = open('census.csv', 'r'), census = list(c...
5372 [zips = pd.Series(zipcodes), zips.value_counts()]
409 []
4971 [def covariance(X):, X_mean = np.mean(X,axis=0...
4506 [import numpy as np, import keras, from keras....
4843 [print("Training ..."), n_epochs=100, batches_...
157 [def parse_async_meta(async_result):, """Parse...
907 [hide_code, plt.figure(figsize=(12,5)), x, y =...
5485 [import utils.dc_ccd as ccd]
3486 [A = build_singularity_matrix(A_source, B_vort...
11 [pivot_table['101 Dalmatians (1996)'].notnull(...
3001 [visualize_aam(aam)]
5664 [scatter(y_test, pred, 0.5)]
1767 [def discard_address_number(address_string):, ...
4656 [moving_average = (runningMeanFast (counts_dat...
text
1553 [n_comps = 1 + np.argmax(vc > 0.95), data_scal...
3563 [import pandas as pd, import matplotlib.pyplot...
3444 [%%time, last_activity = np.max(events.time.va...
4037 [df_filter['Awards'].str.split('.').str.len()....
1482 [from keras import applications, from keras.mo...
29 [len(filter_movies.index.unique())]
2868 [test_X = dft1[columns]]
4950 [ax = df.Income.hist(bins=25), _ = (ax.set_tit...
1208 [W_fc2 = weight_variable([1024, 10]), b_fc2 = ...
791 [folds = 3, batch_size_nn = 16, batch_size_lst...
4405 [iris = datasets.load_iris(), X = iris.data, y...
5711 [df = pd.read_csv('refugee_data.csv')]
3623 [df.shape]
5189 [velo1_17 = pab17.groupby(['gameday_link','pit...
3235 [import csv, import itertools, with open('./Da...
2945 [f2 = open('census.csv', 'r'), census = list(c...
5372 [zips = pd.Series(zipcodes), zips.value_counts()]
409 []
4971 [def covariance(X):, X_mean = np.mean(X,axis=0...
4506 [import numpy as np, import keras, from keras....
4843 [print("Training ..."), n_epochs=100, batches_...
157 [def parse_async_meta(async_result):, """Parse...
907 [hide_code, plt.figure(figsize=(12,5)), x, y =...
5485 [import utils.dc_ccd as ccd]
3486 [A = build_singularity_matrix(A_source, B_vort...
11 [pivot_table['101 Dalmatians (1996)'].notnull(...
3001 [visualize_aam(aam)]
5664 [scatter(y_test, pred, 0.5)]
1767 [def discard_address_number(address_string):, ...
4656 [moving_average = (runningMeanFast (counts_dat...
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bdf6442c4cc3a9fa4e56ac8215b7a972
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.text[1]
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Out[1]:
["l_cols = ['user_id','movie_id','rating']",
"r_cols = ['movie_id','title']",
"l = pd.read_csv('u.data', sep='\\t', names=l_cols, usecols=range(3))",
'r = pd.read_csv(\'u.item\', sep=\'|\', names=r_cols, usecols=range(2), encoding = "ISO-8859-1") ## contains unicode characters']
["l_cols = ['user_id','movie_id','rating']", "r_cols = ['movie_id','title']", "l = pd.read_csv('u.data', sep='\\t', names=l_cols, usecols=range(3))", 'r = pd.read_csv(\'u.item\', sep=\'|\', names=r_cols, usecols=range(2), encoding = "ISO-8859-1") ## contains unicode characters']
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55f83d973a7068840eb756a72e6b6589
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = df["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-148621c711c6>:4[0m
[1;32m 1[0m train_features[38;5;241m.[39mtext[[38;5;241m1[39m]
[1;32m 2[0m [38;5;28;01mfrom[39;00m[38;5;250m [39m[38;5;21;01msklearn[39;00m[38;5;21;01m.[39;00m[38;5;21;01mfeature_extraction[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtext[39;00m[38;5;250m [39m[38;5;28;01mimport[39;00m TfidfVectorizer
[0;32m----> 4[0m text [38;5;241m=[39m [43mdf[49m[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m]
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[1;32m 8[0m text
[0;31mNameError[0m: name 'df' is not defined
Error: name 'df' is not defined
| 0.012583
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|
90dd25410231670285015c646b141bff
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text
|
Out[1]:
0 0
1 [import numpy as np]
2 [r = pd.read_csv('u.item', sep='|', names=r_co...
3 [l.head()]
4 [r.head()]
...
5828 [plt.show()]
5829 [print("The average accuracy among all subject...
5830 [srm.fit(movie_data)]
5831 [image_data_shared[subject] = stats.zscore(ima...
5832 [cm[subject] = cm[subject].astype('float') / c...
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0 0
1 [import numpy as np]
2 [r = pd.read_csv('u.item', sep='|', names=r_co...
3 [l.head()]
4 [r.head()]
...
5828 [plt.show()]
5829 [print("The average accuracy among all subject...
5830 [srm.fit(movie_data)]
5831 [image_data_shared[subject] = stats.zscore(ima...
5832 [cm[subject] = cm[subject].astype('float') / c...
Name: code_line_before, Length: 5833, dtype: object
| 0.005078
| 315,768,832
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},
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},
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|
0f90bec8b524ec4221c6fdbff7bf0d3c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
type(text)
|
Out[1]: pandas.core.series.Series
<class 'pandas.core.series.Series'>
| 0.005351
| 315,899,904
|
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},
"Timestamp": null,
"X": {
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},
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221cc05190145a94dc4e5eda4e44cd5b
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
str(text)
|
Out[1]: '0 0\n1 [import numpy as np]\n2 [r = pd.read_csv(\'u.item\', sep=\'|\', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print("The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype(\'float\') / c...\nName: code_line_before, Length: 5833, dtype: object'
'0 0\n1 [import numpy as np]\n2 [r = pd.read_csv(\'u.item\', sep=\'|\', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print("The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype(\'float\') / c...\nName: code_line_before, Length: 5833, dtype: object'
| 0.006071
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},
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},
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|
a0f22d645a3c93a88acc5db2f8146a9f
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text
|
Out[1]:
0 0
1 [import numpy as np]
2 [r = pd.read_csv('u.item', sep='|', names=r_co...
3 [l.head()]
4 [r.head()]
...
5828 [plt.show()]
5829 [print("The average accuracy among all subject...
5830 [srm.fit(movie_data)]
5831 [image_data_shared[subject] = stats.zscore(ima...
5832 [cm[subject] = cm[subject].astype('float') / c...
Name: code_line_before, Length: 5833, dtype: object
0 0
1 [import numpy as np]
2 [r = pd.read_csv('u.item', sep='|', names=r_co...
3 [l.head()]
4 [r.head()]
...
5828 [plt.show()]
5829 [print("The average accuracy among all subject...
5830 [srm.fit(movie_data)]
5831 [image_data_shared[subject] = stats.zscore(ima...
5832 [cm[subject] = cm[subject].astype('float') / c...
Name: code_line_before, Length: 5833, dtype: object
| 0.006427
| 315,899,904
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},
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},
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},
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},
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"type": "list",
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},
"train_features": {
"name": "train_features",
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},
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},
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}
|
fe89ead02cf5d39d54bd67ff390313dc
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(x))
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-9515e935ac4b>:8[0m
[1;32m 4[0m text [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m]
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mtext[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
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[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
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|
8752bf4554dcbdf2b85e34dd4d2ec962
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(list(x))
|
[0;36m File [0;32m<ipython-input-1-7b4151cc6089>:8[0;36m[0m
[0;31m text.apply(lambda x: " ".join(list(x))[0m
[0m ^[0m
[0;31mSyntaxError[0m[0;31m:[0m unexpected EOF while parsing
Error: None
| 0.003906
| 332,152,832
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},
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"size": 541424,
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"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
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"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
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"name": "train_features",
"size": 5457229,
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},
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"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
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"txt": null,
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"size": 1826459,
"type": "DataFrame",
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},
"vectorizer": {
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"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
b6a1b6c945dfeb020b2c0d835db1f1c1
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["code_line_before"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(list(x)))
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-ba5fe71dd87a>:8[0m
[1;32m 4[0m text [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m]
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mtext[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[38;5;28;43mlist[39;49m[43m([49m[43mx[49m[43m)[49m[43m)[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
[1;32m 4790[0m [38;5;28mself[39m,
[1;32m 4791[0m func: AggFuncType,
[0;32m (...)[0m
[1;32m 4796[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
[1;32m 4797[0m ) [38;5;241m-[39m[38;5;241m>[39m DataFrame [38;5;241m|[39m Series:
[1;32m 4798[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4799[0m [38;5;124;03m Invoke function on values of Series.[39;00m
[1;32m 4800[0m
[0;32m (...)[0m
[1;32m 4915[0m [38;5;124;03m dtype: float64[39;00m
[1;32m 4916[0m [38;5;124;03m """[39;00m
[0;32m-> 4917[0m [38;5;28;01mreturn[39;00m [43mSeriesApply[49m[43m([49m
[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
[1;32m 4920[0m [43m [49m[43mconvert_dtype[49m[38;5;241;43m=[39;49m[43mconvert_dtype[49m[43m,[49m
[1;32m 4921[0m [43m [49m[43mby_row[49m[38;5;241;43m=[39;49m[43mby_row[49m[43m,[49m
[1;32m 4922[0m [43m [49m[43margs[49m[38;5;241;43m=[39;49m[43margs[49m[43m,[49m
[1;32m 4923[0m [43m [49m[43mkwargs[49m[38;5;241;43m=[39;49m[43mkwargs[49m[43m,[49m
[1;32m 4924[0m [43m [49m[43m)[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1507[0m, in [0;36mSeriesApply.apply_standard[0;34m(self)[0m
[1;32m 1501[0m [38;5;66;03m# row-wise access[39;00m
[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
[1;32m 1506[0m action [38;5;241m=[39m [38;5;124m"[39m[38;5;124mignore[39m[38;5;124m"[39m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(obj[38;5;241m.[39mdtype, CategoricalDtype) [38;5;28;01melse[39;00m [38;5;28;01mNone[39;00m
[0;32m-> 1507[0m mapped [38;5;241m=[39m [43mobj[49m[38;5;241;43m.[39;49m[43m_map_values[49m[43m([49m
[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
[1;32m 1509[0m [43m[49m[43m)[49m
[1;32m 1511[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(mapped) [38;5;129;01mand[39;00m [38;5;28misinstance[39m(mapped[[38;5;241m0[39m], ABCSeries):
[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
[1;32m 1514[0m [38;5;28;01mreturn[39;00m obj[38;5;241m.[39m_constructor_expanddim([38;5;28mlist[39m(mapped), index[38;5;241m=[39mobj[38;5;241m.[39mindex)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:921[0m, in [0;36mIndexOpsMixin._map_values[0;34m(self, mapper, na_action, convert)[0m
[1;32m 918[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(arr, ExtensionArray):
[1;32m 919[0m [38;5;28;01mreturn[39;00m arr[38;5;241m.[39mmap(mapper, na_action[38;5;241m=[39mna_action)
[0;32m--> 921[0m [38;5;28;01mreturn[39;00m [43malgorithms[49m[38;5;241;43m.[39;49m[43mmap_array[49m[43m([49m[43marr[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43mna_action[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/algorithms.py:1743[0m, in [0;36mmap_array[0;34m(arr, mapper, na_action, convert)[0m
[1;32m 1741[0m values [38;5;241m=[39m arr[38;5;241m.[39mastype([38;5;28mobject[39m, copy[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
[1;32m 1742[0m [38;5;28;01mif[39;00m na_action [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
[0;32m-> 1743[0m [38;5;28;01mreturn[39;00m [43mlib[49m[38;5;241;43m.[39;49m[43mmap_infer[49m[43m([49m[43mvalues[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
[1;32m 1744[0m [38;5;28;01melse[39;00m:
[1;32m 1745[0m [38;5;28;01mreturn[39;00m lib[38;5;241m.[39mmap_infer_mask(
[1;32m 1746[0m values, mapper, mask[38;5;241m=[39misna(values)[38;5;241m.[39mview(np[38;5;241m.[39muint8), convert[38;5;241m=[39mconvert
[1;32m 1747[0m )
File [0;32mlib.pyx:2972[0m, in [0;36mpandas._libs.lib.map_infer[0;34m()[0m
File [0;32m<ipython-input-1-ba5fe71dd87a>:8[0m, in [0;36m<lambda>[0;34m(x)[0m
[1;32m 4[0m text [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m]
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m text[38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin([38;5;28;43mlist[39;49m[43m([49m[43mx[49m[43m)[49m))
[0;31mTypeError[0m: 'int' object is not iterable
Error: 'int' object is not iterable
| 0.033269
| 332,546,048
|
{
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
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"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
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"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
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"parts": null,
"pattern": null,
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"pred": {
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"size": 15528,
"type": "ndarray",
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},
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},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
809bede31fde9db9ae1d6bdf17c8212e
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0]["code_line_after"] = []
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-071df62a9534>:18[0m
[1;32m 14[0m test_features[38;5;241m.[39mindex [38;5;241m=[39m [38;5;28mrange[39m(test_features[38;5;241m.[39mshape[[38;5;241m0[39m])
[1;32m 17[0m train_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[0;32m---> 18[0m [43mtrain_features[49m[43m[[49m[43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m[43m [49m[38;5;241;43m==[39;49m[43m [49m[38;5;241;43m0[39;49m[43m][49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m [38;5;241m=[39m []
[1;32m 20[0m test_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[1;32m 22[0m validation_features[38;5;241m.[39mfillna([38;5;241m0[39m,inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py:4311[0m, in [0;36mDataFrame.__setitem__[0;34m(self, key, value)[0m
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[1;32m 4309[0m [38;5;28;01melse[39;00m:
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[0;32m-> 4311[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_set_item[49m[43m([49m[43mkey[49m[43m,[49m[43m [49m[43mvalue[49m[43m)[49m
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[1;32m 4516[0m [38;5;124;03m Add series to DataFrame in specified column.[39;00m
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[0;32m (...)[0m
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[1;32m 4523[0m [38;5;124;03m """[39;00m
[0;32m-> 4524[0m value, refs [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_sanitize_column[49m[43m([49m[43mvalue[49m[43m)[49m
[1;32m 4526[0m [38;5;28;01mif[39;00m (
[1;32m 4527[0m key [38;5;129;01min[39;00m [38;5;28mself[39m[38;5;241m.[39mcolumns
[1;32m 4528[0m [38;5;129;01mand[39;00m value[38;5;241m.[39mndim [38;5;241m==[39m [38;5;241m1[39m
[1;32m 4529[0m [38;5;129;01mand[39;00m [38;5;129;01mnot[39;00m [38;5;28misinstance[39m(value[38;5;241m.[39mdtype, ExtensionDtype)
[1;32m 4530[0m ):
[1;32m 4531[0m [38;5;66;03m# broadcast across multiple columns if necessary[39;00m
[1;32m 4532[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m [38;5;28mself[39m[38;5;241m.[39mcolumns[38;5;241m.[39mis_unique [38;5;129;01mor[39;00m [38;5;28misinstance[39m([38;5;28mself[39m[38;5;241m.[39mcolumns, MultiIndex):
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py:5266[0m, in [0;36mDataFrame._sanitize_column[0;34m(self, value)[0m
[1;32m 5263[0m [38;5;28;01mreturn[39;00m _reindex_for_setitem(value, [38;5;28mself[39m[38;5;241m.[39mindex)
[1;32m 5265[0m [38;5;28;01mif[39;00m is_list_like(value):
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File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/common.py:573[0m, in [0;36mrequire_length_match[0;34m(data, index)[0m
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[1;32m 571[0m [38;5;124;03m"""[39;00m
[1;32m 572[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(data) [38;5;241m!=[39m [38;5;28mlen[39m(index):
[0;32m--> 573[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m(
[1;32m 574[0m [38;5;124m"[39m[38;5;124mLength of values [39m[38;5;124m"[39m
[1;32m 575[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124m([39m[38;5;132;01m{[39;00m[38;5;28mlen[39m(data)[38;5;132;01m}[39;00m[38;5;124m) [39m[38;5;124m"[39m
[1;32m 576[0m [38;5;124m"[39m[38;5;124mdoes not match length of index [39m[38;5;124m"[39m
[1;32m 577[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124m([39m[38;5;132;01m{[39;00m[38;5;28mlen[39m(index)[38;5;132;01m}[39;00m[38;5;124m)[39m[38;5;124m"[39m
[1;32m 578[0m )
[0;31mValueError[0m: Length of values (0) does not match length of index (403)
Error: Length of values (0) does not match length of index (403)
| 0.148244
| 361,746,432
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|
226ee1e90d8779b5bd54c121add3d376
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0]
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
| 0.100984
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},
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
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|
d2b2245a11994aa44ebb570e10f3e749
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = []
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-82f9041a854b>:18[0m
[1;32m 14[0m test_features[38;5;241m.[39mindex [38;5;241m=[39m [38;5;28mrange[39m(test_features[38;5;241m.[39mshape[[38;5;241m0[39m])
[1;32m 17[0m train_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[0;32m---> 18[0m [43mtrain_features[49m[43m[[49m[43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m[43m [49m[38;5;241;43m==[39;49m[43m [49m[38;5;241;43m0[39;49m[43m][49m[38;5;241;43m.[39;49m[43mloc[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m [38;5;241m=[39m []
[1;32m 20[0m test_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[1;32m 22[0m validation_features[38;5;241m.[39mfillna([38;5;241m0[39m,inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:911[0m, in [0;36m_LocationIndexer.__setitem__[0;34m(self, key, value)[0m
[1;32m 908[0m [38;5;28mself[39m[38;5;241m.[39m_has_valid_setitem_indexer(key)
[1;32m 910[0m iloc [38;5;241m=[39m [38;5;28mself[39m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39mname [38;5;241m==[39m [38;5;124m"[39m[38;5;124miloc[39m[38;5;124m"[39m [38;5;28;01melse[39;00m [38;5;28mself[39m[38;5;241m.[39mobj[38;5;241m.[39miloc
[0;32m--> 911[0m [43miloc[49m[38;5;241;43m.[39;49m[43m_setitem_with_indexer[49m[43m([49m[43mindexer[49m[43m,[49m[43m [49m[43mvalue[49m[43m,[49m[43m [49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mname[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:1932[0m, in [0;36m_iLocIndexer._setitem_with_indexer[0;34m(self, indexer, value, name)[0m
[1;32m 1929[0m indexer, missing [38;5;241m=[39m convert_missing_indexer(indexer)
[1;32m 1931[0m [38;5;28;01mif[39;00m missing:
[0;32m-> 1932[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_setitem_with_indexer_missing[49m[43m([49m[43mindexer[49m[43m,[49m[43m [49m[43mvalue[49m[43m)[49m
[1;32m 1933[0m [38;5;28;01mreturn[39;00m
[1;32m 1935[0m [38;5;28;01mif[39;00m name [38;5;241m==[39m [38;5;124m"[39m[38;5;124mloc[39m[38;5;124m"[39m:
[1;32m 1936[0m [38;5;66;03m# must come after setting of missing[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/indexing.py:2306[0m, in [0;36m_iLocIndexer._setitem_with_indexer_missing[0;34m(self, indexer, value)[0m
[1;32m 2303[0m [38;5;28;01mif[39;00m is_list_like_indexer(value):
[1;32m 2304[0m [38;5;66;03m# must have conforming columns[39;00m
[1;32m 2305[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(value) [38;5;241m!=[39m [38;5;28mlen[39m([38;5;28mself[39m[38;5;241m.[39mobj[38;5;241m.[39mcolumns):
[0;32m-> 2306[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m([38;5;124m"[39m[38;5;124mcannot set a row with mismatched columns[39m[38;5;124m"[39m)
[1;32m 2308[0m value [38;5;241m=[39m Series(value, index[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mobj[38;5;241m.[39mcolumns, name[38;5;241m=[39mindexer)
[1;32m 2310[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m [38;5;28mlen[39m([38;5;28mself[39m[38;5;241m.[39mobj):
[1;32m 2311[0m [38;5;66;03m# We will ignore the existing dtypes instead of using[39;00m
[1;32m 2312[0m [38;5;66;03m# internals.concat logic[39;00m
[0;31mValueError[0m: cannot set a row with mismatched columns
Error: cannot set a row with mismatched columns
| 0.120661
| 378,507,264
|
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"MultinomialNB": null,
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},
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},
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},
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},
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},
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},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
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"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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}
|
83309e2e13f2a6caa3ffe80831f1d05b
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0]["code_line_after"] = []
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-071df62a9534>:18[0m
[1;32m 14[0m test_features[38;5;241m.[39mindex [38;5;241m=[39m [38;5;28mrange[39m(test_features[38;5;241m.[39mshape[[38;5;241m0[39m])
[1;32m 17[0m train_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[0;32m---> 18[0m [43mtrain_features[49m[43m[[49m[43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m[43m [49m[38;5;241;43m==[39;49m[43m [49m[38;5;241;43m0[39;49m[43m][49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m"[39;49m[43m][49m [38;5;241m=[39m []
[1;32m 20[0m test_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[1;32m 22[0m validation_features[38;5;241m.[39mfillna([38;5;241m0[39m,inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py:4311[0m, in [0;36mDataFrame.__setitem__[0;34m(self, key, value)[0m
[1;32m 4308[0m [38;5;28mself[39m[38;5;241m.[39m_setitem_array([key], value)
[1;32m 4309[0m [38;5;28;01melse[39;00m:
[1;32m 4310[0m [38;5;66;03m# set column[39;00m
[0;32m-> 4311[0m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_set_item[49m[43m([49m[43mkey[49m[43m,[49m[43m [49m[43mvalue[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/frame.py:4524[0m, in [0;36mDataFrame._set_item[0;34m(self, key, value)[0m
[1;32m 4514[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21m_set_item[39m([38;5;28mself[39m, key, value) [38;5;241m-[39m[38;5;241m>[39m [38;5;28;01mNone[39;00m:
[1;32m 4515[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4516[0m [38;5;124;03m Add series to DataFrame in specified column.[39;00m
[1;32m 4517[0m
[0;32m (...)[0m
[1;32m 4522[0m [38;5;124;03m ensure homogeneity.[39;00m
[1;32m 4523[0m [38;5;124;03m """[39;00m
[0;32m-> 4524[0m value, refs [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_sanitize_column[49m[43m([49m[43mvalue[49m[43m)[49m
[1;32m 4526[0m [38;5;28;01mif[39;00m (
[1;32m 4527[0m key [38;5;129;01min[39;00m [38;5;28mself[39m[38;5;241m.[39mcolumns
[1;32m 4528[0m [38;5;129;01mand[39;00m value[38;5;241m.[39mndim [38;5;241m==[39m [38;5;241m1[39m
[1;32m 4529[0m [38;5;129;01mand[39;00m [38;5;129;01mnot[39;00m [38;5;28misinstance[39m(value[38;5;241m.[39mdtype, ExtensionDtype)
[1;32m 4530[0m ):
[1;32m 4531[0m [38;5;66;03m# broadcast across multiple columns if necessary[39;00m
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},
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"name": "train_features",
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},
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|
51126dba96fbe14ab31286a9ef63ffa4
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0]["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-d1263862e539>:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features["code_line_after"] == 0]["code_line_after"] = ""
| 0.032139
| 385,126,400
|
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"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
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},
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},
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},
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},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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},
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"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
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},
"transform": null,
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"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
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"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
f45a75bfa3a86fc09eb57c56a0fb2773
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-9d5e1f008ffb>:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
| 0.032322
| 386,199,552
|
{
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"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
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},
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"clf": {
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"type": "LGBMClassifier",
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},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
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},
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},
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"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
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"stats": null,
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"target": {
"name": "target",
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},
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"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
75e3740473daa54dd80c91a896a484cf
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# train_features.drop(columns=["filename"])
train_features.columns
|
Out[1]:
Index(['filename', 'cell_type', 'cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count',
'text/plain', 'image/png', 'text/html', 'execute_result',
'display_data', 'stream', 'error', 'text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading',
'packages_info', 'primary_label', 'helper_functions', 'load_data',
'data_exploration', 'data_preprocessing', 'evaluation', 'modelling',
'prediction', 'result_visualization', 'save_results', 'comment_only'],
dtype='object')
Index(['filename', 'cell_type', 'cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count',
'text/plain', 'image/png', 'text/html', 'execute_result',
'display_data', 'stream', 'error', 'text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading',
'packages_info', 'primary_label', 'helper_functions', 'load_data',
'data_exploration', 'data_preprocessing', 'evaluation', 'modelling',
'prediction', 'result_visualization', 'save_results', 'comment_only'],
dtype='object')
| 0.004823
| 386,199,552
|
{
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"name": "TfidfVectorizer",
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},
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"type": "csr_matrix",
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},
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"action_time_col": null,
"actions": null,
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"ax": null,
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"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
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"data": null,
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"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
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"name": "features_path",
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"type": "str",
"value": "data/task2/"
},
"fig": null,
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"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
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},
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"target": {
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},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
b4f68eafe8061bc6598815c6570e21b7
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# train_features.drop(columns=["filename"])
train_features
|
Out[1]:
filename cell_type ... save_results comment_only
0 nb_54880.ipynb code ... 0 0
1 nb_54880.ipynb code ... 0 0
2 nb_54880.ipynb code ... 0 0
3 nb_54880.ipynb code ... 0 0
4 nb_54880.ipynb code ... 0 0
... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
filename cell_type ... save_results comment_only
0 nb_54880.ipynb code ... 0 0
1 nb_54880.ipynb code ... 0 0
2 nb_54880.ipynb code ... 0 0
3 nb_54880.ipynb code ... 0 0
4 nb_54880.ipynb code ... 0 0
... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
| 0.02972
| 386,199,552
|
{
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
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"size": 136,
"type": "function",
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},
"f_statistic": null,
"features_path": {
"name": "features_path",
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},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
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"partial": null,
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"pred": {
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"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
668de259a1e2408bfec4e9c2076f099b
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
# train_features.drop(columns=["filename"])
train_features["code_line_after"]
|
Out[1]:
0 [l_cols = ['user_id','movie_id','rating']]
1 [l.head()]
2 [r.head()]
3 [movies = pd.merge(l,r)]
4 [movies.head()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
0 [l_cols = ['user_id','movie_id','rating']]
1 [l.head()]
2 [r.head()]
3 [movies = pd.merge(l,r)]
4 [movies.head()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
| 0.006393
| 386,199,552
|
{
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
ea52d7311f599a97f1c7e1e1873f4b64
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features
|
Out[1]:
filename cell_type ... save_results comment_only
0 nb_54880.ipynb code ... 0 0
1 nb_54880.ipynb code ... 0 0
2 nb_54880.ipynb code ... 0 0
3 nb_54880.ipynb code ... 0 0
4 nb_54880.ipynb code ... 0 0
... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
filename cell_type ... save_results comment_only
0 nb_54880.ipynb code ... 0 0
1 nb_54880.ipynb code ... 0 0
2 nb_54880.ipynb code ... 0 0
3 nb_54880.ipynb code ... 0 0
4 nb_54880.ipynb code ... 0 0
... ... ... ... ... ...
5828 nb_95821.ipynb code ... 0 0
5829 nb_95821.ipynb code ... 0 0
5830 nb_95821.ipynb code ... 0 0
5831 nb_95821.ipynb code ... 0 0
5832 nb_95821.ipynb code ... 0 0
[5833 rows x 32 columns]
| 0.029281
| 386,199,552
|
{
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
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21e517922e381fbe6281f18b2797c0b3
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features.columns
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3af726819da8f96f8c19d3b3829f83a8
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features["packages_info"]
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82b6ae955d94647a0110c001cde71492
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
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train_features["packages_info"].unique()
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File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:1025[0m, in [0;36mIndexOpsMixin.unique[0;34m(self)[0m
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Error: ('Lengths must match to compare', (5833,), (0,))
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11a44f46de86ea08736f64fabb4e6a43
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features["packages_info"] != "[]""
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[0;36m File [0;32m<ipython-input-1-a8d2718a0e1b>:1[0;36m[0m
[0;31m train_features["packages_info"] != "[]""[0m
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Error: None
| 0.005492
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|
a4235924eb30ad6c7237c76ee75bb80c
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
train_features["packages_info"] != "[]"
|
Out[1]:
0 True
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...
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5831 True
5832 True
Name: packages_info, Length: 5833, dtype: bool
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| 0.00583
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|
c64fbf3512950e22e4e4c8c3ec1a13e5
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading']].fillna([], inplace=True)
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0,inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
[0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-61dfcdd20181>:17[0m
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[1;32m 14[0m test_features[38;5;241m.[39mindex [38;5;241m=[39m [38;5;28mrange[39m(test_features[38;5;241m.[39mshape[[38;5;241m0[39m])
[0;32m---> 17[0m [43mtrain_features[49m[43m[[49m[43m[[49m[38;5;124;43m'[39;49m[38;5;124;43mtext[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mcomment[39;49m[38;5;124;43m'[39;49m[43m,[49m
[1;32m 18[0m [43m [49m[38;5;124;43m'[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mcode_line_after[39;49m[38;5;124;43m'[39;49m[43m,[49m[43m [49m[38;5;124;43m'[39;49m[38;5;124;43mmarkdown_heading[39;49m[38;5;124;43m'[39;49m[43m][49m[43m][49m[38;5;241;43m.[39;49m[43mfillna[49m[43m([49m[43m[[49m[43m][49m[43m,[49m[43m [49m[43minplace[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
[1;32m 19[0m train_features[38;5;241m.[39mfillna([38;5;241m0[39m, inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[1;32m 21[0m [38;5;66;03m# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/generic.py:7293[0m, in [0;36mNDFrame.fillna[0;34m(self, value, method, axis, inplace, limit, downcast)[0m
[1;32m 7286[0m [38;5;28;01mif[39;00m ctr [38;5;241m<[39m[38;5;241m=[39m ref_count:
[1;32m 7287[0m warnings[38;5;241m.[39mwarn(
[1;32m 7288[0m _chained_assignment_warning_method_msg,
[1;32m 7289[0m [38;5;167;01mFutureWarning[39;00m,
[1;32m 7290[0m stacklevel[38;5;241m=[39m[38;5;241m2[39m,
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[0;31mTypeError[0m: "value" parameter must be a scalar or dict, but you passed a "list"
Error: "value" parameter must be a scalar or dict, but you passed a "list"
| 0.303702
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|
d6892ac811d19bf53671c1c9d4539106
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading']].fillna("", inplace=True)
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-0f954cf76115>:17: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[['text', 'comment',
| 0.031623
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},
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"name": "target_drop",
"size": 152,
"type": "list",
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},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
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"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
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"time": null,
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"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5457229,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
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},
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"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"y_val_primary": null
}
|
7c1756b2e9c89787cc63271ace4740ab
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c
|
import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading']] = train_features[['text', 'comment',
'code_line_before', 'code_line_after', 'markdown_heading']].fillna("", inplace=True)
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
|
(5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-9d3507acdcd8>:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
'code_line_before', 'code_line_after', 'markdown_heading']] = train_features[['text', 'comment',
| 0.122979
| 424,005,632
|
{
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 2)\t0.5773502691896258\n (0, 1)\t0.5773502691896258\n (0, 0)\t0.5773502691896258"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 541424,
"type": "Series",
"value": "0 0\n1 [import numpy as np]\n2 [r = pd.read_csv('u.item', sep='|', names=r_co...\n3 [l.head()]\n4 [r.head()]\n ... \n5828 [plt.show()]\n5829 [print(\"The average accuracy among all subject...\n5830 [srm.fit(movie_data)]\n5831 [image_data_shared[subject] = stats.zscore(ima...\n5832 [cm[subject] = cm[subject].astype('float') / c...\nName: code_line_before, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 2932213,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
}
|
823db64130a83ce99b38ed3297f76dd6
|
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