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
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-cf90c8cf9350>: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_after', ... | 0.035854 | 423,931,904 | {
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},... | 930fcb0fa93672d2f35649c089bbdf16 |
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)
| 0.031981 | 425,000,960 | {
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"type": "type",
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},... | b3bed2526c57adb0dc00a66f89f0536e |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.006296 | 425,000,960 | {
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},... | f8409e835d8d3d83342b61d5673ca53a |
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)
| 0.125191 | 427,073,536 | {
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"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},... | 84349ba3b31f19f6c89a1d08acaff008 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.009167 | 427,073,536 | {
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},... | 03a9cdf9da01c25f2b33ae931c1b3a54 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.007346 | 427,073,536 | {
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"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},... | 41a85aac4b313eb55ebe3707e8fc9f24 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.007011 | 426,913,792 | {
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},... | 5aa9b2dd85e349029134435715cccf67 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.007081 | 426,913,792 | {
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},... | 826704869f9edda2b8dba94fcd15311b |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | 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()]
... | 0.007041 | 426,913,792 | {
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},... | cb53c5e5297fbfeae6a7860ab5228b5d |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["code_line_after"].sample(20) | Out[1]:
2204 [model = sm.tsa.statespace.SARIMAX(data2.AUDTH...
2987 [HP_embed_size = 128]
3214 [stations = four_hour.groupby(by =['station', ...
867 [wholedf.Location = wholedf.Location.str.lower()]
4382 [structure = pd.read_csv("kaggle/structure.csv")]
2133 [seventh_data... | 0.007311 | 426,913,792 | {
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},... | e0516f81958bc4ba14af4ac461e941ae |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
0
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},... | 2e18f82a3f8d840cd242f99dd0a2a777 |
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)
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},... | 79f45234000025e72ebc061b01165766 |
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-1-9de7af673fb3>:22: 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... | 0.033963 | 425,865,216 | {
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},... | be7fb8901bdb5a67114fb6c185ef7ee9 |
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)
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},... | 6f5990f8fe349e40315d78d9436bfaf0 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 0
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"] == "NONE").sum() | Out[1]: 0
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},... | ee58e15ce905be0874c9476412c8bc15 |
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)
| 0.121225 | 425,996,288 | {
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},... | bf86b936edc25768b90b9ee835c0e90c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"] == "NONE") | Out[1]:
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1 False
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5831 False
5832 False
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},... | c1f4311f89893396f4a6cfccd24f30d9 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]) | Out[1]:
0 [import pandas as pd, import numpy as np]
1 [l_cols = ['user_id','movie_id','rating'], r_c...
2 [l.head()]
3 [r.head()]
4 [movies = pd.merge(l,r)]
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},... | 8bf224f76cd3d51abae95bd1cceb3f85 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]).sample(20) | Out[1]:
1601 [color_matrix = ops.reravel(merged[['C']], img...
2845 [import os, import numpy as np, import pandas ...
3367 [lda = models.LdaModel(corpus=corpus, num_topi...
685 [diff(f, x)]
3154 [train_ids = [], val_ids = [], for dev_index, ...
4672 [train_df.ix[... | 0.007905 | 425,996,288 | {
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},... | 883c7afbb5a4a2397247fd856e50a470 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]).sample(40) | Out[1]:
1136 [sess = tf.Session()]
5813 [def load_data_frame(file_path, column_separat...
3018 [data['MSSubClass'].head()]
2922 [simple_weights = regression_gradient_descent(...
3064 [prediccions_optimized=xgb_optimized.predict(t...
5517 [import time,... | 0.009248 | 425,996,288 | {
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]).sample(100) | Out[1]:
152 [lb_ar = lbview.map_async(high_variated_work, ...
4119 [from __future__ import division, import panda...
2432 [dist_df = pd.DataFrame(dist, columns=df.Disti...
2609 [y_pred = clf.predict(X_test)]
1048 [sub.DATE = pd.to_datetime(sub.DATE)]
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]).sample(100)[0] | [0;31m---------------------------------------------------------------------------[0m
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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
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"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},... | 48701d10206412e4309e92903550408d |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["text"]).sample(100).iloc[0] | Out[1]: ['df=pd.read_csv("Data.csv")']
['df=pd.read_csv("Data.csv")']
| 0.006033 | 425,996,288 | {
"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'>"
},... | dd85ce2ec7a5d97cefcdb0f53f949851 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: list
<class 'list'>
| 0.007368 | 425,996,288 | {
"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'>"
},... | 0b7af3607358a51b919fa4bd11ba604d |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(x)) | Out[1]:
0 import pandas as pd import numpy as np
1 l_cols = ['user_id','movie_id','rating'] r_col...
2 l.head()
3 r.head()
4 movies = pd.merge(l,r)
... | 0.008744 | 423,636,992 | {
"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'>"
},... | 59216771c3032a1226d46620dcb42823 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(x), inplace=True) | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-703747872e17>:8[0m
[1;32m 4[0m text [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mtext[39m[3... | 0.040837 | 423,636,992 | {
"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'>"
},... | 265719c12c90a38777b4f664a508ff06 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
text.apply(lambda x: " ".join(x)) | Out[1]:
0 import pandas as pd import numpy as np
1 l_cols = ['user_id','movie_id','rating'] r_col...
2 l.head()
3 r.head()
4 movies = pd.merge(l,r)
... | 0.006837 | 423,636,992 | {
"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'>"
},... | 380072257da6ad96958a5e29b6a0f2b6 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = text.apply(lambda x: " ".join(x))
| 0.006622 | 423,636,992 | {
"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'>"
},... | d90ed02bd47f321765792f4f7015ee34 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
vectorizer.fit_transform(train_features["text"]) | Out[1]:
<5833x13737 sparse matrix of type '<class 'numpy.float64'>'
with 84595 stored elements in Compressed Sparse Row format>
<5833x13737 sparse matrix of type '<class 'numpy.float64'>'
with 84595 stored elements in Compressed Sparse Row format>
| 0.075474 | 423,636,992 | {
"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'>"
},... | 42ce3358d02a80e997bff4ca1e22e3e2 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
vectorizer.fit_transform(train_features["text"]) | Out[1]:
<5833x13737 sparse matrix of type '<class 'numpy.float64'>'
with 84595 stored elements in Compressed Sparse Row format>
<5833x13737 sparse matrix of type '<class 'numpy.float64'>'
with 84595 stored elements in Compressed Sparse Row format>
| 0.106758 | 423,768,064 | {
"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'>"
},... | 5fddebe62614c4293b970c7089cad591 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstack((... | [0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-deae97751859>:12[0m
[1;32m 8[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(v... | 0.140809 | 423,899,136 | {
"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'>"
},... | 6c1e3ed1bff48271190954b67b93acd0 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = ... | [0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-c08ef251fbe7>:12[0m
[1;32m 8[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(v... | 0.082458 | 425,340,928 | {
"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'>"
},... | b987b43218d485ad9d18a2f180fa1af3 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = ... | [0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-556d4000963d>:12[0m
[1;32m 8[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(v... | 0.10503 | 425,340,928 | {
"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'>"
},... | 7abd973e01c0e247a2d0712e5e810b74 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
# X3 ... | Out[1]: (1927, 9)
(1927, 9)
| 0.071874 | 425,340,928 | {
"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'>"
},... | 60a85d81e81fcd7036cb6b36728c0058 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
# X3 ... | Out[1]: ((1927, 9), (5833, 13737))
((1927, 9), (5833, 13737))
| 0.071002 | 425,340,928 | {
"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'>"
},... | 7d92fd5ecb00318f2ee4ad6695ecdbb5 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
# X3 = hst... | Out[1]: ((5833, 9), (5833, 13737))
((5833, 9), (5833, 13737))
| 0.072369 | 425,340,928 | {
"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'>"
},... | 4f7d80d4f513aadd61ce15aaa8776504 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | 0.071947 | 425,472,000 | {
"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'>"
},... | 4544603db82db926b49bfeadfc9dbf02 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | [0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-640a210a8d34>:18[0m
[1;32m 15[0m clf [38;5;241m=[39m lgb[38;5;241m.[39mLGBMClassifier()
[1;32m 16[0m... | 2.082004 | 437,719,040 | {
"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'>"
},... | af6adeb5c5df073b4e1d3af557010e74 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | [0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e17fffc58b7a>:23[0m
[1;32m 20[0m X2 [38;5;241m=[39m vectorizer[38;5;241m.[39mtransform(text_column)
[... | 2.729737 | 455,069,696 | {
"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'>"
},... | 7c0a51d41d1a4cee59dbc1f37ee6692e |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = text.apply(lambda x: " ".join(x))
validation_features["text"] = text.apply(lambda x: " ".join(x))
test_fea... | 0.010047 | 455,069,696 | {
"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'>"
},... | 22cdde42196961471a1b4d1098bb47e2 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
| 0.005326 | 455,069,696 | {
"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'>"
},... | 240a0ed65d14df06aeef85d274f6c5af |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
| 0.005958 | 455,069,696 | {
"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'>"
},... | f9911e329638130666a608e68c86fd1b |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | clf = lgb.LGBMClassifier() | 0.006336 | 455,069,696 | {
"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'>"
},... | b17540f11e34d795ed9e7e9450dce263 | |
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()
| 1.037714 | 461,889,536 | {
"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'>"
},... | 446e17f92dc0cda73e92667fb2f1f6d1 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[train_columns]), target) | Out[1]: 0.8400480027430138
0.8400480027430138
| 0.043374 | 462,020,608 | {
"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'>"
},... | b29933aef2332e0f76e08b712645bfe1 |
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
... | 0.007454 | 462,020,608 | {
"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'>"
},... | dfc48292a7667152e07c19d6669bd50c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | pred = clf.predict(validation_features[train_columns])
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.5479133178319338
0.5479133178319338
| 0.019947 | 462,020,608 | {
"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'>"
},... | 21f964a1a459822bcea89f6670e65569 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | Out[1]: 0.3478215079449254
0.3478215079449254
| 2.391107 | 462,536,704 | {
"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'>"
},... | 9ae127959638c3de5d87e9d9c9ea4d83 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | Out[1]: 0.3478215079449254
0.3478215079449254
| 2.054555 | 463,015,936 | {
"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'>"
},... | f35bfb338774921cd25b1e1a0b7cf38c |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"].apply(lambda x: " ".join(x), inplace=True)
validation_features["text"].apply(lambda x: " ".join(x), inplace=... | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-7002e816f41b>:8[0m
[1;32m 2[0m [38;5;28;01mfrom[39;00m[38;5;250m [39m[38;5;21;01msklearn[39;00m[38;5;2... | 0.033033 | 463,015,936 | {
"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'>"
},... | 65d1cde714f2db8c7b36643e6f3822dc |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(x))
validation_features["text"] = validation_features["tex... | 0.023491 | 465,375,232 | {
"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'>"
},... | 1cf14e28011cdb3b26471ec0466e411c | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
| 0.004999 | 465,375,232 | {
"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'>"
},... | 87427fdc1cb75cc79fbe23096b7b4ec0 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | clf = lgb.LGBMClassifier() | 0.007294 | 465,375,232 | {
"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'>"
},... | 603e99769d388755476d1fd908f70e7b | |
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()
| 0.875836 | 471,470,080 | {
"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'>"
},... | cb76fc8b46a9d86254b3a48cdceef2bc |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[train_columns]), target) | Out[1]: 0.8400480027430138
0.8400480027430138
| 0.041924 | 471,470,080 | {
"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'>"
},... | 0a0324462b11fcc99dcf3de85aa58a0d |
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
... | 0.006135 | 471,470,080 | {
"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'>"
},... | 6e9bca0d23b3094050e23bc1b63761ee |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | pred = clf.predict(validation_features[train_columns])
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.5479133178319338
0.5479133178319338
| 0.024369 | 471,470,080 | {
"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'>"
},... | fbdc5f3f0372a9f09b92383635700a2e |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | [0;31m---------------------------------------------------------------------------[0m
[0;31mValueError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-e17fffc58b7a>:10[0m
[1;32m 6[0m vectorizer [38;5;241m=[39m TfidfVectorizer()
[1;32m 8[0m X [38;5;24... | 0.114043 | 471,470,080 | {
"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'>"
},... | 67c99b2aa0d3b64c85ffc505001ae702 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # train_features.drop(columns=["filename"])
validation_features["text"] | Out[1]:
0 i m p o r t p a n d a s a s p d i m p ...
1 l _ c o l s = [ ' u s e r _ i d ' , ' m o ...
2 l . h e a d ( )
3 r . h e a d ( )
4 m o v i e s = p d . m e r g e ( l , r )
... | 0.004278 | 471,470,080 | {
"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'>"
},... | b278ffba445c62fbd90c43e5345c48f0 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(eval(x))
validation_features["text"] = validation_features... | [0;36m File [0;32m<ipython-input-1-93cfcee47e22>:9[0;36m[0m
[0;31m validation_features["text"] = validation_features["text"].apply(lambda x: " ".join(eval(x))[0m
[0m ^[0m
[0;31mSyntaxError[0m[0;31m:[0m invalid syntax
Error: None
| 0.004148 | 471,470,080 | {
"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'>"
},... | 580ea89e6cab7a91ab1eef4ce066f7ac |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(eval(x)))
validation_features["text"] = validation_feature... | Traceback [0;36m(most recent call last)[0m:
[0m File [1;32m/usr/local/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3550[0m in [1;35mrun_code[0m
exec(code_obj, self.user_global_ns, self.user_ns)[0m
[0m File [1;32m<ipython-input-1-d7a7b0c056ca>:8[0m
train_features["text"] = train_fe... | 0.004346 | 471,470,080 | {
"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'>"
},... | f2c6bfe09c037acab6c4c6e1820b2ebc |
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)
| 0.031584 | 488,837,120 | {
"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'>"
},... | 51891f1f805106e79e74d5bb111015dd |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(x))
validation_features["text"] = validation_features["tex... | 0.006831 | 490,934,272 | {
"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'>"
},... | 77cd1d61c881e651498a71b284661406 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
| 0.005672 | 491,196,416 | {
"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'>"
},... | a2412ff3ccdb4b64f8a9b0c206cc3384 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
| 0.005194 | 491,327,488 | {
"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'>"
},... | dbfd520b8e22e9434da8bbafdd6f55f3 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | validation_features["text"] | Out[1]:
0 import matplotlib.pyplot as plt import numpy a...
1 length = 80 # x range depth = 200 # z range
2 model = 1 + np.tri(depth, length, -depth//3) p...
3 model[:depth//3,:] = 0 plt.imshow(model) plt.c...
4 rocks = np.array([[2700, 2750], [2400, 2450], ...
... | 0.00618 | 491,458,560 | {
"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'>"
},... | f82f195e975b0d541892ff51aeb32769 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | pred = clf.predict(validation_features[train_columns])
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.5479133178319338
0.5479133178319338
| 0.022512 | 491,982,848 | {
"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'>"
},... | 4dbe3c5f65d35b4c26b4652c5a2bb72f |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # sklearn.linear_model.LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack, vstack
vectorizer = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X2 = vectorizer.fit_transform(train_features["text"])
X3 = hstac... | Out[1]: 0.7263211151032674
0.7263211151032674
| 2.414693 | 487,419,904 | {
"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'>"
},... | e46a9cc8083770fc9394dc38af352c33 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | def transform(X, text_column, vectorizer):
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
vectorizer1 = TfidfVectorizer()
vectorizer2 = TfidfVectorizer()
vectorizer3 = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X1 = vectorizer.fit_transform(train_f... | [0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-57df7f073019>:11[0m
[1;32m 9[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(t... | 0.109583 | 487,419,904 | {
"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'>"
},... | 661a0d236d33119ab3bb625175848979 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(x))
validation_features["text"] = validation_features["tex... | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-fac9f849a0a1>:12[0m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] ... | 0.05887 | 487,161,856 | {
"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'>"
},... | 7b385410b5569ba781a45bc64639d761 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-8fc4d65faab3>:21: 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.... | 0.03332 | 495,091,712 | {
"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'>"
},... | 393a7ac8390d9930bf98222da4a138fa |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(x))
validation_features["text"] = validation_features["tex... | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-fac9f849a0a1>:12[0m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] ... | 0.03941 | 496,009,216 | {
"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'>"
},... | 3446d0f06581685d65971dd885463b90 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # train_features.drop(columns=["filename"])
validation_features["code_line_before"].text | [0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
[0;32m<ipython-input-1-b4d4b25d8ce0>[0m in [0;36m?[0;34m()[0m
[0;32m----> 3[0;31m [0;31m# train_features.drop(columns=["filename"])[0m[0... | 0.009599 | 496,009,216 | {
"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'>"
},... | 014acf2c8c80d77a5c046cb15d6b67f5 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # train_features.drop(columns=["filename"])
validation_features["code_line_before"].sample(100) | Out[1]:
586 [bt_classifier_model = gl.classifier.boosted_t...
1718 [finaldf['end_month'] = pd.DatetimeIndex(final...
1057 [plt.legend()]
1187 [from sklearn.datasets import make_blobs]
1786 ['max_features':['sqrt','log2']}]
... | 0.006634 | 496,009,216 | {
"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'>"
},... | 4a7508efc043130f0f192cd7e695464d |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-75121468ccae>:21: 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.... | 0.035617 | 513,814,528 | {
"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'>"
},... | 0040aa8f8fd5330fb91a051354125151 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-fdc8a4e804f5>:21: 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.... | 0.036525 | 532,303,872 | {
"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'>"
},... | 732571d1f3bdffde84b6c981d7cd35d6 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-142e9484ccbe>:21: 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.... | 0.142749 | 526,938,112 | {
"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'>"
},... | ee7cc0ed023c02f065374fbd66f075b0 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-8fc4d65faab3>:21: 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.... | 0.035513 | 526,938,112 | {
"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'>"
},... | fb4deaaf42e7df3aaffbf7afe81309f9 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
import scipy
from scipy.sparse import hstack
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+... | (5833, 32)
(1927, 32)
(1918, 21)
| 0.03455 | 526,938,112 | {
"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'>"
},... | a6c1b2545ad530b2f147350372ff87bf |
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... | 0.004536 | 526,938,112 | {
"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'>"
},... | f4dc21fdbc7a463eb95def2f5e114210 |
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... | 0.008476 | 521,973,760 | {
"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'>"
},... | 2657f0e372cec7cd2fd742022a895e22 | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | # train_features.drop(columns=["filename"])
validation_features["code_line_before"].sample(100) | Out[1]:
414 [df.to_csv('clean_data.csv')]
382 [opt = tf.train.AdamOptimizer(learning_rate).m...
509 [print(scores4)]
1388 [cross_val_score(clf, texts, labels, cv=Strati...
359 [exec(open("mnist_cnnFORTESTING.py").read())]
... | 0.006546 | 521,973,760 | {
"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'>"
},... | 7c2b85a9e93413979b50212d31d39da4 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.text[1]
from sklearn.feature_extraction.text import TfidfVectorizer
# text = train_features["text"]
# vectorizer = TfidfVectorizer()
# X = vectorizer.fit_transform(corpus)
train_features["text"] = train_features["text"].apply(lambda x: " ".join(x))
validation_features["text"] = validation_features["tex... | [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-fac9f849a0a1>:12[0m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] ... | 0.041905 | 521,973,760 | {
"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'>"
},... | 1314fec21007cebc2d172f3cf6e085bd |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | validation_features["text"] | Out[1]:
0 import matplotlib.pyplot as plt import numpy a...
1 length = 80 # x range depth = 200 # z range
2 model = 1 + np.tri(depth, length, -depth//3) p...
3 model[:depth//3,:] = 0 plt.imshow(model) plt.c...
4 rocks = np.array([[2700, 2750], [2400, 2450], ...
... | 0.005737 | 521,973,760 | {
"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'>"
},... | bbe24fc9688fbf8397cd0a192ec13b37 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | clf = lgb.LGBMClassifier() | 0.006303 | 507,887,616 | {
"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'>"
},... | 880b46336fbe89a448470b9974cc8796 | |
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()
| 1.166255 | 513,785,856 | {
"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'>"
},... | 4b6ad67ce3789f208c621ab553a19fcd |
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
... | 0.006645 | 513,785,856 | {
"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'>"
},... | c6eec377fe5beeb708019d4ea41531a4 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | def transform(X, text_column, vectorizer):
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
vectorizer1 = TfidfVectorizer()
vectorizer2 = TfidfVectorizer()
vectorizer3 = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X1 = vectorizer.fit_transform(train_f... | [0;31m---------------------------------------------------------------------------[0m
[0;31mAttributeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-57df7f073019>:11[0m
[1;32m 9[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(t... | 0.135941 | 513,785,856 | {
"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'>"
},... | 4a38229fcf83d1a4e2df6411d45fb864 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | def transform(X, text_column, vectorizer):
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
vectorizer1 = TfidfVectorizer()
vectorizer2 = TfidfVectorizer()
vectorizer3 = TfidfVectorizer()
X = scipy.sparse.csr_matrix(train_features[train_columns].values)
X1 = vectorizer.fit_transform(train_f... | Out[1]: 0.7263211151032674
0.7263211151032674
| 2.090027 | 514,310,144 | {
"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'>"
},... | d9825efe0222bf599d2ccc12a0e78e52 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c |
X = scipy.sparse.csr_matrix(test_features[train_columns].values)
X1 = vectorizer.transform(test_features['text'])
# X2 = vectorizer1.transform(validation_features['code_line_before'])
# X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1))
pred = clf.predict(X) | 0.052523 | 514,310,144 | {
"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'>"
},... | 82b5466887819e1726d17d0ece0aa9af | |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c |
X = scipy.sparse.csr_matrix(test_features[train_columns].values)
X1 = vectorizer.transform(test_features['text'])
# X2 = vectorizer1.transform(validation_features['code_line_before'])
# X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1))
pred = clf.predict(X)
pred | Out[1]:
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
| 0.053625 | 515,620,864 | {
"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'>"
},... | 17f615500d97f6778e33ec97469e7b84 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c |
X = scipy.sparse.csr_matrix(test_features[train_columns].values)
X1 = vectorizer.transform(test_features['text'])
# X2 = vectorizer1.transform(validation_features['code_line_before'])
# X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1))
pred = clf.predict(X)
pred | Out[1]:
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
| 0.05323 | 515,620,864 | {
"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'>"
},... | be9921194652238ca89a957442060298 |
388ef554-e3e7-4410-89ac-d6ad4aeaec6c |
X = scipy.sparse.csr_matrix(test_features[train_columns].values)
X1 = vectorizer.transform(test_features['text'])
# X2 = vectorizer1.transform(validation_features['code_line_before'])
# X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1))
pred = clf.predict(X)
pred | Out[1]:
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
array(['helper_functions', 'modelling', 'data_preprocessing', ...,
'prediction', 'data_preprocessing', 'prediction'], dtype=object)
| 0.051454 | 515,620,864 | {
"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'>"
},... | ec4f5edd85f0026b8889a55c0777f5d2 |
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