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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']].fillna("", inplace=True)
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-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', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
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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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']].fillna("")
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
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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()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
0 [l_cols = ['user_id','movie_id','rating']]
1 [l.head()]
2 [r.head()]
3 [movies = pd.merge(l,r)]
4 [movies.head()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
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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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']].fillna("NONE")
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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},
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},
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} | 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()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
0 [l_cols = ['user_id','movie_id','rating']]
1 [l.head()]
2 [r.head()]
3 [movies = pd.merge(l,r)]
4 [movies.head()]
...
5828 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...
5829 [image_data_shared = srm.transform(image_data)]
5830 [accuracy = np.zeros((subjects,))]
5831 [plot_confusion_matrix(cm, title="Confusion ma...
5832 0
Name: code_line_after, Length: 5833, dtype: object
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features["code_line_after"].sample(20) | Out[1]:
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | (train_features["code_line_after"] == "NONE").sum() | Out[1]: 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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
'code_line_after', 'markdown_heading']].fillna("NONE")
train_features.fillna(0, inplace=True)
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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},
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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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
# train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']].fillna("NONE")
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0].loc["text"] = "NONE"
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-9de7af673fb3>: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[train_features['text'] == 0].loc["text"] = "NONE"
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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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
# train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']].fillna("NONE")
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]["text"] = "NONE"
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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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 | import pandas as pd
import lightgbm as lgb
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
features_path = 'data/task2/'
test_features = pd.read_pickle(features_path+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
# train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']] = train_features[['text', 'comment', 'code_line_before',
# 'code_line_after', 'markdown_heading']].fillna("NONE")
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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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)
...
5828 plot_confusion_matrix(cm, title="Confusion mat...
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5830 image_data_shared = srm.transform(image_data) ...
5831 accuracy = np.zeros((subjects,)) cm = [None] *...
5832 plot_confusion_matrix(cm, title="Confusion mat...
Name: text, Length: 5833, dtype: object
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)
...
5828 plot_confusion_matrix(cm, title="Confusion mat...
5829 srm = brainiak.funcalign.srm.DetSRM(n_iter=10,...
5830 image_data_shared = srm.transform(image_data) ...
5831 accuracy = np.zeros((subjects,)) cm = [None] *...
5832 plot_confusion_matrix(cm, title="Confusion mat...
Name: text, Length: 5833, dtype: object
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} | 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[38;5;124m"[39m]
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mtext[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m,[49m[43m [49m[43minplace[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
[1;32m 4790[0m [38;5;28mself[39m,
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[1;32m 4796[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
[1;32m 4797[0m ) [38;5;241m-[39m[38;5;241m>[39m DataFrame [38;5;241m|[39m Series:
[1;32m 4798[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4799[0m [38;5;124;03m Invoke function on values of Series.[39;00m
[1;32m 4800[0m
[0;32m (...)[0m
[1;32m 4915[0m [38;5;124;03m dtype: float64[39;00m
[1;32m 4916[0m [38;5;124;03m """[39;00m
[0;32m-> 4917[0m [38;5;28;01mreturn[39;00m [43mSeriesApply[49m[43m([49m
[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
[1;32m 4920[0m [43m [49m[43mconvert_dtype[49m[38;5;241;43m=[39;49m[43mconvert_dtype[49m[43m,[49m
[1;32m 4921[0m [43m [49m[43mby_row[49m[38;5;241;43m=[39;49m[43mby_row[49m[43m,[49m
[1;32m 4922[0m [43m [49m[43margs[49m[38;5;241;43m=[39;49m[43margs[49m[43m,[49m
[1;32m 4923[0m [43m [49m[43mkwargs[49m[38;5;241;43m=[39;49m[43mkwargs[49m[43m,[49m
[1;32m 4924[0m [43m [49m[43m)[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1507[0m, in [0;36mSeriesApply.apply_standard[0;34m(self)[0m
[1;32m 1501[0m [38;5;66;03m# row-wise access[39;00m
[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
[1;32m 1506[0m action [38;5;241m=[39m [38;5;124m"[39m[38;5;124mignore[39m[38;5;124m"[39m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(obj[38;5;241m.[39mdtype, CategoricalDtype) [38;5;28;01melse[39;00m [38;5;28;01mNone[39;00m
[0;32m-> 1507[0m mapped [38;5;241m=[39m [43mobj[49m[38;5;241;43m.[39;49m[43m_map_values[49m[43m([49m
[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
[1;32m 1509[0m [43m[49m[43m)[49m
[1;32m 1511[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(mapped) [38;5;129;01mand[39;00m [38;5;28misinstance[39m(mapped[[38;5;241m0[39m], ABCSeries):
[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
[1;32m 1514[0m [38;5;28;01mreturn[39;00m obj[38;5;241m.[39m_constructor_expanddim([38;5;28mlist[39m(mapped), index[38;5;241m=[39mobj[38;5;241m.[39mindex)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:921[0m, in [0;36mIndexOpsMixin._map_values[0;34m(self, mapper, na_action, convert)[0m
[1;32m 918[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(arr, ExtensionArray):
[1;32m 919[0m [38;5;28;01mreturn[39;00m arr[38;5;241m.[39mmap(mapper, na_action[38;5;241m=[39mna_action)
[0;32m--> 921[0m [38;5;28;01mreturn[39;00m [43malgorithms[49m[38;5;241;43m.[39;49m[43mmap_array[49m[43m([49m[43marr[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43mna_action[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/algorithms.py:1743[0m, in [0;36mmap_array[0;34m(arr, mapper, na_action, convert)[0m
[1;32m 1741[0m values [38;5;241m=[39m arr[38;5;241m.[39mastype([38;5;28mobject[39m, copy[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
[1;32m 1742[0m [38;5;28;01mif[39;00m na_action [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
[0;32m-> 1743[0m [38;5;28;01mreturn[39;00m [43mlib[49m[38;5;241;43m.[39;49m[43mmap_infer[49m[43m([49m[43mvalues[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
[1;32m 1744[0m [38;5;28;01melse[39;00m:
[1;32m 1745[0m [38;5;28;01mreturn[39;00m lib[38;5;241m.[39mmap_infer_mask(
[1;32m 1746[0m values, mapper, mask[38;5;241m=[39misna(values)[38;5;241m.[39mview(np[38;5;241m.[39muint8), convert[38;5;241m=[39mconvert
[1;32m 1747[0m )
File [0;32mlib.pyx:2972[0m, in [0;36mpandas._libs.lib.map_infer[0;34m()[0m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1496[0m, in [0;36mSeriesApply.apply_standard.<locals>.curried[0;34m(x)[0m
[1;32m 1495[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mcurried[39m(x):
[0;32m-> 1496[0m [38;5;28;01mreturn[39;00m [43mfunc[49m[43m([49m[43mx[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43margs[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mkwargs[49m[43m)[49m
[0;31mTypeError[0m: <lambda>() got an unexpected keyword argument 'inplace'
Error: <lambda>() got an unexpected keyword argument 'inplace'
| 0.040837 | 423,636,992 | {
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"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
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} | 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)
...
5828 plot_confusion_matrix(cm, title="Confusion mat...
5829 srm = brainiak.funcalign.srm.DetSRM(n_iter=10,...
5830 image_data_shared = srm.transform(image_data) ...
5831 accuracy = np.zeros((subjects,)) cm = [None] *...
5832 plot_confusion_matrix(cm, title="Confusion mat...
Name: text, Length: 5833, dtype: object
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)
...
5828 plot_confusion_matrix(cm, title="Confusion mat...
5829 srm = brainiak.funcalign.srm.DetSRM(n_iter=10,...
5830 image_data_shared = srm.transform(image_data) ...
5831 accuracy = np.zeros((subjects,)) cm = [None] *...
5832 plot_confusion_matrix(cm, title="Confusion mat...
Name: text, Length: 5833, dtype: object
| 0.006837 | 423,636,992 | {
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},
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"type": "list",
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},
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},
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},
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"name": "validation_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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} | 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 | {
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},
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},
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},
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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} | 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>
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"df": null,
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"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
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"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 0)\t1.0\n (0, 1)\t1.0\n (0, 3)\t3.0\n (1, 0)\t2.0\n (1, 1)\t2.0\n (1, 3)\t2.0\n (1, 4)\t2.0\n (2, 0)\t3.0\n (2, 1)\t3.0\n (2, 3)\t3.0\n (2, 4)\t1.0\n (2, 6)\t1.0\n (3, 0)\t5.0\n (3, 1)\t4.0\n (3, 3)\t4.0\n (3, 4)\t1.0\n (3, 6)\t1.0\n (4, 0)\t7.0\n (4, 1)\t5.0\n (4, 3)\t4.0\n (4, 4)\t2.0\n (5, 0)\t9.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (5, 4)\t1.0\n :\t:\n (1921, 3)\t3.0\n (1921, 4)\t1.0\n (1921, 7)\t3.0\n (1922, 0)\t28.0\n (1922, 1)\t12.0\n (1922, 3)\t1.0\n (1922, 7)\t1.0\n (1923, 0)\t30.0\n (1923, 1)\t16.0\n (1923, 3)\t5.0\n (1923, 7)\t1.0\n (1924, 0)\t32.0\n (1924, 1)\t8.0\n (1924, 3)\t3.0\n (1924, 7)\t1.0\n (1925, 0)\t35.0\n (1925, 1)\t19.0\n (1925, 3)\t10.0\n (1925, 4)\t4.0\n (1925, 7)\t1.0\n (1926, 0)\t38.0\n (1926, 1)\t19.0\n (1926, 3)\t13.0\n (1926, 4)\t7.0\n (1926, 7)\t1.0"
},
"X1": null,
"X2": null,
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
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"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": null,
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": null,
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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((X, X2)) | [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(validation_features[train_columns][38;5;241m.[39mvalues)
[1;32m 10[0m X2 [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(train_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m])
[0;32m---> 12[0m X3 [38;5;241m=[39m [43mhstack[49m[43m([49m[43m([49m[43mX[49m[43m,[49m[43m [49m[43mX2[49m[43m)[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:733[0m, in [0;36mhstack[0;34m(blocks, format, dtype)[0m
[1;32m 731[0m [38;5;28;01mreturn[39;00m _block([blocks], [38;5;28mformat[39m, dtype)
[1;32m 732[0m [38;5;28;01melse[39;00m:
[0;32m--> 733[0m [38;5;28;01mreturn[39;00m [43m_block[49m[43m([49m[43m[[49m[43mblocks[49m[43m][49m[43m,[49m[43m [49m[38;5;28;43mformat[39;49m[43m,[49m[43m [49m[43mdtype[49m[43m,[49m[43m [49m[43mreturn_spmatrix[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:908[0m, in [0;36m_block[0;34m(blocks, format, dtype, return_spmatrix)[0m
[1;32m 903[0m [38;5;28;01mif[39;00m ([38;5;28mformat[39m [38;5;129;01min[39;00m ([38;5;28;01mNone[39;00m, [38;5;124m'[39m[38;5;124mcsr[39m[38;5;124m'[39m) [38;5;129;01mand[39;00m
[1;32m 904[0m [38;5;28mall[39m(issparse(b) [38;5;129;01mand[39;00m b[38;5;241m.[39mformat [38;5;241m==[39m [38;5;124m'[39m[38;5;124mcsr[39m[38;5;124m'[39m [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m blocks[38;5;241m.[39mflat)
[1;32m 905[0m ):
[1;32m 906[0m [38;5;28;01mif[39;00m N [38;5;241m>[39m [38;5;241m1[39m:
[1;32m 907[0m [38;5;66;03m# stack along columns (axis 1): must have shape (M, 1)[39;00m
[0;32m--> 908[0m blocks [38;5;241m=[39m [[_stack_along_minor_axis(blocks[b, :], [38;5;241m1[39m)] [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m [38;5;28mrange[39m(M)]
[1;32m 909[0m blocks [38;5;241m=[39m np[38;5;241m.[39masarray(blocks, dtype[38;5;241m=[39m[38;5;124m'[39m[38;5;124mobject[39m[38;5;124m'[39m)
[1;32m 911[0m [38;5;66;03m# stack along rows (axis 0):[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:908[0m, in [0;36m<listcomp>[0;34m(.0)[0m
[1;32m 903[0m [38;5;28;01mif[39;00m ([38;5;28mformat[39m [38;5;129;01min[39;00m ([38;5;28;01mNone[39;00m, [38;5;124m'[39m[38;5;124mcsr[39m[38;5;124m'[39m) [38;5;129;01mand[39;00m
[1;32m 904[0m [38;5;28mall[39m(issparse(b) [38;5;129;01mand[39;00m b[38;5;241m.[39mformat [38;5;241m==[39m [38;5;124m'[39m[38;5;124mcsr[39m[38;5;124m'[39m [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m blocks[38;5;241m.[39mflat)
[1;32m 905[0m ):
[1;32m 906[0m [38;5;28;01mif[39;00m N [38;5;241m>[39m [38;5;241m1[39m:
[1;32m 907[0m [38;5;66;03m# stack along columns (axis 1): must have shape (M, 1)[39;00m
[0;32m--> 908[0m blocks [38;5;241m=[39m [[[43m_stack_along_minor_axis[49m[43m([49m[43mblocks[49m[43m[[49m[43mb[49m[43m,[49m[43m [49m[43m:[49m[43m][49m[43m,[49m[43m [49m[38;5;241;43m1[39;49m[43m)[49m] [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m [38;5;28mrange[39m(M)]
[1;32m 909[0m blocks [38;5;241m=[39m np[38;5;241m.[39masarray(blocks, dtype[38;5;241m=[39m[38;5;124m'[39m[38;5;124mobject[39m[38;5;124m'[39m)
[1;32m 911[0m [38;5;66;03m# stack along rows (axis 0):[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:648[0m, in [0;36m_stack_along_minor_axis[0;34m(blocks, axis)[0m
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[1;32m 647[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(other_axis_dims) [38;5;241m>[39m [38;5;241m1[39m:
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[1;32m 649[0m [38;5;124mf[39m[38;5;124m'[39m[38;5;132;01m{[39;00mother_axis_dims[38;5;132;01m}[39;00m[38;5;124m'[39m)
[1;32m 650[0m constant_dim, [38;5;241m=[39m other_axis_dims
[1;32m 652[0m [38;5;66;03m# Do the stacking[39;00m
[0;31mValueError[0m: Mismatching dimensions along axis 0: {5833, 1927}
Error: Mismatching dimensions along axis 0: {5833, 1927}
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} | 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 = Vstack((X, X2)) | [0;31m---------------------------------------------------------------------------[0m
[0;31mNameError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-c08ef251fbe7>:12[0m
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[1;32m 10[0m X2 [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(train_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m])
[0;32m---> 12[0m X3 [38;5;241m=[39m [43mVstack[49m((X, X2))
[0;31mNameError[0m: name 'Vstack' is not defined
Error: name 'Vstack' is not defined
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},
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} | 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 = vstack((X, X2)) | [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(validation_features[train_columns][38;5;241m.[39mvalues)
[1;32m 10[0m X2 [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(train_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m])
[0;32m---> 12[0m X3 [38;5;241m=[39m [43mvstack[49m[43m([49m[43m([49m[43mX[49m[43m,[49m[43m [49m[43mX2[49m[43m)[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:781[0m, in [0;36mvstack[0;34m(blocks, format, dtype)[0m
[1;32m 779[0m [38;5;28;01mreturn[39;00m _block([[b] [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m blocks], [38;5;28mformat[39m, dtype)
[1;32m 780[0m [38;5;28;01melse[39;00m:
[0;32m--> 781[0m [38;5;28;01mreturn[39;00m [43m_block[49m[43m([49m[43m[[49m[43m[[49m[43mb[49m[43m][49m[43m [49m[38;5;28;43;01mfor[39;49;00m[43m [49m[43mb[49m[43m [49m[38;5;129;43;01min[39;49;00m[43m [49m[43mblocks[49m[43m][49m[43m,[49m[43m [49m[38;5;28;43mformat[39;49m[43m,[49m[43m [49m[43mdtype[49m[43m,[49m[43m [49m[43mreturn_spmatrix[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:912[0m, in [0;36m_block[0;34m(blocks, format, dtype, return_spmatrix)[0m
[1;32m 909[0m blocks [38;5;241m=[39m np[38;5;241m.[39masarray(blocks, dtype[38;5;241m=[39m[38;5;124m'[39m[38;5;124mobject[39m[38;5;124m'[39m)
[1;32m 911[0m [38;5;66;03m# stack along rows (axis 0):[39;00m
[0;32m--> 912[0m A [38;5;241m=[39m [43m_compressed_sparse_stack[49m[43m([49m[43mblocks[49m[43m[[49m[43m:[49m[43m,[49m[43m [49m[38;5;241;43m0[39;49m[43m][49m[43m,[49m[43m [49m[38;5;241;43m0[39;49m[43m,[49m[43m [49m[43mreturn_spmatrix[49m[43m)[49m
[1;32m 913[0m [38;5;28;01mif[39;00m dtype [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
[1;32m 914[0m A [38;5;241m=[39m A[38;5;241m.[39mastype(dtype)
File [0;32m/usr/local/lib/python3.9/site-packages/scipy/sparse/_construct.py:606[0m, in [0;36m_compressed_sparse_stack[0;34m(blocks, axis, return_spmatrix)[0m
[1;32m 604[0m [38;5;28;01mfor[39;00m b [38;5;129;01min[39;00m blocks:
[1;32m 605[0m [38;5;28;01mif[39;00m b[38;5;241m.[39mshape[other_axis] [38;5;241m!=[39m constant_dim:
[0;32m--> 606[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m([38;5;124mf[39m[38;5;124m'[39m[38;5;124mincompatible dimensions for axis [39m[38;5;132;01m{[39;00mother_axis[38;5;132;01m}[39;00m[38;5;124m'[39m)
[1;32m 607[0m indices[sum_indices:sum_indices[38;5;241m+[39mb[38;5;241m.[39mindices[38;5;241m.[39msize] [38;5;241m=[39m b[38;5;241m.[39mindices
[1;32m 608[0m sum_indices [38;5;241m+[39m[38;5;241m=[39m b[38;5;241m.[39mindices[38;5;241m.[39msize
[0;31mValueError[0m: incompatible dimensions for axis 1
Error: incompatible dimensions for axis 1
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"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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 = hstack((X, X2))
X.shape | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 0)\t1.0\n (0, 1)\t1.0\n (0, 3)\t3.0\n (1, 0)\t2.0\n (1, 1)\t2.0\n (1, 3)\t2.0\n (1, 4)\t2.0\n (2, 0)\t3.0\n (2, 1)\t3.0\n (2, 3)\t3.0\n (2, 4)\t1.0\n (2, 6)\t1.0\n (3, 0)\t5.0\n (3, 1)\t4.0\n (3, 3)\t4.0\n (3, 4)\t1.0\n (3, 6)\t1.0\n (4, 0)\t7.0\n (4, 1)\t5.0\n (4, 3)\t4.0\n (4, 4)\t2.0\n (5, 0)\t9.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (5, 4)\t1.0\n :\t:\n (1921, 3)\t3.0\n (1921, 4)\t1.0\n (1921, 7)\t3.0\n (1922, 0)\t28.0\n (1922, 1)\t12.0\n (1922, 3)\t1.0\n (1922, 7)\t1.0\n (1923, 0)\t30.0\n (1923, 1)\t16.0\n (1923, 3)\t5.0\n (1923, 7)\t1.0\n (1924, 0)\t32.0\n (1924, 1)\t8.0\n (1924, 3)\t3.0\n (1924, 7)\t1.0\n (1925, 0)\t35.0\n (1925, 1)\t19.0\n (1925, 3)\t10.0\n (1925, 4)\t4.0\n (1925, 7)\t1.0\n (1926, 0)\t38.0\n (1926, 1)\t19.0\n (1926, 3)\t13.0\n (1926, 4)\t7.0\n (1926, 7)\t1.0"
},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
"name": "hstack",
"size": 136,
"type": "function",
"value": "<function hstack at 0xffffa48fed30>"
},
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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 = hstack((X, X2))
X.shape, X2.shape | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 0)\t1.0\n (0, 1)\t1.0\n (0, 3)\t3.0\n (1, 0)\t2.0\n (1, 1)\t2.0\n (1, 3)\t2.0\n (1, 4)\t2.0\n (2, 0)\t3.0\n (2, 1)\t3.0\n (2, 3)\t3.0\n (2, 4)\t1.0\n (2, 6)\t1.0\n (3, 0)\t5.0\n (3, 1)\t4.0\n (3, 3)\t4.0\n (3, 4)\t1.0\n (3, 6)\t1.0\n (4, 0)\t7.0\n (4, 1)\t5.0\n (4, 3)\t4.0\n (4, 4)\t2.0\n (5, 0)\t9.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (5, 4)\t1.0\n :\t:\n (1921, 3)\t3.0\n (1921, 4)\t1.0\n (1921, 7)\t3.0\n (1922, 0)\t28.0\n (1922, 1)\t12.0\n (1922, 3)\t1.0\n (1922, 7)\t1.0\n (1923, 0)\t30.0\n (1923, 1)\t16.0\n (1923, 3)\t5.0\n (1923, 7)\t1.0\n (1924, 0)\t32.0\n (1924, 1)\t8.0\n (1924, 3)\t3.0\n (1924, 7)\t1.0\n (1925, 0)\t35.0\n (1925, 1)\t19.0\n (1925, 3)\t10.0\n (1925, 4)\t4.0\n (1925, 7)\t1.0\n (1926, 0)\t38.0\n (1926, 1)\t19.0\n (1926, 3)\t13.0\n (1926, 4)\t7.0\n (1926, 7)\t1.0"
},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
"name": "hstack",
"size": 136,
"type": "function",
"value": "<function hstack at 0xffffa48fed30>"
},
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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 = hstack((X, X2))
X.shape, X2.shape | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (2, 0)\t2.0\n (2, 1)\t3.0\n (2, 3)\t1.0\n (3, 0)\t3.0\n (3, 1)\t4.0\n (3, 3)\t1.0\n (4, 0)\t4.0\n (4, 1)\t5.0\n (4, 3)\t1.0\n (4, 4)\t1.0\n (5, 0)\t5.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (6, 0)\t6.0\n (6, 1)\t7.0\n (6, 3)\t1.0\n (6, 7)\t1.0\n (7, 0)\t7.0\n :\t:\n (5827, 1)\t-1.0\n (5827, 3)\t23.0\n (5827, 4)\t8.0\n (5827, 5)\t1.0\n (5828, 0)\t27.0\n (5828, 1)\t-1.0\n (5828, 3)\t2.0\n (5828, 4)\t1.0\n (5829, 0)\t29.0\n (5829, 1)\t-1.0\n (5829, 3)\t2.0\n (5829, 4)\t1.0\n (5830, 0)\t31.0\n (5830, 1)\t-1.0\n (5830, 3)\t3.0\n (5830, 4)\t2.0\n (5831, 0)\t33.0\n (5831, 1)\t-1.0\n (5831, 2)\t5.0\n (5831, 3)\t17.0\n (5831, 4)\t14.0\n (5832, 0)\t35.0\n (5832, 1)\t-1.0\n (5832, 3)\t2.0\n (5832, 4)\t1.0"
},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": null,
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
"name": "hstack",
"size": 136,
"type": "function",
"value": "<function hstack at 0xffffa48fed30>"
},
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": null,
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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 = hstack((X, X2))
# X.shape, X2.shape | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (2, 0)\t2.0\n (2, 1)\t3.0\n (2, 3)\t1.0\n (3, 0)\t3.0\n (3, 1)\t4.0\n (3, 3)\t1.0\n (4, 0)\t4.0\n (4, 1)\t5.0\n (4, 3)\t1.0\n (4, 4)\t1.0\n (5, 0)\t5.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (6, 0)\t6.0\n (6, 1)\t7.0\n (6, 3)\t1.0\n (6, 7)\t1.0\n (7, 0)\t7.0\n :\t:\n (5827, 1)\t-1.0\n (5827, 3)\t23.0\n (5827, 4)\t8.0\n (5827, 5)\t1.0\n (5828, 0)\t27.0\n (5828, 1)\t-1.0\n (5828, 3)\t2.0\n (5828, 4)\t1.0\n (5829, 0)\t29.0\n (5829, 1)\t-1.0\n (5829, 3)\t2.0\n (5829, 4)\t1.0\n (5830, 0)\t31.0\n (5830, 1)\t-1.0\n (5830, 3)\t3.0\n (5830, 4)\t2.0\n (5831, 0)\t33.0\n (5831, 1)\t-1.0\n (5831, 2)\t5.0\n (5831, 3)\t17.0\n (5831, 4)\t14.0\n (5832, 0)\t35.0\n (5832, 1)\t-1.0\n (5832, 3)\t2.0\n (5832, 4)\t1.0"
},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
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},
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},
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"cnt": null,
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"type": "list",
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},
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"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
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},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
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"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
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"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
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"time": null,
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"top_users": null,
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"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
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"name": "train_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
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"name": "validation_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
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"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
pred = clf.predict(validation_features[train_columns])
f1_score(pred, validation_features["primary_label"], average='weighted') | [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 clf[38;5;241m.[39mfit(X3, target)
[0;32m---> 18[0m pred [38;5;241m=[39m [43mclf[49m[38;5;241;43m.[39;49m[43mpredict[49m[43m([49m[43mvalidation_features[49m[43m[[49m[43mtrain_columns[49m[43m][49m[43m)[49m
[1;32m 19[0m f1_score(pred, validation_features[[38;5;124m"[39m[38;5;124mprimary_label[39m[38;5;124m"[39m], average[38;5;241m=[39m[38;5;124m'[39m[38;5;124mweighted[39m[38;5;124m'[39m)
File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/sklearn.py:1321[0m, in [0;36mLGBMClassifier.predict[0;34m(self, X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, validate_features, **kwargs)[0m
[1;32m 1309[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mpredict[39m(
[1;32m 1310[0m [38;5;28mself[39m,
[1;32m 1311[0m X: _LGBM_ScikitMatrixLike,
[0;32m (...)[0m
[1;32m 1318[0m [38;5;241m*[39m[38;5;241m*[39mkwargs: Any,
[1;32m 1319[0m ):
[1;32m 1320[0m [38;5;250m [39m[38;5;124;03m"""Docstring is inherited from the LGBMModel."""[39;00m
[0;32m-> 1321[0m result [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mpredict_proba[49m[43m([49m
[1;32m 1322[0m [43m [49m[43mX[49m[38;5;241;43m=[39;49m[43mX[49m[43m,[49m
[1;32m 1323[0m [43m [49m[43mraw_score[49m[38;5;241;43m=[39;49m[43mraw_score[49m[43m,[49m
[1;32m 1324[0m [43m [49m[43mstart_iteration[49m[38;5;241;43m=[39;49m[43mstart_iteration[49m[43m,[49m
[1;32m 1325[0m [43m [49m[43mnum_iteration[49m[38;5;241;43m=[39;49m[43mnum_iteration[49m[43m,[49m
[1;32m 1326[0m [43m [49m[43mpred_leaf[49m[38;5;241;43m=[39;49m[43mpred_leaf[49m[43m,[49m
[1;32m 1327[0m [43m [49m[43mpred_contrib[49m[38;5;241;43m=[39;49m[43mpred_contrib[49m[43m,[49m
[1;32m 1328[0m [43m [49m[43mvalidate_features[49m[38;5;241;43m=[39;49m[43mvalidate_features[49m[43m,[49m
[1;32m 1329[0m [43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m,[49m
[1;32m 1330[0m [43m [49m[43m)[49m
[1;32m 1331[0m [38;5;28;01mif[39;00m [38;5;28mcallable[39m([38;5;28mself[39m[38;5;241m.[39m_objective) [38;5;129;01mor[39;00m raw_score [38;5;129;01mor[39;00m pred_leaf [38;5;129;01mor[39;00m pred_contrib:
[1;32m 1332[0m [38;5;28;01mreturn[39;00m result
File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/sklearn.py:1351[0m, in [0;36mLGBMClassifier.predict_proba[0;34m(self, X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, validate_features, **kwargs)[0m
[1;32m 1339[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mpredict_proba[39m(
[1;32m 1340[0m [38;5;28mself[39m,
[1;32m 1341[0m X: _LGBM_ScikitMatrixLike,
[0;32m (...)[0m
[1;32m 1348[0m [38;5;241m*[39m[38;5;241m*[39mkwargs: Any,
[1;32m 1349[0m ):
[1;32m 1350[0m [38;5;250m [39m[38;5;124;03m"""Docstring is set after definition, using a template."""[39;00m
[0;32m-> 1351[0m result [38;5;241m=[39m [38;5;28;43msuper[39;49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mpredict[49m[43m([49m
[1;32m 1352[0m [43m [49m[43mX[49m[38;5;241;43m=[39;49m[43mX[49m[43m,[49m
[1;32m 1353[0m [43m [49m[43mraw_score[49m[38;5;241;43m=[39;49m[43mraw_score[49m[43m,[49m
[1;32m 1354[0m [43m [49m[43mstart_iteration[49m[38;5;241;43m=[39;49m[43mstart_iteration[49m[43m,[49m
[1;32m 1355[0m [43m [49m[43mnum_iteration[49m[38;5;241;43m=[39;49m[43mnum_iteration[49m[43m,[49m
[1;32m 1356[0m [43m [49m[43mpred_leaf[49m[38;5;241;43m=[39;49m[43mpred_leaf[49m[43m,[49m
[1;32m 1357[0m [43m [49m[43mpred_contrib[49m[38;5;241;43m=[39;49m[43mpred_contrib[49m[43m,[49m
[1;32m 1358[0m [43m [49m[43mvalidate_features[49m[38;5;241;43m=[39;49m[43mvalidate_features[49m[43m,[49m
[1;32m 1359[0m [43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m,[49m
[1;32m 1360[0m [43m [49m[43m)[49m
[1;32m 1361[0m [38;5;28;01mif[39;00m [38;5;28mcallable[39m([38;5;28mself[39m[38;5;241m.[39m_objective) [38;5;129;01mand[39;00m [38;5;129;01mnot[39;00m (raw_score [38;5;129;01mor[39;00m pred_leaf [38;5;129;01mor[39;00m pred_contrib):
[1;32m 1362[0m _log_warning(
[1;32m 1363[0m [38;5;124m"[39m[38;5;124mCannot compute class probabilities or labels [39m[38;5;124m"[39m
[1;32m 1364[0m [38;5;124m"[39m[38;5;124mdue to the usage of customized objective function.[39m[38;5;130;01m\n[39;00m[38;5;124m"[39m
[1;32m 1365[0m [38;5;124m"[39m[38;5;124mReturning raw scores instead.[39m[38;5;124m"[39m
[1;32m 1366[0m )
File [0;32m/usr/local/lib/python3.9/site-packages/lightgbm/sklearn.py:1010[0m, in [0;36mLGBMModel.predict[0;34m(self, X, raw_score, start_iteration, num_iteration, pred_leaf, pred_contrib, validate_features, **kwargs)[0m
[1;32m 1008[0m n_features [38;5;241m=[39m X[38;5;241m.[39mshape[[38;5;241m1[39m]
[1;32m 1009[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39m_n_features [38;5;241m!=[39m n_features:
[0;32m-> 1010[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m(
[1;32m 1011[0m [38;5;124m"[39m[38;5;124mNumber of features of the model must [39m[38;5;124m"[39m
[1;32m 1012[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124mmatch the input. Model n_features_ is [39m[38;5;132;01m{[39;00m[38;5;28mself[39m[38;5;241m.[39m_n_features[38;5;132;01m}[39;00m[38;5;124m and [39m[38;5;124m"[39m
[1;32m 1013[0m [38;5;124mf[39m[38;5;124m"[39m[38;5;124minput n_features is [39m[38;5;132;01m{[39;00mn_features[38;5;132;01m}[39;00m[38;5;124m"[39m
[1;32m 1014[0m )
[1;32m 1015[0m [38;5;66;03m# retrieve original params that possibly can be used in both training and prediction[39;00m
[1;32m 1016[0m [38;5;66;03m# and then overwrite them (considering aliases) with params that were passed directly in prediction[39;00m
[1;32m 1017[0m predict_params [38;5;241m=[39m [38;5;28mself[39m[38;5;241m.[39m_process_params(stage[38;5;241m=[39m[38;5;124m"[39m[38;5;124mpredict[39m[38;5;124m"[39m)
[0;31mValueError[0m: Number of features of the model must match the input. Model n_features_ is 13746 and input n_features is 9
Error: Number of features of the model must match the input. Model n_features_ is 13746 and input n_features is 9
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
def transform(X, text_column):
X = scipy.sparse.csr_matrix(X.values)
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
pred = clf.predict(transform(validation_features[train_columns], validation_features['text']))
f1_score(pred, validation_features["primary_label"], average='weighted') | [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)
[1;32m 21[0m [38;5;28;01mreturn[39;00m hstack((X, X2))
[0;32m---> 23[0m pred [38;5;241m=[39m clf[38;5;241m.[39mpredict([43mtransform[49m[43m([49m[43mvalidation_features[49m[43m[[49m[43mtrain_columns[49m[43m][49m[43m,[49m[43m [49m[43mvalidation_features[49m[43m[[49m[38;5;124;43m'[39;49m[38;5;124;43mtext[39;49m[38;5;124;43m'[39;49m[43m][49m[43m)[49m)
[1;32m 25[0m f1_score(pred, validation_features[[38;5;124m"[39m[38;5;124mprimary_label[39m[38;5;124m"[39m], average[38;5;241m=[39m[38;5;124m'[39m[38;5;124mweighted[39m[38;5;124m'[39m)
File [0;32m<ipython-input-1-e17fffc58b7a>:20[0m, in [0;36mtransform[0;34m(X, text_column)[0m
[1;32m 18[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mtransform[39m(X, text_column):
[1;32m 19[0m X [38;5;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(X[38;5;241m.[39mvalues)
[0;32m---> 20[0m X2 [38;5;241m=[39m [43mvectorizer[49m[38;5;241;43m.[39;49m[43mtransform[49m[43m([49m[43mtext_column[49m[43m)[49m
[1;32m 21[0m [38;5;28;01mreturn[39;00m hstack((X, X2))
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:2128[0m, in [0;36mTfidfVectorizer.transform[0;34m(self, raw_documents)[0m
[1;32m 2111[0m [38;5;250m[39m[38;5;124;03m"""Transform documents to document-term matrix.[39;00m
[1;32m 2112[0m
[1;32m 2113[0m [38;5;124;03mUses the vocabulary and document frequencies (df) learned by fit (or[39;00m
[0;32m (...)[0m
[1;32m 2124[0m [38;5;124;03m Tf-idf-weighted document-term matrix.[39;00m
[1;32m 2125[0m [38;5;124;03m"""[39;00m
[1;32m 2126[0m check_is_fitted([38;5;28mself[39m, msg[38;5;241m=[39m[38;5;124m"[39m[38;5;124mThe TF-IDF vectorizer is not fitted[39m[38;5;124m"[39m)
[0;32m-> 2128[0m X [38;5;241m=[39m [38;5;28;43msuper[39;49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mtransform[49m[43m([49m[43mraw_documents[49m[43m)[49m
[1;32m 2129[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39m_tfidf[38;5;241m.[39mtransform(X, copy[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1421[0m, in [0;36mCountVectorizer.transform[0;34m(self, raw_documents)[0m
[1;32m 1418[0m [38;5;28mself[39m[38;5;241m.[39m_check_vocabulary()
[1;32m 1420[0m [38;5;66;03m# use the same matrix-building strategy as fit_transform[39;00m
[0;32m-> 1421[0m _, X [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_count_vocab[49m[43m([49m[43mraw_documents[49m[43m,[49m[43m [49m[43mfixed_vocab[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
[1;32m 1422[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39mbinary:
[1;32m 1423[0m X[38;5;241m.[39mdata[38;5;241m.[39mfill([38;5;241m1[39m)
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1263[0m, in [0;36mCountVectorizer._count_vocab[0;34m(self, raw_documents, fixed_vocab)[0m
[1;32m 1261[0m [38;5;28;01mfor[39;00m doc [38;5;129;01min[39;00m raw_documents:
[1;32m 1262[0m feature_counter [38;5;241m=[39m {}
[0;32m-> 1263[0m [38;5;28;01mfor[39;00m feature [38;5;129;01min[39;00m [43manalyze[49m[43m([49m[43mdoc[49m[43m)[49m:
[1;32m 1264[0m [38;5;28;01mtry[39;00m:
[1;32m 1265[0m feature_idx [38;5;241m=[39m vocabulary[feature]
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:104[0m, in [0;36m_analyze[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)[0m
[1;32m 102[0m [38;5;28;01melse[39;00m:
[1;32m 103[0m [38;5;28;01mif[39;00m preprocessor [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
[0;32m--> 104[0m doc [38;5;241m=[39m [43mpreprocessor[49m[43m([49m[43mdoc[49m[43m)[49m
[1;32m 105[0m [38;5;28;01mif[39;00m tokenizer [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
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} | 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_features["text"] = text.apply(lambda x: " ".join(x))
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clf.fit(train_features[train_columns], target) | Out[1]: LGBMClassifier()
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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
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"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
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},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
def transform(X, text_column):
X = scipy.sparse.csr_matrix(X.values)
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
pred = clf.predict(transform(validation_features[train_columns], validation_features['text']))
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.3478215079449254
0.3478215079449254
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"Readliner": null,
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"type": "type",
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},
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"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (0, 5937)\t0.5163862850483787\n (0, 8795)\t0.3558057453091318\n (0, 1298)\t0.600749677748211\n (0, 8945)\t0.2644837387295696\n (0, 8341)\t0.3329314167288935\n (0, 8226)\t0.25505357203318946\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (1, 8945)\t0.13504093794218394\n (1, 6501)\t0.39227128128564803\n (1, 12731)\t0.15070921240643548\n (1, 7679)\t0.37455610991232274\n (1, 9871)\t0.12763921978626358\n (1, 9755)\t0.39227128128564803\n (1, 12084)\t0.08913990946874038\n (1, 9909)\t0.19020164731249567\n (1, 3084)\t0.06905797226187309\n (1, 10713)\t0.2534189679486199\n (1, 7907)\t0.2507702182943471\n (1, 12713)\t0.3375368288426258\n :\t:\n (5832, 4)\t1.0\n (5832, 8226)\t0.16028844651757307\n (5832, 12084)\t0.10971765781838334\n (5832, 7332)\t0.1147810162510238\n (5832, 4718)\t0.07566267269096397\n (5832, 9424)\t0.08270028936005187\n (5832, 11786)\t0.11982910271737358\n (5832, 1085)\t0.15181520730198833\n (5832, 2408)\t0.16771609806839577\n (5832, 862)\t0.3850659908550527\n (5832, 4733)\t0.11653251292101924\n (5832, 2636)\t0.18550009637025716\n (5832, 13198)\t0.12148662041890751\n (5832, 3518)\t0.19813777844657318\n (5832, 11967)\t0.11063613914309335\n (5832, 1435)\t0.16019848675176263\n (5832, 6237)\t0.12660744935867593\n (5832, 11332)\t0.1473973483809412\n (5832, 1145)\t0.2009707623161172\n (5832, 9145)\t0.18909055223525678\n (5832, 7246)\t0.2041377688368914\n (5832, 3371)\t0.2167754509132074\n (5832, 11222)\t0.41545644940378196\n (5832, 11473)\t0.3864708586863744\n (5832, 3359)\t0.2414131137701598"
},
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},
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"cnt": null,
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},
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},
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},
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},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": {
"name": "transform",
"size": 136,
"type": "function",
"value": "<function transform at 0xffff7167e0d0>"
},
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 2090749,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
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"y_val_multi": null,
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
def transform(X, text_column):
X = scipy.sparse.csr_matrix(X.values)
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
pred = clf.predict(transform(validation_features[train_columns], validation_features['text']))
f1_score(pred, validation_features["primary_label"], average='weighted') | 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (2, 0)\t2.0\n (2, 1)\t3.0\n (2, 3)\t1.0\n (3, 0)\t3.0\n (3, 1)\t4.0\n (3, 3)\t1.0\n (4, 0)\t4.0\n (4, 1)\t5.0\n (4, 3)\t1.0\n (4, 4)\t1.0\n (5, 0)\t5.0\n (5, 1)\t6.0\n (5, 3)\t1.0\n (6, 0)\t6.0\n (6, 1)\t7.0\n (6, 3)\t1.0\n (6, 7)\t1.0\n (7, 0)\t7.0\n :\t:\n (5827, 1)\t-1.0\n (5827, 3)\t23.0\n (5827, 4)\t8.0\n (5827, 5)\t1.0\n (5828, 0)\t27.0\n (5828, 1)\t-1.0\n (5828, 3)\t2.0\n (5828, 4)\t1.0\n (5829, 0)\t29.0\n (5829, 1)\t-1.0\n (5829, 3)\t2.0\n (5829, 4)\t1.0\n (5830, 0)\t31.0\n (5830, 1)\t-1.0\n (5830, 3)\t3.0\n (5830, 4)\t2.0\n (5831, 0)\t33.0\n (5831, 1)\t-1.0\n (5831, 2)\t5.0\n (5831, 3)\t17.0\n (5831, 4)\t14.0\n (5832, 0)\t35.0\n (5832, 1)\t-1.0\n (5832, 3)\t2.0\n (5832, 4)\t1.0"
},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": {
"name": "X3",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (0, 5937)\t0.5163862850483787\n (0, 8795)\t0.3558057453091318\n (0, 1298)\t0.600749677748211\n (0, 8945)\t0.2644837387295696\n (0, 8341)\t0.3329314167288935\n (0, 8226)\t0.25505357203318946\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (1, 8945)\t0.13504093794218394\n (1, 6501)\t0.39227128128564803\n (1, 12731)\t0.15070921240643548\n (1, 7679)\t0.37455610991232274\n (1, 9871)\t0.12763921978626358\n (1, 9755)\t0.39227128128564803\n (1, 12084)\t0.08913990946874038\n (1, 9909)\t0.19020164731249567\n (1, 3084)\t0.06905797226187309\n (1, 10713)\t0.2534189679486199\n (1, 7907)\t0.2507702182943471\n (1, 12713)\t0.3375368288426258\n :\t:\n (5832, 4)\t1.0\n (5832, 8226)\t0.16028844651757307\n (5832, 12084)\t0.10971765781838334\n (5832, 7332)\t0.1147810162510238\n (5832, 4718)\t0.07566267269096397\n (5832, 9424)\t0.08270028936005187\n (5832, 11786)\t0.11982910271737358\n (5832, 1085)\t0.15181520730198833\n (5832, 2408)\t0.16771609806839577\n (5832, 862)\t0.3850659908550527\n (5832, 4733)\t0.11653251292101924\n (5832, 2636)\t0.18550009637025716\n (5832, 13198)\t0.12148662041890751\n (5832, 3518)\t0.19813777844657318\n (5832, 11967)\t0.11063613914309335\n (5832, 1435)\t0.16019848675176263\n (5832, 6237)\t0.12660744935867593\n (5832, 11332)\t0.1473973483809412\n (5832, 1145)\t0.2009707623161172\n (5832, 9145)\t0.18909055223525678\n (5832, 7246)\t0.2041377688368914\n (5832, 3371)\t0.2167754509132074\n (5832, 11222)\t0.41545644940378196\n (5832, 11473)\t0.3864708586863744\n (5832, 3359)\t0.2414131137701598"
},
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
"action_durations": null,
"action_name": null,
"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
"class_map": null,
"clf": {
"name": "clf",
"size": 48,
"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
"data_path": null,
"datetime": null,
"day": null,
"day_counts": null,
"deltas": null,
"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
"name": "hstack",
"size": 136,
"type": "function",
"value": "<function hstack at 0xffffa48fed30>"
},
"i": null,
"ind": null,
"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
"myzip": null,
"name": null,
"nb_prd": null,
"new_row": null,
"p_test": null,
"p_val": null,
"p_value": null,
"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
"pivot_counts": null,
"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'prediction'\n 'prediction' 'data_preprocessing']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1799635,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
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"tf_transformer": null,
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"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 6280981,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": {
"name": "transform",
"size": 136,
"type": "function",
"value": "<function transform at 0xffff719cbb80>"
},
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 2090749,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
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} | 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=True)
test_features["text"].apply(lambda x: " ".join(x), inplace=True)
| [0;31m---------------------------------------------------------------------------[0m
[0;31mTypeError[0m Traceback (most recent call last)
File [0;32m<ipython-input-1-7002e816f41b>:8[0m
[1;32m 2[0m [38;5;28;01mfrom[39;00m[38;5;250m [39m[38;5;21;01msklearn[39;00m[38;5;21;01m.[39;00m[38;5;21;01mfeature_extraction[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtext[39;00m[38;5;250m [39m[38;5;28;01mimport[39;00m TfidfVectorizer
[1;32m 4[0m [38;5;66;03m# text = train_features["text"][39;00m
[1;32m 5[0m [38;5;66;03m# vectorizer = TfidfVectorizer()[39;00m
[1;32m 6[0m [38;5;66;03m# X = vectorizer.fit_transform(corpus)[39;00m
[0;32m----> 8[0m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mtext[39;49m[38;5;124;43m"[39;49m[43m][49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m,[49m[43m [49m[43minplace[49m[38;5;241;43m=[39;49m[38;5;28;43;01mTrue[39;49;00m[43m)[49m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x), inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x), inplace[38;5;241m=[39m[38;5;28;01mTrue[39;00m)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
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[1;32m 4916[0m [38;5;124;03m """[39;00m
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[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
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File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
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[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
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[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
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[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
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},
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},
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"type": "TfidfVectorizer",
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} | 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["text"].apply(lambda x: " ".join(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
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} | 1cf14e28011cdb3b26471ec0466e411c |
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
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|
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()
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | accuracy_score(clf.predict(train_features[train_columns]), target) | Out[1]: 0.8400480027430138
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
def transform(X, text_column):
X = scipy.sparse.csr_matrix(X.values)
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
pred = clf.predict(transform(validation_features[train_columns], validation_features['text']))
f1_score(pred, validation_features["primary_label"], average='weighted') | [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;241m=[39m scipy[38;5;241m.[39msparse[38;5;241m.[39mcsr_matrix(train_features[train_columns][38;5;241m.[39mvalues)
[0;32m---> 10[0m X2 [38;5;241m=[39m [43mvectorizer[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mtext[39;49m[38;5;124;43m"[39;49m[43m][49m[43m)[49m
[1;32m 12[0m X3 [38;5;241m=[39m hstack((X, X2))
[1;32m 13[0m [38;5;66;03m# X.shape, X2.shape[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:2104[0m, in [0;36mTfidfVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 2097[0m [38;5;28mself[39m[38;5;241m.[39m_check_params()
[1;32m 2098[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf [38;5;241m=[39m TfidfTransformer(
[1;32m 2099[0m norm[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mnorm,
[1;32m 2100[0m use_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39muse_idf,
[1;32m 2101[0m smooth_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msmooth_idf,
[1;32m 2102[0m sublinear_tf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msublinear_tf,
[1;32m 2103[0m )
[0;32m-> 2104[0m X [38;5;241m=[39m [38;5;28;43msuper[39;49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mraw_documents[49m[43m)[49m
[1;32m 2105[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf[38;5;241m.[39mfit(X)
[1;32m 2106[0m [38;5;66;03m# X is already a transformed view of raw_documents so[39;00m
[1;32m 2107[0m [38;5;66;03m# we set copy to False[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/base.py:1389[0m, in [0;36m_fit_context.<locals>.decorator.<locals>.wrapper[0;34m(estimator, *args, **kwargs)[0m
[1;32m 1382[0m estimator[38;5;241m.[39m_validate_params()
[1;32m 1384[0m [38;5;28;01mwith[39;00m config_context(
[1;32m 1385[0m skip_parameter_validation[38;5;241m=[39m(
[1;32m 1386[0m prefer_skip_nested_validation [38;5;129;01mor[39;00m global_skip_validation
[1;32m 1387[0m )
[1;32m 1388[0m ):
[0;32m-> 1389[0m [38;5;28;01mreturn[39;00m [43mfit_method[49m[43m([49m[43mestimator[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[43margs[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1376[0m, in [0;36mCountVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 1368[0m warnings[38;5;241m.[39mwarn(
[1;32m 1369[0m [38;5;124m"[39m[38;5;124mUpper case characters found in[39m[38;5;124m"[39m
[1;32m 1370[0m [38;5;124m"[39m[38;5;124m vocabulary while [39m[38;5;124m'[39m[38;5;124mlowercase[39m[38;5;124m'[39m[38;5;124m"[39m
[1;32m 1371[0m [38;5;124m"[39m[38;5;124m is True. These entries will not[39m[38;5;124m"[39m
[1;32m 1372[0m [38;5;124m"[39m[38;5;124m be matched with any documents[39m[38;5;124m"[39m
[1;32m 1373[0m )
[1;32m 1374[0m [38;5;28;01mbreak[39;00m
[0;32m-> 1376[0m vocabulary, X [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_count_vocab[49m[43m([49m[43mraw_documents[49m[43m,[49m[43m [49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mfixed_vocabulary_[49m[43m)[49m
[1;32m 1378[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39mbinary:
[1;32m 1379[0m X[38;5;241m.[39mdata[38;5;241m.[39mfill([38;5;241m1[39m)
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1282[0m, in [0;36mCountVectorizer._count_vocab[0;34m(self, raw_documents, fixed_vocab)[0m
[1;32m 1280[0m vocabulary [38;5;241m=[39m [38;5;28mdict[39m(vocabulary)
[1;32m 1281[0m [38;5;28;01mif[39;00m [38;5;129;01mnot[39;00m vocabulary:
[0;32m-> 1282[0m [38;5;28;01mraise[39;00m [38;5;167;01mValueError[39;00m(
[1;32m 1283[0m [38;5;124m"[39m[38;5;124mempty vocabulary; perhaps the documents only contain stop words[39m[38;5;124m"[39m
[1;32m 1284[0m )
[1;32m 1286[0m [38;5;28;01mif[39;00m indptr[[38;5;241m-[39m[38;5;241m1[39m] [38;5;241m>[39m np[38;5;241m.[39miinfo(np[38;5;241m.[39mint32)[38;5;241m.[39mmax: [38;5;66;03m# = 2**31 - 1[39;00m
[1;32m 1287[0m [38;5;28;01mif[39;00m _IS_32BIT:
[0;31mValueError[0m: empty vocabulary; perhaps the documents only contain stop words
Error: empty vocabulary; perhaps the documents only contain stop words
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},
"text": {
"name": "text",
"size": 786640,
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},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 7552563,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": {
"name": "transform",
"size": 136,
"type": "function",
"value": "<function transform at 0xffff719cbb80>"
},
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 2519192,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
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"y_train_multi": null,
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"y_val_multi": null,
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} | 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 )
...
1922 p r i n t ( " B u i l d i n g m o d e l . . ...
1923 p _ x , p _ y = z i p ( * t e s t _ d a ...
1924 % t i m e p r e d s = m o d e l . p r e ...
1925 p r e d _ i d x = [ n p . a r g m a x ( a ...
1926 p a i r s = z i p ( p r e d _ i d x , p ...
Name: text, Length: 1927, dtype: object
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 )
...
1922 p r i n t ( " B u i l d i n g m o d e l . . ...
1923 p _ x , p _ y = z i p ( * t e s t _ d a ...
1924 % t i m e p r e d s = m o d e l . p r e ...
1925 p r e d _ i d x = [ n p . a r g m a x ( a ...
1926 p a i r s = z i p ( p r e d _ i d x , p ...
Name: text, Length: 1927, dtype: object
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"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
"TfidfVectorizer": {
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"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
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"type": "csr_matrix",
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},
"X1": null,
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": {
"name": "X3",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (0, 5937)\t0.5163862850483787\n (0, 8795)\t0.3558057453091318\n (0, 1298)\t0.600749677748211\n (0, 8945)\t0.2644837387295696\n (0, 8341)\t0.3329314167288935\n (0, 8226)\t0.25505357203318946\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (1, 8945)\t0.13504093794218394\n (1, 6501)\t0.39227128128564803\n (1, 12731)\t0.15070921240643548\n (1, 7679)\t0.37455610991232274\n (1, 9871)\t0.12763921978626358\n (1, 9755)\t0.39227128128564803\n (1, 12084)\t0.08913990946874038\n (1, 9909)\t0.19020164731249567\n (1, 3084)\t0.06905797226187309\n (1, 10713)\t0.2534189679486199\n (1, 7907)\t0.2507702182943471\n (1, 12713)\t0.3375368288426258\n :\t:\n (5832, 4)\t1.0\n (5832, 8226)\t0.16028844651757307\n (5832, 12084)\t0.10971765781838334\n (5832, 7332)\t0.1147810162510238\n (5832, 4718)\t0.07566267269096397\n (5832, 9424)\t0.08270028936005187\n (5832, 11786)\t0.11982910271737358\n (5832, 1085)\t0.15181520730198833\n (5832, 2408)\t0.16771609806839577\n (5832, 862)\t0.3850659908550527\n (5832, 4733)\t0.11653251292101924\n (5832, 2636)\t0.18550009637025716\n (5832, 13198)\t0.12148662041890751\n (5832, 3518)\t0.19813777844657318\n (5832, 11967)\t0.11063613914309335\n (5832, 1435)\t0.16019848675176263\n (5832, 6237)\t0.12660744935867593\n (5832, 11332)\t0.1473973483809412\n (5832, 1145)\t0.2009707623161172\n (5832, 9145)\t0.18909055223525678\n (5832, 7246)\t0.2041377688368914\n (5832, 3371)\t0.2167754509132074\n (5832, 11222)\t0.41545644940378196\n (5832, 11473)\t0.3864708586863744\n (5832, 3359)\t0.2414131137701598"
},
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
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"action_name_col": null,
"action_time": null,
"action_time_col": null,
"actions": null,
"actions_per_day": null,
"ax": null,
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"clf": {
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"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
"curr_session": null,
"cv": null,
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"daily_actions": null,
"daily_counts": null,
"data": null,
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"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
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"type": "function",
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},
"i": null,
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"krdl": null,
"l": null,
"line": null,
"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
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"myfile": null,
"myzip": null,
"name": null,
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"p_test": null,
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"parse": null,
"parsed": null,
"partial": null,
"parts": null,
"pattern": null,
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"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'load_data' 'data_exploration' ... 'data_exploration'\n 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
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"r": null,
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"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
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"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 2226140,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
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"tf_transformer": null,
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"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 7552563,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": {
"name": "transform",
"size": 136,
"type": "function",
"value": "<function transform at 0xffff719cbb80>"
},
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 2519192,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": null,
"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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["text"].apply(lambda x: " ".join(eval(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
| [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'>"
},
"Timestamp": null,
"X": {
"name": "X",
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} | 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_features["text"].apply(lambda x: " ".join(eval(x)))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
| 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_features["text"].apply(lambda x: " ".join(eval(x)))[0m
[0m File [1;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m in [1;35mapply[0m
return SeriesApply([0m
[0m File [1;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m in [1;35mapply[0m
return self.apply_standard()[0m
[0m File [1;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1507[0m in [1;35mapply_standard[0m
mapped = obj._map_values([0m
[0m File [1;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:921[0m in [1;35m_map_values[0m
return algorithms.map_array(arr, mapper, na_action=na_action, convert=convert)[0m
[0m File [1;32m/usr/local/lib/python3.9/site-packages/pandas/core/algorithms.py:1743[0m in [1;35mmap_array[0m
return lib.map_infer(values, mapper, convert=convert)[0m
[0m File [1;32mlib.pyx:2972[0m in [1;35mpandas._libs.lib.map_infer[0m
[0;36m File [0;32m<ipython-input-1-d7a7b0c056ca>:8[0;36m in [0;35m<lambda>[0;36m
[0;31m train_features["text"] = train_features["text"].apply(lambda x: " ".join(eval(x)))[0;36m
[0;36m File [0;32m<string>:1[0;36m[0m
[0;31m i m p o r t p a n d a s a s p d i m p o r t n u m p y a s n p[0m
[0m ^[0m
[0;31mSyntaxError[0m[0;31m:[0m invalid syntax
Error: invalid syntax (<string>, line 1)
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"Readliner": null,
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"size": 2008,
"type": "type",
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},
"vectorizer": {
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"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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} | 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_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
# train_features[train_features["code_line_after"] == 0].loc["code_line_after"] = ""
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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"type": "type",
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} | 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["text"].apply(lambda x: " ".join(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
| 0.006831 | 490,934,272 | {
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} | 77cd1d61c881e651498a71b284661406 |
|
388ef554-e3e7-4410-89ac-d6ad4aeaec6c | type((train_features["text"]).sample(100).iloc[0]) | Out[1]: str
<class 'str'>
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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], ...
...
1922 print(num_mistakes_dict['177'][1])
1923 print(num_mistakes_dict['279'][1]) print("----...
1924 print(num_mistakes_dict['2854'][1]) print("---...
1925 error_type_dict = {} for key in error_dict: fo...
1926 misspelled_words = {} for key in error_dict: f...
Name: text, Length: 1927, dtype: object
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], ...
...
1922 print(num_mistakes_dict['177'][1])
1923 print(num_mistakes_dict['279'][1]) print("----...
1924 print(num_mistakes_dict['2854'][1]) print("---...
1925 error_type_dict = {} for key in error_dict: fo...
1926 misspelled_words = {} for key in error_dict: f...
Name: text, Length: 1927, dtype: object
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} | 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 | {
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},
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"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
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"value": " filename cell_type ... save_results comment_only\n0 nb_54880.ipynb code ... 0 0\n1 nb_54880.ipynb code ... 0 0\n2 nb_54880.ipynb code ... 0 0\n3 nb_54880.ipynb code ... 0 0\n4 nb_54880.ipynb code ... 0 0\n... ... ... ... ... ...\n5828 nb_95821.ipynb code ... 0 0\n5829 nb_95821.ipynb code ... 0 0\n5830 nb_95821.ipynb code ... 0 0\n5831 nb_95821.ipynb code ... 0 0\n5832 nb_95821.ipynb code ... 0 0\n\n[5833 rows x 32 columns]"
},
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"vectorizer2": null,
"vectorizer3": null,
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
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} | 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 = hstack((X, X2))
# X.shape, X2.shape
clf = lgb.LGBMClassifier()
clf.fit(X3, target)
def transform(X, text_column):
X = scipy.sparse.csr_matrix(X.values)
X2 = vectorizer.transform(text_column)
return hstack((X, X2))
pred = clf.predict(transform(validation_features[train_columns], validation_features['text']))
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.7263211151032674
0.7263211151032674
| 2.414693 | 487,419,904 | {
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},
"X3": {
"name": "X3",
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} | 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_features['text'])
X2 = vectorizer1.fit_transform(train_features['code_line_before'])
X3 = vectorizer2.fit_transform(train_features['code_line_after'])
X = hstack((X, X1, X2, X3))
clf = lgb.LGBMClassifier()
clf.fit(X, target)
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X1 = vectorizer.transform(validation_features['text'])
X2 = vectorizer1.transform(validation_features['code_line_before'])
X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1, X2, X3))
pred = clf.predict(X)
f1_score(pred, validation_features["primary_label"], average='weighted') | [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(train_features[train_columns][38;5;241m.[39mvalues)
[1;32m 10[0m X1 [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(train_features[[38;5;124m'[39m[38;5;124mtext[39m[38;5;124m'[39m])
[0;32m---> 11[0m X2 [38;5;241m=[39m [43mvectorizer1[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mtrain_features[49m[43m[[49m[38;5;124;43m'[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m'[39;49m[43m][49m[43m)[49m
[1;32m 12[0m X3 [38;5;241m=[39m vectorizer2[38;5;241m.[39mfit_transform(train_features[[38;5;124m'[39m[38;5;124mcode_line_after[39m[38;5;124m'[39m])
[1;32m 14[0m X [38;5;241m=[39m hstack((X, X1, X2, X3))
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:2104[0m, in [0;36mTfidfVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 2097[0m [38;5;28mself[39m[38;5;241m.[39m_check_params()
[1;32m 2098[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf [38;5;241m=[39m TfidfTransformer(
[1;32m 2099[0m norm[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mnorm,
[1;32m 2100[0m use_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39muse_idf,
[1;32m 2101[0m smooth_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msmooth_idf,
[1;32m 2102[0m sublinear_tf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msublinear_tf,
[1;32m 2103[0m )
[0;32m-> 2104[0m X [38;5;241m=[39m [38;5;28;43msuper[39;49m[43m([49m[43m)[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mraw_documents[49m[43m)[49m
[1;32m 2105[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf[38;5;241m.[39mfit(X)
[1;32m 2106[0m [38;5;66;03m# X is already a transformed view of raw_documents so[39;00m
[1;32m 2107[0m [38;5;66;03m# we set copy to False[39;00m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/base.py:1389[0m, in [0;36m_fit_context.<locals>.decorator.<locals>.wrapper[0;34m(estimator, *args, **kwargs)[0m
[1;32m 1382[0m estimator[38;5;241m.[39m_validate_params()
[1;32m 1384[0m [38;5;28;01mwith[39;00m config_context(
[1;32m 1385[0m skip_parameter_validation[38;5;241m=[39m(
[1;32m 1386[0m prefer_skip_nested_validation [38;5;129;01mor[39;00m global_skip_validation
[1;32m 1387[0m )
[1;32m 1388[0m ):
[0;32m-> 1389[0m [38;5;28;01mreturn[39;00m [43mfit_method[49m[43m([49m[43mestimator[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[43margs[49m[43m,[49m[43m [49m[38;5;241;43m*[39;49m[38;5;241;43m*[39;49m[43mkwargs[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1376[0m, in [0;36mCountVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 1368[0m warnings[38;5;241m.[39mwarn(
[1;32m 1369[0m [38;5;124m"[39m[38;5;124mUpper case characters found in[39m[38;5;124m"[39m
[1;32m 1370[0m [38;5;124m"[39m[38;5;124m vocabulary while [39m[38;5;124m'[39m[38;5;124mlowercase[39m[38;5;124m'[39m[38;5;124m"[39m
[1;32m 1371[0m [38;5;124m"[39m[38;5;124m is True. These entries will not[39m[38;5;124m"[39m
[1;32m 1372[0m [38;5;124m"[39m[38;5;124m be matched with any documents[39m[38;5;124m"[39m
[1;32m 1373[0m )
[1;32m 1374[0m [38;5;28;01mbreak[39;00m
[0;32m-> 1376[0m vocabulary, X [38;5;241m=[39m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43m_count_vocab[49m[43m([49m[43mraw_documents[49m[43m,[49m[43m [49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mfixed_vocabulary_[49m[43m)[49m
[1;32m 1378[0m [38;5;28;01mif[39;00m [38;5;28mself[39m[38;5;241m.[39mbinary:
[1;32m 1379[0m X[38;5;241m.[39mdata[38;5;241m.[39mfill([38;5;241m1[39m)
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:1263[0m, in [0;36mCountVectorizer._count_vocab[0;34m(self, raw_documents, fixed_vocab)[0m
[1;32m 1261[0m [38;5;28;01mfor[39;00m doc [38;5;129;01min[39;00m raw_documents:
[1;32m 1262[0m feature_counter [38;5;241m=[39m {}
[0;32m-> 1263[0m [38;5;28;01mfor[39;00m feature [38;5;129;01min[39;00m [43manalyze[49m[43m([49m[43mdoc[49m[43m)[49m:
[1;32m 1264[0m [38;5;28;01mtry[39;00m:
[1;32m 1265[0m feature_idx [38;5;241m=[39m vocabulary[feature]
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:104[0m, in [0;36m_analyze[0;34m(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)[0m
[1;32m 102[0m [38;5;28;01melse[39;00m:
[1;32m 103[0m [38;5;28;01mif[39;00m preprocessor [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
[0;32m--> 104[0m doc [38;5;241m=[39m [43mpreprocessor[49m[43m([49m[43mdoc[49m[43m)[49m
[1;32m 105[0m [38;5;28;01mif[39;00m tokenizer [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
[1;32m 106[0m doc [38;5;241m=[39m tokenizer(doc)
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:62[0m, in [0;36m_preprocess[0;34m(doc, accent_function, lower)[0m
[1;32m 43[0m [38;5;250m[39m[38;5;124;03m"""Chain together an optional series of text preprocessing steps to[39;00m
[1;32m 44[0m [38;5;124;03mapply to a document.[39;00m
[1;32m 45[0m
[0;32m (...)[0m
[1;32m 59[0m [38;5;124;03m preprocessed string[39;00m
[1;32m 60[0m [38;5;124;03m"""[39;00m
[1;32m 61[0m [38;5;28;01mif[39;00m lower:
[0;32m---> 62[0m doc [38;5;241m=[39m [43mdoc[49m[38;5;241;43m.[39;49m[43mlower[49m()
[1;32m 63[0m [38;5;28;01mif[39;00m accent_function [38;5;129;01mis[39;00m [38;5;129;01mnot[39;00m [38;5;28;01mNone[39;00m:
[1;32m 64[0m doc [38;5;241m=[39m accent_function(doc)
[0;31mAttributeError[0m: 'int' object has no attribute 'lower'
Error: 'int' object has no attribute 'lower'
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} | 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["text"].apply(lambda x: " ".join(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
train_features["code_line_before"] = train_features["code_line_before"].apply(lambda x: " ".join(x))
validation_features["code_line_before"] = validation_features["code_line_before"].apply(lambda x: " ".join(x))
test_features["code_line_before"] = test_features["code_line_before"].apply(lambda x: " ".join(x))
train_features["code_line_after"] = train_features["code_line_after"].apply(lambda x: " ".join(x))
validation_features["code_line_after"] = validation_features["code_line_after"].apply(lambda x: " ".join(x))
test_features["code_line_after"] = test_features["code_line_after"].apply(lambda x: " ".join(x)) | [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] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;32m---> 12[0m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m"[39;49m[43m][49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m)[49m
[1;32m 13[0m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
[1;32m 4790[0m [38;5;28mself[39m,
[1;32m 4791[0m func: AggFuncType,
[0;32m (...)[0m
[1;32m 4796[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
[1;32m 4797[0m ) [38;5;241m-[39m[38;5;241m>[39m DataFrame [38;5;241m|[39m Series:
[1;32m 4798[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4799[0m [38;5;124;03m Invoke function on values of Series.[39;00m
[1;32m 4800[0m
[0;32m (...)[0m
[1;32m 4915[0m [38;5;124;03m dtype: float64[39;00m
[1;32m 4916[0m [38;5;124;03m """[39;00m
[0;32m-> 4917[0m [38;5;28;01mreturn[39;00m [43mSeriesApply[49m[43m([49m
[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
[1;32m 4920[0m [43m [49m[43mconvert_dtype[49m[38;5;241;43m=[39;49m[43mconvert_dtype[49m[43m,[49m
[1;32m 4921[0m [43m [49m[43mby_row[49m[38;5;241;43m=[39;49m[43mby_row[49m[43m,[49m
[1;32m 4922[0m [43m [49m[43margs[49m[38;5;241;43m=[39;49m[43margs[49m[43m,[49m
[1;32m 4923[0m [43m [49m[43mkwargs[49m[38;5;241;43m=[39;49m[43mkwargs[49m[43m,[49m
[1;32m 4924[0m [43m [49m[43m)[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
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[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
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[0;32m-> 1507[0m mapped [38;5;241m=[39m [43mobj[49m[38;5;241;43m.[39;49m[43m_map_values[49m[43m([49m
[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
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[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
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[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
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},
"vectorizer": {
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"value": "TfidfVectorizer()"
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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},
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-8fc4d65faab3>: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.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-8fc4d65faab3>:22: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
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"name": "validation_features",
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"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"value": "TfidfVectorizer()"
},
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"value": "TfidfVectorizer()"
},
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"name": "vectorizer3",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vstack": {
"name": "vstack",
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"type": "function",
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} | 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["text"].apply(lambda x: " ".join(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
train_features["code_line_before"] = train_features["code_line_before"].apply(lambda x: " ".join(x))
validation_features["code_line_before"] = validation_features["code_line_before"].apply(lambda x: " ".join(x))
test_features["code_line_before"] = test_features["code_line_before"].apply(lambda x: " ".join(x))
train_features["code_line_after"] = train_features["code_line_after"].apply(lambda x: " ".join(x))
validation_features["code_line_after"] = validation_features["code_line_after"].apply(lambda x: " ".join(x))
test_features["code_line_after"] = test_features["code_line_after"].apply(lambda x: " ".join(x)) | [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] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;32m---> 12[0m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m"[39;49m[43m][49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m)[49m
[1;32m 13[0m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
[1;32m 4790[0m [38;5;28mself[39m,
[1;32m 4791[0m func: AggFuncType,
[0;32m (...)[0m
[1;32m 4796[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
[1;32m 4797[0m ) [38;5;241m-[39m[38;5;241m>[39m DataFrame [38;5;241m|[39m Series:
[1;32m 4798[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4799[0m [38;5;124;03m Invoke function on values of Series.[39;00m
[1;32m 4800[0m
[0;32m (...)[0m
[1;32m 4915[0m [38;5;124;03m dtype: float64[39;00m
[1;32m 4916[0m [38;5;124;03m """[39;00m
[0;32m-> 4917[0m [38;5;28;01mreturn[39;00m [43mSeriesApply[49m[43m([49m
[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
[1;32m 4920[0m [43m [49m[43mconvert_dtype[49m[38;5;241;43m=[39;49m[43mconvert_dtype[49m[43m,[49m
[1;32m 4921[0m [43m [49m[43mby_row[49m[38;5;241;43m=[39;49m[43mby_row[49m[43m,[49m
[1;32m 4922[0m [43m [49m[43margs[49m[38;5;241;43m=[39;49m[43margs[49m[43m,[49m
[1;32m 4923[0m [43m [49m[43mkwargs[49m[38;5;241;43m=[39;49m[43mkwargs[49m[43m,[49m
[1;32m 4924[0m [43m [49m[43m)[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1507[0m, in [0;36mSeriesApply.apply_standard[0;34m(self)[0m
[1;32m 1501[0m [38;5;66;03m# row-wise access[39;00m
[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
[1;32m 1506[0m action [38;5;241m=[39m [38;5;124m"[39m[38;5;124mignore[39m[38;5;124m"[39m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(obj[38;5;241m.[39mdtype, CategoricalDtype) [38;5;28;01melse[39;00m [38;5;28;01mNone[39;00m
[0;32m-> 1507[0m mapped [38;5;241m=[39m [43mobj[49m[38;5;241;43m.[39;49m[43m_map_values[49m[43m([49m
[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
[1;32m 1509[0m [43m[49m[43m)[49m
[1;32m 1511[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(mapped) [38;5;129;01mand[39;00m [38;5;28misinstance[39m(mapped[[38;5;241m0[39m], ABCSeries):
[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
[1;32m 1514[0m [38;5;28;01mreturn[39;00m obj[38;5;241m.[39m_constructor_expanddim([38;5;28mlist[39m(mapped), index[38;5;241m=[39mobj[38;5;241m.[39mindex)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:921[0m, in [0;36mIndexOpsMixin._map_values[0;34m(self, mapper, na_action, convert)[0m
[1;32m 918[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(arr, ExtensionArray):
[1;32m 919[0m [38;5;28;01mreturn[39;00m arr[38;5;241m.[39mmap(mapper, na_action[38;5;241m=[39mna_action)
[0;32m--> 921[0m [38;5;28;01mreturn[39;00m [43malgorithms[49m[38;5;241;43m.[39;49m[43mmap_array[49m[43m([49m[43marr[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43mna_action[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/algorithms.py:1743[0m, in [0;36mmap_array[0;34m(arr, mapper, na_action, convert)[0m
[1;32m 1741[0m values [38;5;241m=[39m arr[38;5;241m.[39mastype([38;5;28mobject[39m, copy[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
[1;32m 1742[0m [38;5;28;01mif[39;00m na_action [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
[0;32m-> 1743[0m [38;5;28;01mreturn[39;00m [43mlib[49m[38;5;241;43m.[39;49m[43mmap_infer[49m[43m([49m[43mvalues[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
[1;32m 1744[0m [38;5;28;01melse[39;00m:
[1;32m 1745[0m [38;5;28;01mreturn[39;00m lib[38;5;241m.[39mmap_infer_mask(
[1;32m 1746[0m values, mapper, mask[38;5;241m=[39misna(values)[38;5;241m.[39mview(np[38;5;241m.[39muint8), convert[38;5;241m=[39mconvert
[1;32m 1747[0m )
File [0;32mlib.pyx:2972[0m, in [0;36mpandas._libs.lib.map_infer[0;34m()[0m
File [0;32m<ipython-input-1-fac9f849a0a1>:12[0m, in [0;36m<lambda>[0;34m(x)[0m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;32m---> 12[0m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m)
[1;32m 13[0m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;31mTypeError[0m: can only join an iterable
Error: can only join an iterable
| 0.03941 | 496,009,216 | {
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"DF": null,
"MultinomialNB": null,
"RandomForestClassifier": null,
"Readliner": null,
"TfidfTransformer": null,
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"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
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} | 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;34m[0m[0;34m[0m[0m
[0m[1;32m 4[0m [0;34m[0m[0m
[1;32m 5[0m [0mvalidation_features[0m[0;34m[[0m[0;34m"code_line_before"[0m[0;34m][0m[0;34m.[0m[0mtext[0m[0;34m[0m[0;34m[0m[0m
[0;32m/usr/local/lib/python3.9/site-packages/pandas/core/generic.py[0m in [0;36m?[0;34m(self, name)[0m
[1;32m 6295[0m [0;32mand[0m [0mname[0m [0;32mnot[0m [0;32min[0m [0mself[0m[0;34m.[0m[0m_accessors[0m[0;34m[0m[0;34m[0m[0m
[1;32m 6296[0m [0;32mand[0m [0mself[0m[0;34m.[0m[0m_info_axis[0m[0;34m.[0m[0m_can_hold_identifiers_and_holds_name[0m[0;34m([0m[0mname[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
[1;32m 6297[0m ):
[1;32m 6298[0m [0;32mreturn[0m [0mself[0m[0;34m[[0m[0mname[0m[0;34m][0m[0;34m[0m[0;34m[0m[0m
[0;32m-> 6299[0;31m [0;32mreturn[0m [0mobject[0m[0;34m.[0m[0m__getattribute__[0m[0;34m([0m[0mself[0m[0;34m,[0m [0mname[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
[0m
[0;31mAttributeError[0m: 'Series' object has no attribute 'text'
Error: 'Series' object has no attribute 'text'
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} | 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']}]
...
3 [plt.show()]
372 [show(p1)]
690 [return act]
1625 [content['TrimContent'][:count] = trimtext]
1051 [index=True,index_label='Row')]
Name: code_line_before, Length: 100, dtype: object
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']}]
...
3 [plt.show()]
372 [show(p1)]
690 [return act]
1625 [content['TrimContent'][:count] = trimtext]
1051 [index=True,index_label='Row')]
Name: code_line_before, Length: 100, dtype: object
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"value": "TfidfVectorizer()"
},
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"value": "TfidfVectorizer()"
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"value": "TfidfVectorizer()"
},
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
validation_features[validation_features['text'] == 0]['text'] = "NONE"
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-75121468ccae>: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.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-75121468ccae>:22: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
<ipython-input-1-75121468ccae>:25: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-75121468ccae>:26: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
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"type": "type",
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},
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},
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
validation_features[validation_features['text'] == 0]['text'] = "NONE"
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features[test_features['text'] == 0]['text'] = "NONE"
test_features[test_features['code_line_after'] == 0]['code_line_after'] = "NONE"
test_features[test_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-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.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-fdc8a4e804f5>:22: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
<ipython-input-1-fdc8a4e804f5>:25: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-fdc8a4e804f5>:26: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
<ipython-input-1-fdc8a4e804f5>:29: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
test_features[test_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-fdc8a4e804f5>:30: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
test_features[test_features['code_line_before'] == 0]['code_line_before'] = "NONE"
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"type": "type",
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},
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"value": "TfidfVectorizer()"
},
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},
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},
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
validation_features[validation_features['text'] == 0]['text'] = "NONE"
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features[test_features['text'] == 0]['text'] = "NONE"
test_features[test_features['code_line_after'] == 0]['code_line_after'] = "NONE"
test_features[test_features['code_line_before'] == 0]['code_line_before'] = "NONE"
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (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.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-142e9484ccbe>:22: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
<ipython-input-1-142e9484ccbe>:28: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-142e9484ccbe>:29: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
validation_features[validation_features['code_line_before'] == 0]['code_line_before'] = "NONE"
<ipython-input-1-142e9484ccbe>:32: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
test_features[test_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-142e9484ccbe>:33: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
test_features[test_features['code_line_before'] == 0]['code_line_before'] = "NONE"
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
train_features[train_features['text'] == 0]['text'] = "NONE"
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
<ipython-input-1-8fc4d65faab3>: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.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
<ipython-input-1-8fc4d65faab3>:22: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
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},
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},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": {
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"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer2": {
"name": "vectorizer2",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"name": "vectorizer3",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vstack": {
"name": "vstack",
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"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
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} | 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+'test_features.pkl')
train_features = pd.read_pickle(features_path+'train_features.pkl')
validation_features = pd.read_pickle(features_path+'validation_features.pkl')
train_features.index = range(train_features.shape[0])
validation_features.index = range(validation_features.shape[0])
test_features.index = range(test_features.shape[0])
train_features.fillna(0, inplace=True)
# train_features[train_features['text'] == 0]['text'] = "NONE"
# train_features[train_features['code_line_after'] == 0]['code_line_after'] = "NONE"
# train_features[train_features['code_line_before'] == 0]['code_line_before'] = "NONE"
test_features.fillna(0, inplace=True)
validation_features.fillna(0, inplace=True)
print(train_features.shape)
print(validation_features.shape)
print(test_features.shape)
| (5833, 32)
(1927, 32)
(1918, 21)
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"DF": null,
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"Readliner": null,
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"type": "type",
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},
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388ef554-e3e7-4410-89ac-d6ad4aeaec6c | train_features.columns
validation_features.columns | Out[1]:
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer2": {
"name": "vectorizer2",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer3": {
"name": "vectorizer3",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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())]
...
408 [df.loc[df.exits > 8000,'exits'] = np.nan]
1431 [sn.distplot(year_price['SalePrice'])]
564 [sc.stop()]
117 [plt.title('Training: %i' % label)]
626 [vectorizer]
Name: code_line_before, Length: 100, dtype: object
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())]
...
408 [df.loc[df.exits > 8000,'exits'] = np.nan]
1431 [sn.distplot(year_price['SalePrice'])]
564 [sc.stop()]
117 [plt.title('Training: %i' % label)]
626 [vectorizer]
Name: code_line_before, Length: 100, dtype: object
| 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'>"
},
"Timestamp": null,
"X": {
"name": "X",
"size": 48,
"type": "csr_matrix",
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},
"X1": {
"name": "X1",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X2": {
"name": "X2",
"size": 48,
"type": "csr_matrix",
"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": {
"name": "X3",
"size": 48,
"type": "csr_matrix",
"value": " (0, 1)\t1.0\n (0, 3)\t2.0\n (0, 5937)\t0.5163862850483787\n (0, 8795)\t0.3558057453091318\n (0, 1298)\t0.600749677748211\n (0, 8945)\t0.2644837387295696\n (0, 8341)\t0.3329314167288935\n (0, 8226)\t0.25505357203318946\n (1, 0)\t1.0\n (1, 1)\t2.0\n (1, 2)\t1.0\n (1, 3)\t4.0\n (1, 4)\t4.0\n (1, 8945)\t0.13504093794218394\n (1, 6501)\t0.39227128128564803\n (1, 12731)\t0.15070921240643548\n (1, 7679)\t0.37455610991232274\n (1, 9871)\t0.12763921978626358\n (1, 9755)\t0.39227128128564803\n (1, 12084)\t0.08913990946874038\n (1, 9909)\t0.19020164731249567\n (1, 3084)\t0.06905797226187309\n (1, 10713)\t0.2534189679486199\n (1, 7907)\t0.2507702182943471\n (1, 12713)\t0.3375368288426258\n :\t:\n (5832, 4)\t1.0\n (5832, 8226)\t0.16028844651757307\n (5832, 12084)\t0.10971765781838334\n (5832, 7332)\t0.1147810162510238\n (5832, 4718)\t0.07566267269096397\n (5832, 9424)\t0.08270028936005187\n (5832, 11786)\t0.11982910271737358\n (5832, 1085)\t0.15181520730198833\n (5832, 2408)\t0.16771609806839577\n (5832, 862)\t0.3850659908550527\n (5832, 4733)\t0.11653251292101924\n (5832, 2636)\t0.18550009637025716\n (5832, 13198)\t0.12148662041890751\n (5832, 3518)\t0.19813777844657318\n (5832, 11967)\t0.11063613914309335\n (5832, 1435)\t0.16019848675176263\n (5832, 6237)\t0.12660744935867593\n (5832, 11332)\t0.1473973483809412\n (5832, 1145)\t0.2009707623161172\n (5832, 9145)\t0.18909055223525678\n (5832, 7246)\t0.2041377688368914\n (5832, 3371)\t0.2167754509132074\n (5832, 11222)\t0.41545644940378196\n (5832, 11473)\t0.3864708586863744\n (5832, 3359)\t0.2414131137701598"
},
"X_columns": null,
"X_columns_text": null,
"X_test": null,
"X_train": null,
"X_val": null,
"accuracy_score": {
"name": "accuracy_score",
"size": 136,
"type": "function",
"value": "<function accuracy_score at 0xffff7244be50>"
},
"act": null,
"action": null,
"action_counts": null,
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"action_name": null,
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"action_time": null,
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"actions": null,
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"ax": null,
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"clf": {
"name": "clf",
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"type": "LGBMClassifier",
"value": "LGBMClassifier()"
},
"clf_c": null,
"clf_ft": null,
"cnt": null,
"contents": null,
"corpus": {
"name": "corpus",
"size": 64,
"type": "list",
"value": "['I want some pitsa']"
},
"corrupt": null,
"count": null,
"count_all": null,
"counts": null,
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"cv": null,
"cv_c": null,
"cv_ft": null,
"daily_actions": null,
"daily_counts": null,
"data": null,
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"datetime": null,
"day": null,
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"df": null,
"df2": null,
"df_temp": null,
"df_timed": null,
"df_top_users": null,
"durations": null,
"end_mask": null,
"f": null,
"f1_score": {
"name": "f1_score",
"size": 136,
"type": "function",
"value": "<function f1_score at 0xffff72456670>"
},
"f_statistic": null,
"features_path": {
"name": "features_path",
"size": 60,
"type": "str",
"value": "data/task2/"
},
"fig": null,
"generate_tokens": null,
"group": null,
"grouped": null,
"hstack": {
"name": "hstack",
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"type": "function",
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},
"i": null,
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"l": null,
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"lines": null,
"lines2": null,
"mask": null,
"mean_duration": null,
"median_duration": null,
"mode_duration": null,
"myfile": null,
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"name": null,
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"parse": null,
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"parts": null,
"pattern": null,
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"pred": {
"name": "pred",
"size": 15528,
"type": "ndarray",
"value": "['helper_functions' 'data_preprocessing' 'result_visualization' ...\n 'data_exploration' 'data_exploration' 'data_exploration']"
},
"prepare_X": null,
"prepare_dataset_RF": null,
"prepare_y": null,
"r": null,
"repeats": null,
"rfclf": null,
"row": null,
"session_df": null,
"session_durations": null,
"session_num": null,
"session_num_col": null,
"split_lines": null,
"stats": null,
"t_stat": null,
"target": {
"name": "target",
"size": 416963,
"type": "Series",
"value": "0 helper_functions\n1 load_data\n2 data_exploration\n3 data_exploration\n4 data_preprocessing\n ... \n5828 evaluation\n5829 modelling\n5830 data_preprocessing\n5831 modelling\n5832 evaluation\nName: primary_label, Length: 5833, dtype: object"
},
"target_drop": {
"name": "target_drop",
"size": 152,
"type": "list",
"value": "['primary_label', 'load_data', 'helper_functions', 'data_preprocessing', 'data_exploration', 'modelling', 'prediction', 'evaluation', 'result_visualization', 'save_results', 'comment_only']"
},
"test_features": {
"name": "test_features",
"size": 1546797,
"type": "DataFrame",
"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
"text_counts": null,
"tf_transformer": null,
"tf_transformer_c": null,
"tf_transformer_ft": null,
"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
"top_users": null,
"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 4990589,
"type": "DataFrame",
"value": " filename ... primary_label\n0 nb_54880.ipynb ... helper_functions\n1 nb_54880.ipynb ... load_data\n2 nb_54880.ipynb ... data_exploration\n3 nb_54880.ipynb ... data_exploration\n4 nb_54880.ipynb ... data_preprocessing\n... ... ... ...\n5828 nb_95821.ipynb ... evaluation\n5829 nb_95821.ipynb ... modelling\n5830 nb_95821.ipynb ... data_preprocessing\n5831 nb_95821.ipynb ... modelling\n5832 nb_95821.ipynb ... evaluation\n\n[5833 rows x 22 columns]"
},
"train_test_split": {
"name": "train_test_split",
"size": 136,
"type": "function",
"value": "<function train_test_split at 0xffff71a97e50>"
},
"transform": {
"name": "transform",
"size": 136,
"type": "function",
"value": "<function transform at 0xffffa5836f70>"
},
"txt": null,
"user_daily_actions": null,
"user_id": null,
"user_id_col": null,
"user_total_actions": null,
"validation_features": {
"name": "validation_features",
"size": 1826459,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": {
"name": "vectorizer1",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer2": {
"name": "vectorizer2",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer3": {
"name": "vectorizer3",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vstack": {
"name": "vstack",
"size": 136,
"type": "function",
"value": "<function vstack at 0xffffa48fedc0>"
},
"weekday_counts": null,
"weekday_df": null,
"y_column": null,
"y_columns": null,
"y_train_multi": null,
"y_train_primary": null,
"y_val_multi": null,
"y_val_primary": null
} | 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["text"].apply(lambda x: " ".join(x))
test_features["text"] = test_features["text"].apply(lambda x: " ".join(x))
train_features["code_line_before"] = train_features["code_line_before"].apply(lambda x: " ".join(x))
validation_features["code_line_before"] = validation_features["code_line_before"].apply(lambda x: " ".join(x))
test_features["code_line_before"] = test_features["code_line_before"].apply(lambda x: " ".join(x))
train_features["code_line_after"] = train_features["code_line_after"].apply(lambda x: " ".join(x))
validation_features["code_line_after"] = validation_features["code_line_after"].apply(lambda x: " ".join(x))
test_features["code_line_after"] = test_features["code_line_after"].apply(lambda x: " ".join(x)) | [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] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;32m---> 12[0m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m [43mtrain_features[49m[43m[[49m[38;5;124;43m"[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m"[39;49m[43m][49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[38;5;28;43;01mlambda[39;49;00m[43m [49m[43mx[49m[43m:[49m[43m [49m[38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m[43m)[49m
[1;32m 13[0m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/series.py:4917[0m, in [0;36mSeries.apply[0;34m(self, func, convert_dtype, args, by_row, **kwargs)[0m
[1;32m 4789[0m [38;5;28;01mdef[39;00m[38;5;250m [39m[38;5;21mapply[39m(
[1;32m 4790[0m [38;5;28mself[39m,
[1;32m 4791[0m func: AggFuncType,
[0;32m (...)[0m
[1;32m 4796[0m [38;5;241m*[39m[38;5;241m*[39mkwargs,
[1;32m 4797[0m ) [38;5;241m-[39m[38;5;241m>[39m DataFrame [38;5;241m|[39m Series:
[1;32m 4798[0m [38;5;250m [39m[38;5;124;03m"""[39;00m
[1;32m 4799[0m [38;5;124;03m Invoke function on values of Series.[39;00m
[1;32m 4800[0m
[0;32m (...)[0m
[1;32m 4915[0m [38;5;124;03m dtype: float64[39;00m
[1;32m 4916[0m [38;5;124;03m """[39;00m
[0;32m-> 4917[0m [38;5;28;01mreturn[39;00m [43mSeriesApply[49m[43m([49m
[1;32m 4918[0m [43m [49m[38;5;28;43mself[39;49m[43m,[49m
[1;32m 4919[0m [43m [49m[43mfunc[49m[43m,[49m
[1;32m 4920[0m [43m [49m[43mconvert_dtype[49m[38;5;241;43m=[39;49m[43mconvert_dtype[49m[43m,[49m
[1;32m 4921[0m [43m [49m[43mby_row[49m[38;5;241;43m=[39;49m[43mby_row[49m[43m,[49m
[1;32m 4922[0m [43m [49m[43margs[49m[38;5;241;43m=[39;49m[43margs[49m[43m,[49m
[1;32m 4923[0m [43m [49m[43mkwargs[49m[38;5;241;43m=[39;49m[43mkwargs[49m[43m,[49m
[1;32m 4924[0m [43m [49m[43m)[49m[38;5;241;43m.[39;49m[43mapply[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1427[0m, in [0;36mSeriesApply.apply[0;34m(self)[0m
[1;32m 1424[0m [38;5;28;01mreturn[39;00m [38;5;28mself[39m[38;5;241m.[39mapply_compat()
[1;32m 1426[0m [38;5;66;03m# self.func is Callable[39;00m
[0;32m-> 1427[0m [38;5;28;01mreturn[39;00m [38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mapply_standard[49m[43m([49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/apply.py:1507[0m, in [0;36mSeriesApply.apply_standard[0;34m(self)[0m
[1;32m 1501[0m [38;5;66;03m# row-wise access[39;00m
[1;32m 1502[0m [38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons[39;00m
[1;32m 1503[0m [38;5;66;03m# we need to give `na_action="ignore"` for categorical data.[39;00m
[1;32m 1504[0m [38;5;66;03m# TODO: remove the `na_action="ignore"` when that default has been changed in[39;00m
[1;32m 1505[0m [38;5;66;03m# Categorical (GH51645).[39;00m
[1;32m 1506[0m action [38;5;241m=[39m [38;5;124m"[39m[38;5;124mignore[39m[38;5;124m"[39m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(obj[38;5;241m.[39mdtype, CategoricalDtype) [38;5;28;01melse[39;00m [38;5;28;01mNone[39;00m
[0;32m-> 1507[0m mapped [38;5;241m=[39m [43mobj[49m[38;5;241;43m.[39;49m[43m_map_values[49m[43m([49m
[1;32m 1508[0m [43m [49m[43mmapper[49m[38;5;241;43m=[39;49m[43mcurried[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43maction[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[38;5;28;43mself[39;49m[38;5;241;43m.[39;49m[43mconvert_dtype[49m
[1;32m 1509[0m [43m[49m[43m)[49m
[1;32m 1511[0m [38;5;28;01mif[39;00m [38;5;28mlen[39m(mapped) [38;5;129;01mand[39;00m [38;5;28misinstance[39m(mapped[[38;5;241m0[39m], ABCSeries):
[1;32m 1512[0m [38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested[39;00m
[1;32m 1513[0m [38;5;66;03m# See also GH#25959 regarding EA support[39;00m
[1;32m 1514[0m [38;5;28;01mreturn[39;00m obj[38;5;241m.[39m_constructor_expanddim([38;5;28mlist[39m(mapped), index[38;5;241m=[39mobj[38;5;241m.[39mindex)
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/base.py:921[0m, in [0;36mIndexOpsMixin._map_values[0;34m(self, mapper, na_action, convert)[0m
[1;32m 918[0m [38;5;28;01mif[39;00m [38;5;28misinstance[39m(arr, ExtensionArray):
[1;32m 919[0m [38;5;28;01mreturn[39;00m arr[38;5;241m.[39mmap(mapper, na_action[38;5;241m=[39mna_action)
[0;32m--> 921[0m [38;5;28;01mreturn[39;00m [43malgorithms[49m[38;5;241;43m.[39;49m[43mmap_array[49m[43m([49m[43marr[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mna_action[49m[38;5;241;43m=[39;49m[43mna_action[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
File [0;32m/usr/local/lib/python3.9/site-packages/pandas/core/algorithms.py:1743[0m, in [0;36mmap_array[0;34m(arr, mapper, na_action, convert)[0m
[1;32m 1741[0m values [38;5;241m=[39m arr[38;5;241m.[39mastype([38;5;28mobject[39m, copy[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
[1;32m 1742[0m [38;5;28;01mif[39;00m na_action [38;5;129;01mis[39;00m [38;5;28;01mNone[39;00m:
[0;32m-> 1743[0m [38;5;28;01mreturn[39;00m [43mlib[49m[38;5;241;43m.[39;49m[43mmap_infer[49m[43m([49m[43mvalues[49m[43m,[49m[43m [49m[43mmapper[49m[43m,[49m[43m [49m[43mconvert[49m[38;5;241;43m=[39;49m[43mconvert[49m[43m)[49m
[1;32m 1744[0m [38;5;28;01melse[39;00m:
[1;32m 1745[0m [38;5;28;01mreturn[39;00m lib[38;5;241m.[39mmap_infer_mask(
[1;32m 1746[0m values, mapper, mask[38;5;241m=[39misna(values)[38;5;241m.[39mview(np[38;5;241m.[39muint8), convert[38;5;241m=[39mconvert
[1;32m 1747[0m )
File [0;32mlib.pyx:2972[0m, in [0;36mpandas._libs.lib.map_infer[0;34m()[0m
File [0;32m<ipython-input-1-fac9f849a0a1>:12[0m, in [0;36m<lambda>[0;34m(x)[0m
[1;32m 9[0m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 10[0m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mtext[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;32m---> 12[0m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m train_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124;43m"[39;49m[38;5;124;43m [39;49m[38;5;124;43m"[39;49m[38;5;241;43m.[39;49m[43mjoin[49m[43m([49m[43mx[49m[43m)[49m)
[1;32m 13[0m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m validation_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[1;32m 14[0m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m] [38;5;241m=[39m test_features[[38;5;124m"[39m[38;5;124mcode_line_before[39m[38;5;124m"[39m][38;5;241m.[39mapply([38;5;28;01mlambda[39;00m x: [38;5;124m"[39m[38;5;124m [39m[38;5;124m"[39m[38;5;241m.[39mjoin(x))
[0;31mTypeError[0m: can only join an iterable
Error: can only join an iterable
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} | 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], ...
...
1922 print(num_mistakes_dict['177'][1])
1923 print(num_mistakes_dict['279'][1]) print("----...
1924 print(num_mistakes_dict['2854'][1]) print("---...
1925 error_type_dict = {} for key in error_dict: fo...
1926 misspelled_words = {} for key in error_dict: f...
Name: text, Length: 1927, dtype: object
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], ...
...
1922 print(num_mistakes_dict['177'][1])
1923 print(num_mistakes_dict['279'][1]) print("----...
1924 print(num_mistakes_dict['2854'][1]) print("---...
1925 error_type_dict = {} for key in error_dict: fo...
1926 misspelled_words = {} for key in error_dict: f...
Name: text, Length: 1927, dtype: object
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} | 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()
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} | 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
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prediction 0.030859
comment_only 0.023144
save_results 0.018858
Name: proportion, dtype: float64
primary_label
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comment_only 0.023144
save_results 0.018858
Name: proportion, dtype: float64
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} | 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_features['text'])
X2 = vectorizer1.fit_transform(train_features['code_line_before'])
X3 = vectorizer2.fit_transform(train_features['code_line_after'])
X = hstack((X, X1, X2, X3))
clf = lgb.LGBMClassifier()
clf.fit(X, target)
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X1 = vectorizer.transform(validation_features['text'])
X2 = vectorizer1.transform(validation_features['code_line_before'])
X3 = vectorizer2.transform(validation_features['code_line_after'])
X = hstack((X, X1, X2, X3))
pred = clf.predict(X)
f1_score(pred, validation_features["primary_label"], average='weighted') | [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(train_features[train_columns][38;5;241m.[39mvalues)
[1;32m 10[0m X1 [38;5;241m=[39m vectorizer[38;5;241m.[39mfit_transform(train_features[[38;5;124m'[39m[38;5;124mtext[39m[38;5;124m'[39m])
[0;32m---> 11[0m X2 [38;5;241m=[39m [43mvectorizer1[49m[38;5;241;43m.[39;49m[43mfit_transform[49m[43m([49m[43mtrain_features[49m[43m[[49m[38;5;124;43m'[39;49m[38;5;124;43mcode_line_before[39;49m[38;5;124;43m'[39;49m[43m][49m[43m)[49m
[1;32m 12[0m X3 [38;5;241m=[39m vectorizer2[38;5;241m.[39mfit_transform(train_features[[38;5;124m'[39m[38;5;124mcode_line_after[39m[38;5;124m'[39m])
[1;32m 14[0m X [38;5;241m=[39m hstack((X, X1, X2, X3))
File [0;32m/usr/local/lib/python3.9/site-packages/sklearn/feature_extraction/text.py:2104[0m, in [0;36mTfidfVectorizer.fit_transform[0;34m(self, raw_documents, y)[0m
[1;32m 2097[0m [38;5;28mself[39m[38;5;241m.[39m_check_params()
[1;32m 2098[0m [38;5;28mself[39m[38;5;241m.[39m_tfidf [38;5;241m=[39m TfidfTransformer(
[1;32m 2099[0m norm[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39mnorm,
[1;32m 2100[0m use_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39muse_idf,
[1;32m 2101[0m smooth_idf[38;5;241m=[39m[38;5;28mself[39m[38;5;241m.[39msmooth_idf,
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},
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"name": "vectorizer",
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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"type": "TfidfVectorizer",
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} | 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_features['text'])
# X2 = vectorizer1.fit_transform(train_features['code_line_before'])
# X3 = vectorizer2.fit_transform(train_features['code_line_after'])
X = hstack((X, X1))
clf = lgb.LGBMClassifier()
clf.fit(X, target)
X = scipy.sparse.csr_matrix(validation_features[train_columns].values)
X1 = vectorizer.transform(validation_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)
f1_score(pred, validation_features["primary_label"], average='weighted') | Out[1]: 0.7263211151032674
0.7263211151032674
| 2.090027 | 514,310,144 | {
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},
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},
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},
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},
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},
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"name": "test_features",
"size": 1935634,
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"value": " filename ... packages_info\n0 nb_28545.ipynb ... [harmonic ======== Planning, Matplotlib strive...\n1 nb_28545.ipynb ... []\n2 nb_28545.ipynb ... []\n3 nb_28545.ipynb ... []\n4 nb_28545.ipynb ... []\n... ... ... ...\n1913 nb_96779.ipynb ... []\n1914 nb_96779.ipynb ... []\n1915 nb_96779.ipynb ... []\n1916 nb_96779.ipynb ... []\n1917 nb_96779.ipynb ... []\n\n[1918 rows x 21 columns]"
},
"text": {
"name": "text",
"size": 786640,
"type": "Series",
"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
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"time": null,
"tokenize_py": null,
"top_user_daily_actions": null,
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"tqdm": null,
"train_columns": {
"name": "train_columns",
"size": 152,
"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5814341,
"type": "DataFrame",
"value": " filename ... primary_label\n0 nb_54880.ipynb ... helper_functions\n1 nb_54880.ipynb ... load_data\n2 nb_54880.ipynb ... data_exploration\n3 nb_54880.ipynb ... data_exploration\n4 nb_54880.ipynb ... data_preprocessing\n... ... ... ...\n5828 nb_95821.ipynb ... evaluation\n5829 nb_95821.ipynb ... modelling\n5830 nb_95821.ipynb ... data_preprocessing\n5831 nb_95821.ipynb ... modelling\n5832 nb_95821.ipynb ... evaluation\n\n[5833 rows x 22 columns]"
},
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"size": 136,
"type": "function",
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},
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"name": "validation_features",
"size": 2174322,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
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"type": "TfidfVectorizer",
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},
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},
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},
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"type": "TfidfVectorizer",
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},
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} | 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,
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} | 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)
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},
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},
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},
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"size": 5814341,
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},
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},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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},
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},
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} | 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)
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"Readliner": null,
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"name": "TfidfVectorizer",
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"type": "type",
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},
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},
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},
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},
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"size": 1935634,
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},
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"value": "0 [import pandas as pd, import numpy as np]\n1 [l_cols = ['user_id','movie_id','rating'], r_c...\n2 [l.head()]\n3 [r.head()]\n4 [movies = pd.merge(l,r)]\n ... \n5828 [plot_confusion_matrix(cm, title=\"Confusion ma...\n5829 [srm = brainiak.funcalign.srm.DetSRM(n_iter=10...\n5830 [image_data_shared = srm.transform(image_data)...\n5831 [accuracy = np.zeros((subjects,)), cm = [None]...\n5832 [plot_confusion_matrix(cm, title=\"Confusion ma...\nName: text, Length: 5833, dtype: object"
},
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"time": null,
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"name": "train_columns",
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"type": "list",
"value": "['cell_number', 'execution_count', 'linesofcomment', 'linesofcode', 'variable_count', 'function_count', 'display_data', 'stream', 'error']"
},
"train_features": {
"name": "train_features",
"size": 5814341,
"type": "DataFrame",
"value": " filename ... primary_label\n0 nb_54880.ipynb ... helper_functions\n1 nb_54880.ipynb ... load_data\n2 nb_54880.ipynb ... data_exploration\n3 nb_54880.ipynb ... data_exploration\n4 nb_54880.ipynb ... data_preprocessing\n... ... ... ...\n5828 nb_95821.ipynb ... evaluation\n5829 nb_95821.ipynb ... modelling\n5830 nb_95821.ipynb ... data_preprocessing\n5831 nb_95821.ipynb ... modelling\n5832 nb_95821.ipynb ... evaluation\n\n[5833 rows x 22 columns]"
},
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},
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"name": "validation_features",
"size": 2174322,
"type": "DataFrame",
"value": " filename cell_type ... save_results comment_only\n0 nb_128972.ipynb code ... 0 0\n1 nb_128972.ipynb code ... 0 0\n2 nb_128972.ipynb code ... 0 0\n3 nb_128972.ipynb code ... 0 0\n4 nb_128972.ipynb code ... 0 0\n... ... ... ... ... ...\n1922 nb_750781.ipynb code ... 0 0\n1923 nb_750781.ipynb code ... 0 0\n1924 nb_750781.ipynb code ... 0 0\n1925 nb_750781.ipynb code ... 0 0\n1926 nb_750781.ipynb code ... 0 0\n\n[1927 rows x 32 columns]"
},
"vectorizer": {
"name": "vectorizer",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer1": {
"name": "vectorizer1",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer2": {
"name": "vectorizer2",
"size": 48,
"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
"vectorizer3": {
"name": "vectorizer3",
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"type": "TfidfVectorizer",
"value": "TfidfVectorizer()"
},
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} | 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)
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"Readliner": null,
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"TfidfVectorizer": {
"name": "TfidfVectorizer",
"size": 2008,
"type": "type",
"value": "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>"
},
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},
"X1": {
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"value": " (0, 1289)\t0.15885296956008174\n (0, 1416)\t0.3496121221457142\n (0, 4295)\t0.1824194221358756\n (0, 4699)\t0.17829403023019316\n (0, 4790)\t0.07439651573307421\n (0, 5928)\t0.273090433031957\n (0, 6086)\t0.10011171939660647\n (0, 7236)\t0.2772403616303452\n (0, 8217)\t0.06744242871048134\n (0, 8332)\t0.08803524357344077\n (0, 9182)\t0.07034769585034792\n (0, 9578)\t0.09850303118113365\n (0, 9885)\t0.49373696586116833\n (0, 10502)\t0.4063046855578408\n (0, 10750)\t0.4063046855578408\n (0, 11916)\t0.11555863418791022\n (0, 12478)\t0.0789073856360859\n (1, 31)\t0.4095686187958287\n (1, 62)\t0.25331785011924673\n (1, 5392)\t0.6197110670742616\n (1, 5401)\t0.6197110670742616\n (2, 5392)\t0.25111811752889757\n (2, 5401)\t0.25111811752889757\n (2, 8150)\t0.5004244472233608\n (2, 10190)\t0.789591511301052\n :\t:\n (1912, 5756)\t0.3025167444786825\n (1912, 10465)\t0.9531440705947971\n (1914, 9309)\t0.8143019942915565\n (1914, 9415)\t0.5804414372637382\n (1915, 9309)\t0.8143019942915565\n (1915, 9415)\t0.5804414372637382\n (1916, 62)\t0.23299760096642663\n (1916, 127)\t0.24099465932818226\n (1916, 219)\t0.26299734678948977\n (1916, 265)\t0.2170675543063656\n (1916, 314)\t0.16178690217359176\n (1916, 475)\t0.24099465932818226\n (1916, 498)\t0.22104062899140903\n (1916, 499)\t0.23725585262958482\n (1916, 1646)\t0.2850000342507972\n (1916, 2618)\t0.2721292871708448\n (1916, 5262)\t0.245233368769108\n (1916, 6585)\t0.20122799384649304\n (1916, 6982)\t0.245233368769108\n (1916, 11193)\t0.26299734678948977\n (1916, 11194)\t0.2850000342507972\n (1916, 12922)\t0.21525316516827733\n (1916, 13706)\t0.26299734678948977\n (1917, 9309)\t0.8143019942915565\n (1917, 9415)\t0.5804414372637382"
},
"X2": {
"name": "X2",
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"value": " (0, 5928)\t0.5163862850483787\n (0, 8786)\t0.3558057453091318\n (0, 1289)\t0.600749677748211\n (0, 8936)\t0.2644837387295696\n (0, 8332)\t0.3329314167288935\n (0, 8217)\t0.25505357203318946\n (1, 8936)\t0.13504093794218394\n (1, 6492)\t0.39227128128564803\n (1, 12722)\t0.15070921240643548\n (1, 7670)\t0.37455610991232274\n (1, 9862)\t0.12763921978626358\n (1, 9746)\t0.39227128128564803\n (1, 12075)\t0.08913990946874038\n (1, 9900)\t0.19020164731249567\n (1, 3075)\t0.06905797226187309\n (1, 10704)\t0.2534189679486199\n (1, 7898)\t0.2507702182943471\n (1, 12704)\t0.3375368288426258\n (1, 9833)\t0.16336991054449349\n (1, 6275)\t0.14695505860734503\n (1, 3994)\t0.1418516267197728\n (1, 6262)\t0.16585135515189833\n (1, 675)\t0.16585135515189833\n (1, 2657)\t0.148136183778573\n (1, 12603)\t0.18099349789736122\n :\t:\n (5831, 9323)\t0.29667945547250524\n (5832, 8217)\t0.16028844651757307\n (5832, 12075)\t0.10971765781838334\n (5832, 7323)\t0.1147810162510238\n (5832, 4709)\t0.07566267269096397\n (5832, 9415)\t0.08270028936005187\n (5832, 11777)\t0.11982910271737358\n (5832, 1076)\t0.15181520730198833\n (5832, 2399)\t0.16771609806839577\n (5832, 853)\t0.3850659908550527\n (5832, 4724)\t0.11653251292101924\n (5832, 2627)\t0.18550009637025716\n (5832, 13189)\t0.12148662041890751\n (5832, 3509)\t0.19813777844657318\n (5832, 11958)\t0.11063613914309335\n (5832, 1426)\t0.16019848675176263\n (5832, 6228)\t0.12660744935867593\n (5832, 11323)\t0.1473973483809412\n (5832, 1136)\t0.2009707623161172\n (5832, 9136)\t0.18909055223525678\n (5832, 7237)\t0.2041377688368914\n (5832, 3362)\t0.2167754509132074\n (5832, 11213)\t0.41545644940378196\n (5832, 11464)\t0.3864708586863744\n (5832, 3350)\t0.2414131137701598"
},
"X3": {
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