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 |
|---|---|---|---|---|---|---|
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])
val... | (5833, 32)
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(1918, 21)
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... | 201c0cafbfc6160333215ff621876c7f |
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 ... | 0.033076 | 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)... | 0.103024 | 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 |
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 ... | 0.032081 | 141,406,208 | {
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... | 1c987ec71827805977bbfbe6534ce6e7 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | !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[1... | 1.124286 | 141,406,208 | {
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... | 3e01ee2a6b103aefe548045db047bf67 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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... | 0.004836 | 141,406,208 | {
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... | 8d213e547d93f43c02dfdbfb7d02f981 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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',
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... | 85e05b1bc24514afa12ce1eb67d5c13f |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c |
target_drop = ["primary_label", "load_data", "helper_functions",
"data_preprocessing", "data_exploration", "modelling",
"prediction", "evaluation", "result_visualization",
"save_results", "comment_only"]
target = train_features["primary_label"]
train_features.drop(columns=target, inpl... | 0.005346 | 142,454,784 | {
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... | 391dd78a289c5e851457f2774a964c17 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.drop(columns=["filename"]) | Out[1]:
cell_type ... primary_label
0 code ... helper_functions
1 code ... load_data
2 code ... data_exploration
3 code ... data_exploration
4 code ... data_preprocessing
... ... ... ...
5828 code ... ev... | 0.026532 | 142,454,784 | {
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... | 9d75d48b7ecfe4a0ee4f1bc57bf4f1d2 |
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;31mN... | 0.01199 | 142,454,784 | {
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... | 592b6cc09515ff26c5ca278ae654dc28 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | !pip install sklearn
!pip install lightgbm as lgb | 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[1... | 1.064995 | 142,454,784 | {
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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[1... | 1.267826 | 142,454,784 | {
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... | 401633d321a74dca7f418c2f673aacf5 |
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;31mN... | 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;31mN... | 0.010982 | 142,454,784 | {
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... | eeae56585cdd8d4f9e25581c8103ba2c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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... | 0.005477 | 142,454,784 | {
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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_ty... | 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;31mN... | 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+'validat... | (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 | clf.train(train[['cell_type', 'cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]) | [0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-a2c2fc20951a>:1[0m
[0;32m----> 1[0m [43mclf[49m[38;5;241;43m.[39;49m[43mtrain[49m(train[[[38;5;124m'[39m[... | 0.026057 | 236,068,864 | {
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... | 7c0d7d86287768aab34d6876fd93d3c9 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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
[0;32m----> 1[0m [43mclf[49m[38;5;241;43m.[39;49m[43mfit[49m[43m([49m[43mtrain_feature... | 0.39015 | 247,578,624 | {
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... | afc00ece715a191cb4585fc2bf672fdf |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | clf.fit(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']], target) | Out[1]: LGBMClassifier()
LGBMClassifier()
| 0.845052 | 256,839,680 | {
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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 | 265,506,816 | {
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... | 9e1697bbf301a9c17ec6c6ce6d99b381 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]) target) | [0;36m File [0;32m<ipython-input-1-c806046091db>:2[0;36m[0m
[0;31m 'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]) target)[0m
[0m ^[0m
[0;31mSyntaxError[0m[0;31m:[0m invalid syntax
Error: None
| 0.003835 | 265,506,816 | {
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... | d0392ef2c011e9e34ea0dedecde6b050 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[['cell_number', 'execution_count',
'linesofcomment', 'linesofcode', 'variable_count', 'function_count']]), target) | [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;12... | 0.011244 | 265,637,888 | {
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"Timestamp": null,
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"X_test": 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_feat... | (5833, 32)
(1927, 32)
(1918, 21)
| 0.077509 | 282,116,096 | {
"CountVectorizer": null,
"DF": null,
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"X_test": 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 | {
"CountVectorizer": null,
"DF": null,
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"Timestamp": null,
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"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
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"X_columns": null,
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"X_test": null,
... | 22c34da5662945830f579b2ffb794851 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | clf = lgb.LGBMClassifier() | 0.004447 | 283,426,816 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
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"X_test": null,
... | d88608dfc195a5c3c74f6ffe8391cf5e | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_columns = ['cell_number', 'execution_count', 'linesofcomment', 'linesofcode',
'variable_count', 'function_count', 'display_data', 'stream', 'error']
clf.fit(train_features[train_columns], target) | Out[1]: LGBMClassifier()
LGBMClassifier()
| 1.402952 | 288,612,352 | {
"CountVectorizer": null,
"DF": null,
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"RandomForestClassifier": null,
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"Timestamp": null,
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"X_columns": null,
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"X_test": null,
... | 9791f5f3890226ec9c47e1a648ac2db2 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[train_columns]), target) | Out[1]: 0.8400480027430138
0.8400480027430138
| 0.037897 | 288,743,424 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
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"Timestamp": null,
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"X1": null,
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"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 92f9845971518f06100a04b0d119a180 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | target.value_counts() | Out[1]:
primary_label
data_exploration 1664
data_preprocessing 1396
modelling 922
helper_functions 467
load_data 434
result_visualization 292
evaluation 233
prediction 180
comment_only 135
save_results 110
Name: c... | 0.005388 | 288,890,880 | {
"CountVectorizer": null,
"DF": null,
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"RandomForestClassifier": null,
"Readliner": null,
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"X_test": null,
... | a8ddef30b7247b3ed05e8c7d5d81ba69 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | target.value_counts(normalize=True) | Out[1]:
primary_label
data_exploration 0.285273
data_preprocessing 0.239328
modelling 0.158066
helper_functions 0.080062
load_data 0.074404
result_visualization 0.050060
evaluation 0.039945
prediction 0.030859
comment_only 0.023144
... | 0.006626 | 288,890,880 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 828f07fc32d8a3a2539ed586e31eebcf |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | target.value_counts(normalize=True) | Out[1]:
primary_label
data_exploration 0.285273
data_preprocessing 0.239328
modelling 0.158066
helper_functions 0.080062
load_data 0.074404
result_visualization 0.050060
evaluation 0.039945
prediction 0.030859
comment_only 0.023144
... | 0.006949 | 288,890,880 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
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"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 3... | 0.395222 | 308,031,488 | {
"CountVectorizer": null,
"DF": null,
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"TfidfTransformer": null,
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"Timestamp": null,
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"X_columns": null,
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"X_test": null,
... | e427c292517ee0760b4d3897207f6bf4 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.columns
test_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... | 0.00602 | 308,031,488 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
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"X_test": null,
... | 419868f4e2cfd5ebcb1492c87e48177b |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.columns
validation_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... | 0.005921 | 308,031,488 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
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"X_test": null,
... | 9a424ea348b564df03afa869be4bd98d |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"]) | Out[1]: 0.5365853658536586
0.5365853658536586
| 0.018844 | 308,031,488 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
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"X3": null,
"X_columns": null,
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"X_test": null,
... | 5e761cde4a9be16cd38f668aaf3c0bea |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"]) | Out[1]: 0.5365853658536586
0.5365853658536586
| 0.021231 | 308,031,488 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
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"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | a41635c6659cbd9aec0fa8fe487d4cae |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"]) | Out[1]: 0.5365853658536586
0.5365853658536586
| 0.017821 | 308,031,488 | {
"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,
... | 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 | {
"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,
... | 06428dc58437cded19ee2568dacfd6f4 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(validation_features[train_columns]), validation_features["primary_label"]) | Out[1]: 0.5365853658536586
0.5365853658536586
| 0.018781 | 308,031,488 | {
"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,
... | a1396408444015dd3203a2e6b4be29d7 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | target.value_counts(normalize=True) | Out[1]:
primary_label
data_exploration 0.285273
data_preprocessing 0.239328
modelling 0.158066
helper_functions 0.080062
load_data 0.074404
result_visualization 0.050060
evaluation 0.039945
prediction 0.030859
comment_only 0.023144
... | 0.006623 | 308,031,488 | {
"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,
... | 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,
... | 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 | {
"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,
... | 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 | {
"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,
... | 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 | {
"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,
... | 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 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": 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 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
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"X": null,
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"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
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"Timestamp": null,
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"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"CountVectorizer": null,
"DF": null,
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"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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
0.5479133178319338
| 0.021595 | 308,424,704 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"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,
... | 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
0.5479133178319338
| 0.020237 | 308,555,776 | {
"CountVectorizer": null,
"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"Timestamp": null,
"X": null,
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
... | 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 | {
"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,
... | 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 | {
"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,
... | 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 | {
"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,
... | 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;... | 0.0739 | 314,585,088 | {
"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'>"
},... | 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 | {
"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'>"
},... | 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 ... | 0.012339 | 315,244,544 | {
"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'>"
},... | 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 ... | 0.011412 | 315,244,544 | {
"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'>"
},... | 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 | {
"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'>"
},... | 6268f9932d5eced3665b564aa9ed71f4 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1] | 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... | 0.003531 | 315,375,616 | {
"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'>"
},... | 7e9dfec8ac1fa2840d0009c105759825 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1] | 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... | 0.005863 | 315,375,616 | {
"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'>"
},... | 6465c27241d2d441bb9330acd2ea3cf6 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1] | 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... | 0.005256 | 315,375,616 | {
"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'>"
},... | 34138f7c4c3397b46410f896bbaa5dc7 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.sample(20) | Out[1]:
filename cell_type ... save_results comment_only
5134 nb_78623.ipynb code ... 0 0
5195 nb_8001.ipynb code ... 0 0
1370 nb_18303.ipynb code ... 0 0
2432 nb_33105.ipynb code ... 0 ... | 0.039783 | 315,506,688 | {
"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'>"
},... | 9e9fa424d5d0ab48bd8e092dc3d91d0c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.sample(20)[["code_line_before"]] | Out[1]:
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 Predi... | 0.011084 | 315,506,688 | {
"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'>"
},... | 48834e65ba8f95258c628e42637043c1 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.sample(20)[["code_line_before"]] | Out[1]:
code_line_before
4672 [train_df = train_df.drop('sub_area', 1)]
4591 [print(boston.target.shape)]
2195 [plt.plot(data3_diff)]
5383 [plt.show()]
5484 [pixel_coordinates = {"la... | 0.011213 | 315,506,688 | {
"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'>"
},... | 82b965e7a86ad0be85b88e0838d9cbb8 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.sample(30)[["code_line_before"]] | Out[1]:
code_line_before
4332 [x_train_reduced.shape, x_test_reduced.shape]
3170 [from treeinterpreter import treeinterpreter a...
3115 [model.most_similar("game")]
4624 [sklearn.metrics.mean_squared_error(bos.PRICE,...
270 [tests.test_g... | 0.013448 | 315,506,688 | {
"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'>"
},... | 0d2dc212bdba7375161db31bf4557a3c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.sample(30)[["text"]] | Out[1]:
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 applic... | 0.014814 | 315,506,688 | {
"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'>"
},... | bdf6442c4cc3a9fa4e56ac8215b7a972 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1] | 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... | 0.004782 | 315,637,760 | {
"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'>"
},... | 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... | 0.012583 | 315,768,832 | {
"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'>"
},... | 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()]
... | 0.005078 | 315,768,832 | {
"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'>"
},... | 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 | {
"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'>"
},... | 221cc05190145a94dc4e5eda4e44cd5b |
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)
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 ... | 0.006071 | 315,899,904 | {
"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'>"
},... | 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()]
... | 0.006427 | 315,899,904 | {
"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'>"
},... | 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_be... | 0.286633 | 332,152,832 | {
"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'>"
},... | 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 | {
"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'>"
},... | 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_be... | 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'>"
},... | 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_feat... | [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(te... | 0.148244 | 361,746,432 | {
"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'>"
},... | 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_feat... | (5833, 32)
(1927, 32)
(1918, 21)
| 0.100984 | 378,081,280 | {
"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'>"
},... | 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_feat... | [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(te... | 0.120661 | 378,507,264 | {
"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'>"
},... | 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_feat... | [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(te... | 0.124196 | 383,619,072 | {
"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'>"
},... | 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_feat... | (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.... | 0.032139 | 385,126,400 | {
"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'>"
},... | 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_feat... | (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[trai... | 0.032322 | 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'>"
},... | 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... | 0.004823 | 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'>"
},... | 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 ... | 0.02972 | 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'>"
},... | 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()]
... | 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'>"
},... | 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 ... | 0.029281 | 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'>"
},... | 21e517922e381fbe6281f18b2797c0b3 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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... | 0.004964 | 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'>"
},... | 3af726819da8f96f8c19d3b3829f83a8 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["packages_info"] | Out[1]:
0 [**pandas** is a Python package providing fast...
1 []
2 []
3 []
4 []
... | 0.006016 | 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'>"
},... | 82b6ae955d94647a0110c001cde71492 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["packages_info"].unique() | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-8561fb9650bc>:1[0m
[0;32m----> 1[0m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mpackag... | 0.056429 | 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'>"
},... | aac3d7040c687fae59e921071c3bcf6a |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["packages_info"] != [] | [0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-3e6aa13d7bf3>:1[0m
[0;32m----> 1[0m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mpackag... | 0.063223 | 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'>"
},... | 11a44f46de86ea08736f64fabb4e6a43 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["packages_info"] != "[]"" | [0;36m File [0;32m<ipython-input-1-a8d2718a0e1b>:1[0;36m[0m
[0;31m train_features["packages_info"] != "[]""[0m
[0m ^[0m
[0;31mSyntaxError[0m[0;31m:[0m EOL while scanning string literal
Error: None
| 0.005492 | 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'>"
},... | a4235924eb30ad6c7237c76ee75bb80c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["packages_info"] != "[]" | Out[1]:
0 True
1 True
2 True
3 True
4 True
...
5828 True
5829 True
5830 True
5831 True
5832 True
Name: packages_info, Length: 5833, dtype: bool
0 True
1 True
2 True
3 True
4 True
...
5828 True
5829 True
5830 True
5831... | 0.00583 | 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'>"
},... | 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_feat... | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-61dfcdd20181>:17[0m
[1;32m 13[0m validation_features[38;5;241m.[39mindex [38;5;241m=[39m [38;5;28mrange[... | 0.303702 | 408,780,800 | {
"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'>"
},... | 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_feat... | (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[['te... | 0.031623 | 422,273,024 | {
"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'>"
},... | 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_feat... | (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',... | 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'>"
},... | 823db64130a83ce99b38ed3297f76dd6 |
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