prompt stringlengths 105 4.73k | reference_code stringlengths 11 774 | code_context stringlengths 746 120k | problem_id int64 0 999 | library_problem_id int64 0 290 | library class label 7
classes | test_case_cnt int64 0 5 | perturbation_type class label 4
classes | perturbation_origin_id int64 0 289 |
|---|---|---|---|---|---|---|---|---|
Problem:
When using SelectKBest or SelectPercentile in sklearn.feature_selection, it's known that we can use following code to get selected features
np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
However, I'm not clear how to perform feature selection when using linear models like LinearSVC,... | svc = LinearSVC(penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)] | import numpy as np
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"This is the first document.",
... | 900 | 83 | 5Sklearn | 1 | 3Surface | 82 |
Problem:
This question and answer demonstrate that when feature selection is performed using one of scikit-learn's dedicated feature selection routines, then the names of the selected features can be retrieved as follows:
np.asarray(vectorizer.get_feature_names())[featureSelector.get_support()]
For example, in the ab... | # def solve(corpus, y, vectorizer, X):
### BEGIN SOLUTION
svc = LinearSVC(penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names_out())[np.flatnonzero(svc.coef_)]
### END SOLUTION
# return selected_feature_names
# selected_feature_names = solve(c... | import numpy as np
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"This is the first document.",
... | 901 | 84 | 5Sklearn | 1 | 3Surface | 82 |
Problem:
I am trying to vectorize some data using
sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:
corpus = [
'We are looking for Java developer',
'Frontend developer with knowledge in SQL and Jscript',
'And this is the third one.',
'Is this the first document?',
]... | vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
vocabulary=['Jscript', '.Net', 'TypeScript', 'SQL', 'NodeJS', 'Angular', 'Mongo',
'CSS',
'Python', 'PHP', 'Photoshop', 'Oracle',... | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"We are looking for Java developer",
"Frontend developer with k... | 902 | 85 | 5Sklearn | 1 | 1Origin | 85 |
Problem:
I am trying to vectorize some data using
sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:
corpus = [
'We are looking for Java developer',
'Frontend developer with knowledge in SQL and Jscript',
'And this is the third one.',
'Is this the first document?',
]... | vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
vocabulary=['Jscript', '.Net', 'TypeScript', 'NodeJS', 'Angular', 'Mongo',
'CSS',
'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux... | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"We are looking for Java developer",
"Frontend developer with k... | 903 | 86 | 5Sklearn | 1 | 3Surface | 85 |
Problem:
I am trying to vectorize some data using
sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:
corpus = [
'We are looking for Java developer',
'Frontend developer with knowledge in SQL and Jscript',
'And this is the third one.',
'Is this the first document?',
]... | vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
vocabulary=['Jscript', '.Net', 'TypeScript', 'SQL', 'NodeJS', 'Angular', 'Mongo',
'CSS',
'Python', 'PHP', 'Photoshop', 'Oracle',... | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"We are looking for Java developer",
"Frontend developer with k... | 904 | 87 | 5Sklearn | 1 | 2Semantic | 85 |
Problem:
I am trying to vectorize some data using
sklearn.feature_extraction.text.CountVectorizer.
This is the data that I am trying to vectorize:
corpus = [
'We are looking for Java developer',
'Frontend developer with knowledge in SQL and Jscript',
'And this is the third one.',
'Is this the first document?',
]... | vectorizer = CountVectorizer(stop_words="english", binary=True, lowercase=False,
vocabulary=['Jscript', '.Net', 'TypeScript', 'NodeJS', 'Angular', 'Mongo',
'CSS',
'Python', 'PHP', 'Photoshop', 'Oracle', 'Linux... | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
corpus = [
"We are looking for Java developer",
"Frontend developer with k... | 905 | 88 | 5Sklearn | 1 | 0Difficult-Rewrite | 85 |
Problem:
I'm trying to find a way to iterate code for a linear regression over many many columns, upwards of Z3. Here is a snippet of the dataframe called df1
Time A1 A2 A3 B1 B2 B3
1 1.00 6.64 6.82 6.79 6.70 6.95 7.02
2 2.00 6.70 6.86 6.92 NaN NaN... | slopes = []
for col in df1.columns:
if col == "Time":
continue
mask = ~np.isnan(df1[col])
x = np.atleast_2d(df1.Time[mask].values).T
y = np.atleast_2d(df1[col][mask].values).T
reg = LinearRegression().fit(x, y)
slopes.append(reg.coef_[0])
slopes = np.array(slopes).reshape(-1) | import numpy as np
import pandas as pd
import copy
from sklearn.linear_model import LinearRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df1 = pd.DataFrame(
{
"Time": [1, 2, 3, 4, 5, 5.5, 6],
... | 906 | 89 | 5Sklearn | 2 | 1Origin | 89 |
Problem:
I'm trying to iterate code for a linear regression over all columns, upwards of Z3. Here is a snippet of the dataframe called df1
Time A1 A2 A3 B1 B2 B3
1 5.00 NaN NaN NaN NaN 7.40 7.51
2 5.50 7.44 7.63 7.58 7.54 NaN NaN
3 6.00 ... | slopes = []
for col in df1.columns:
if col == "Time":
continue
mask = ~np.isnan(df1[col])
x = np.atleast_2d(df1.Time[mask].values).T
y = np.atleast_2d(df1[col][mask].values).T
reg = LinearRegression().fit(x, y)
slopes.append(reg.coef_[0])
slopes = np.array(slopes).reshape(-1) | import numpy as np
import pandas as pd
import copy
from sklearn.linear_model import LinearRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df1 = pd.DataFrame(
{
"Time": [1, 2, 3, 4, 5, 5.5, 6],
... | 907 | 90 | 5Sklearn | 2 | 3Surface | 89 |
Problem:
I was playing with the Titanic dataset on Kaggle (https://www.kaggle.com/c/titanic/data), and I want to use LabelEncoder from sklearn.preprocessing to transform Sex, originally labeled as 'male' into '1' and 'female' into '0'.. I had the following four lines of code,
import pandas as pd
from sklearn.preproce... | le = LabelEncoder()
transformed_df = df.copy()
transformed_df['Sex'] = le.fit_transform(df['Sex']) | import pandas as pd
import copy
import tokenize, io
from sklearn.preprocessing import LabelEncoder
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.read_csv("train.csv")
elif test_case_id == 2:
df = pd.read_csv("test.c... | 908 | 91 | 5Sklearn | 2 | 1Origin | 91 |
Problem:
I'd like to use LabelEncoder to transform a dataframe column 'Sex', originally labeled as 'male' into '1' and 'female' into '0'.
I tried this below:
df = pd.read_csv('data.csv')
df['Sex'] = LabelEncoder.fit_transform(df['Sex'])
However, I got an error:
TypeError: fit_transform() missing 1 required positiona... | le = LabelEncoder()
transformed_df = df.copy()
transformed_df['Sex'] = le.fit_transform(df['Sex']) | import pandas as pd
import copy
import tokenize, io
from sklearn.preprocessing import LabelEncoder
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.read_csv("train.csv")
elif test_case_id == 2:
df = pd.read_csv("test.c... | 909 | 92 | 5Sklearn | 2 | 3Surface | 91 |
Problem:
I was playing with the Titanic dataset on Kaggle (https://www.kaggle.com/c/titanic/data), and I want to use LabelEncoder from sklearn.preprocessing to transform Sex, originally labeled as 'male' into '1' and 'female' into '0'.. I had the following four lines of code,
import pandas as pd
from sklearn.preproce... | # def Transform(df):
### BEGIN SOLUTION
le = LabelEncoder()
transformed_df = df.copy()
transformed_df['Sex'] = le.fit_transform(df['Sex'])
### END SOLUTION
# return transformed_df
# transformed_df = Transform(df)
return transformed_df
| import pandas as pd
import copy
import tokenize, io
from sklearn.preprocessing import LabelEncoder
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.read_csv("train.csv")
elif test_case_id == 2:
df = pd.read_csv("test.c... | 910 | 93 | 5Sklearn | 2 | 3Surface | 91 |
Problem:
I am trying to run an Elastic Net regression but get the following error: NameError: name 'sklearn' is not defined... any help is greatly appreciated!
# ElasticNet Regression
from sklearn import linear_model
import statsmodels.api as sm
ElasticNet = sklearn.linear_model.ElasticNet() # creat... | ElasticNet = linear_model.ElasticNet()
ElasticNet.fit(X_train, y_train)
training_set_score = ElasticNet.score(X_train, y_train)
test_set_score = ElasticNet.score(X_test, y_test) | import numpy as np
import copy
from sklearn import linear_model
import sklearn
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
X_train, y_train = ... | 911 | 94 | 5Sklearn | 1 | 1Origin | 94 |
Problem:
Right now, I have my data in a 2 by 2 numpy array. If I was to use MinMaxScaler fit_transform on the array, it will normalize it column by column, whereas I wish to normalize the entire np array all together. Is there anyway to do that?
A:
<code>
import numpy as np
import pandas as pd
from sklearn.preproces... | scaler = MinMaxScaler()
X_one_column = np_array.reshape([-1, 1])
result_one_column = scaler.fit_transform(X_one_column)
transformed = result_one_column.reshape(np_array.shape) | import numpy as np
import copy
from sklearn.preprocessing import MinMaxScaler
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
X = np.array([[-1, 2], [-0.5, 6]])
return X
def generate_ans(data):
X = data
scaler = MinM... | 912 | 95 | 5Sklearn | 1 | 1Origin | 95 |
Problem:
Right now, I have my data in a 3 by 3 numpy array. If I was to use MinMaxScaler fit_transform on the array, it will normalize it column by column, whereas I wish to normalize the entire np array all together. Is there anyway to do that?
A:
<code>
import numpy as np
import pandas as pd
from sklearn.preproces... | scaler = MinMaxScaler()
X_one_column = np_array.reshape([-1, 1])
result_one_column = scaler.fit_transform(X_one_column)
transformed = result_one_column.reshape(np_array.shape) | import numpy as np
import copy
from sklearn.preprocessing import MinMaxScaler
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
X = np.array([[-1, 2, 1], [-0.5, 6, 0.5], [1.5, 2, -2]])
return X
def generate_ans(data):
X = data... | 913 | 96 | 5Sklearn | 1 | 3Surface | 95 |
Problem:
Right now, I have my data in a 2 by 2 numpy array. If I was to use MinMaxScaler fit_transform on the array, it will normalize it column by column, whereas I wish to normalize the entire np array all together. Is there anyway to do that?
A:
<code>
import numpy as np
import pandas as pd
from sklearn.preproces... | # def Transform(a):
### BEGIN SOLUTION
scaler = MinMaxScaler()
a_one_column = a.reshape([-1, 1])
result_one_column = scaler.fit_transform(a_one_column)
new_a = result_one_column.reshape(a.shape)
### END SOLUTION
# return new_a
# transformed = Transform(np_array)
return new_a
| import numpy as np
import copy
from sklearn.preprocessing import MinMaxScaler
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
X = np.array([[-1, 2], [-0.5, 6]])
return X
def generate_ans(data):
X = data
scaler = MinM... | 914 | 97 | 5Sklearn | 1 | 3Surface | 95 |
Problem:
So I fed the testing data, but when I try to test it with clf.predict() it just gives me an error. So I want it to predict on the data that i give, which is the last close price, the moving averages. However everytime i try something it just gives me an error. Also is there a better way to do this than on pan... | close_buy1 = close[:-1]
m5 = ma_50[:-1]
m10 = ma_100[:-1]
ma20 = ma_200[:-1]
# b = np.concatenate([close_buy1, m5, m10, ma20], axis=1)
predict = clf.predict(pd.concat([close_buy1, m5, m10, ma20], axis=1)) | import numpy as np
import pandas as pd
import copy
from sklearn import tree
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
dataframe_csv = """Date,High,Low,Open,Close,Volume,Adj Close
2012-04-30,15.34448528289795,14.959178924560547,15.26752376... | 915 | 98 | 5Sklearn | 1 | 1Origin | 98 |
Problem:
Are you able to train a DecisionTreeClassifier with string data?
When I try to use String data I get a ValueError: could not converter string to float
X = [['asdf', '1'], ['asdf', '0']]
clf = DecisionTreeClassifier()
clf.fit(X, ['2', '3'])
So how can I use this String data to train my model?
Note I need... | from sklearn.feature_extraction import DictVectorizer
X = [dict(enumerate(x)) for x in X]
vect = DictVectorizer(sparse=False)
new_X = vect.fit_transform(X) | def generate_test_case(test_case_id):
return None, None
def exec_test(result, ans):
try:
assert len(result[0]) > 1 and len(result[1]) > 1
return 1
except:
return 0
exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
X = [['as... | 916 | 99 | 5Sklearn | 0 | 1Origin | 99 |
Problem:
Can I use string as input for a DecisionTreeClassifier?
I get a ValueError when I ran this piece of code below: could not converter string to float
X = [['asdf', '1'], ['asdf', '0']]
clf = DecisionTreeClassifier()
clf.fit(X, ['2', '3'])
What should I do to use this kind of string input to train my classifie... | from sklearn.feature_extraction import DictVectorizer
X = [dict(enumerate(x)) for x in X]
vect = DictVectorizer(sparse=False)
new_X = vect.fit_transform(X) | def generate_test_case(test_case_id):
return None, None
def exec_test(result, ans):
try:
assert len(result[0]) > 1 and len(result[1]) > 1
return 1
except:
return 0
exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
X = [['as... | 917 | 100 | 5Sklearn | 0 | 3Surface | 99 |
Problem:
Are you able to train a DecisionTreeClassifier with string data?
When I try to use String data I get a ValueError: could not converter string to float
X = [['dsa', '2'], ['sato', '3']]
clf = DecisionTreeClassifier()
clf.fit(X, ['4', '5'])
So how can I use this String data to train my model?
Note I need ... | from sklearn.feature_extraction import DictVectorizer
X = [dict(enumerate(x)) for x in X]
vect = DictVectorizer(sparse=False)
new_X = vect.fit_transform(X) | def generate_test_case(test_case_id):
return None, None
def exec_test(result, ans):
try:
assert len(result[0]) > 1 and len(result[1]) > 1
return 1
except:
return 0
exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
X = [['ds... | 918 | 101 | 5Sklearn | 0 | 3Surface | 99 |
Problem:
I have been trying this for the last few days and not luck. What I want to do is do a simple Linear regression fit and predict using sklearn, but I cannot get the data to work with the model. I know I am not reshaping my data right I just dont know how to do that.
Any help on this will be appreciated. I have ... | # Seperating the data into dependent and independent variables
X = dataframe.iloc[:, 0:-1].astype(float)
y = dataframe.iloc[:, -1]
logReg = LogisticRegression()
logReg.fit(X[:None], y) | import numpy as np
import pandas as pd
import copy
from sklearn.linear_model import LogisticRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
dataframe = pd.DataFrame(
{
"Name": [
... | 919 | 102 | 5Sklearn | 1 | 1Origin | 102 |
Problem:
I want to perform a Linear regression fit and prediction, but it doesn't work.
I guess my data shape is not proper, but I don't know how to fix it.
The error message is Found input variables with inconsistent numbers of samples: [1, 9] , which seems to mean that the Y has 9 values and the X only has 1.
I woul... | # Seperating the data into dependent and independent variables
X = dataframe.iloc[:, 0:-1].astype(float)
y = dataframe.iloc[:, -1]
logReg = LogisticRegression()
logReg.fit(X[:None], y) | import numpy as np
import pandas as pd
import copy
from sklearn.linear_model import LogisticRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
dataframe = pd.DataFrame(
{
"Name": [
... | 920 | 103 | 5Sklearn | 1 | 3Surface | 102 |
Problem:
I have a data which include dates in sorted order.
I would like to split the given data to train and test set. However, I must to split the data in a way that the test have to be newer than the train set.
Please look at the given example:
Let's assume that we have data by dates:
1, 2, 3, ..., n.
The numb... | n = features_dataframe.shape[0]
train_size = 0.2
train_dataframe = features_dataframe.iloc[:int(n * train_size)]
test_dataframe = features_dataframe.iloc[int(n * train_size):] | import pandas as pd
import datetime
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.DataFrame(
{
"date": [
"2017-03-01",
"2017-03-02",
... | 921 | 104 | 5Sklearn | 2 | 1Origin | 104 |
Problem:
I have a data which include dates in sorted order.
I would like to split the given data to train and test set. However, I must to split the data in a way that the test have to be older than the train set.
Please look at the given example:
Let's assume that we have data by dates:
1, 2, 3, ..., n.
The numb... | n = features_dataframe.shape[0]
train_size = 0.8
test_size = 1 - train_size + 0.005
train_dataframe = features_dataframe.iloc[int(n * test_size):]
test_dataframe = features_dataframe.iloc[:int(n * test_size)] | import pandas as pd
import datetime
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.DataFrame(
{
"date": [
"2017-03-01",
"2017-03-02",
... | 922 | 105 | 5Sklearn | 2 | 3Surface | 104 |
Problem:
I have a data which include dates in sorted order.
I would like to split the given data to train and test set. However, I must to split the data in a way that the test have to be newer than the train set.
Please look at the given example:
Let's assume that we have data by dates:
1, 2, 3, ..., n.
The numb... | # def solve(features_dataframe):
### BEGIN SOLUTION
n = features_dataframe.shape[0]
train_size = 0.2
train_dataframe = features_dataframe.iloc[:int(n * train_size)]
test_dataframe = features_dataframe.iloc[int(n * train_size):]
### END SOLUTION
# return train_dataframe, test_dataframe
# trai... | import pandas as pd
import datetime
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.DataFrame(
{
"date": [
"2017-03-01",
"2017-03-02",
... | 923 | 106 | 5Sklearn | 2 | 3Surface | 104 |
Problem:
I would like to apply minmax scaler to column X2 and X3 in dataframe df and add columns X2_scale and X3_scale for each month.
df = pd.DataFrame({
'Month': [1,1,1,1,1,1,2,2,2,2,2,2,2],
'X1': [12,10,100,55,65,60,35,25,10,15,30,40,50],
'X2': [10,15,24,32,8,6,10,23,24,56,45,10,56],
'X3': [12,90,2... | cols = df.columns[2:4]
def scale(X):
X_ = np.atleast_2d(X)
return pd.DataFrame(scaler.fit_transform(X_), X.index)
df[cols + '_scale'] = df.groupby('Month')[cols].apply(scale) | import numpy as np
import pandas as pd
import copy
from sklearn.preprocessing import MinMaxScaler
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.DataFrame(
{
"Month": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2,... | 924 | 107 | 5Sklearn | 1 | 1Origin | 107 |
Problem:
I would like to apply minmax scaler to column A2 and A3 in dataframe myData and add columns new_A2 and new_A3 for each month.
myData = pd.DataFrame({
'Month': [3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8],
'A1': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2],
'A2': [31, 13, 13, 13, 33, 33, 81, 38, 18, 38, 18,... | cols = myData.columns[2:4]
def scale(X):
X_ = np.atleast_2d(X)
return pd.DataFrame(scaler.fit_transform(X_), X.index)
myData['new_' + cols] = myData.groupby('Month')[cols].apply(scale) | import numpy as np
import pandas as pd
import copy
from sklearn.preprocessing import MinMaxScaler
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
myData = pd.DataFrame(
{
"Month": [3, 3, 3, 3, 3, 3, 8, 8, 8, 8... | 925 | 108 | 5Sklearn | 1 | 3Surface | 107 |
Problem:
Here is my code:
count = CountVectorizer(lowercase = False)
vocabulary = count.fit_transform([words])
print(count.get_feature_names())
For example if:
words = "Hello @friend, this is a good day. #good."
I want it to be separated into this:
['Hello', '@friend', 'this', 'is', 'a', 'good', 'day', '#good']
C... | count = CountVectorizer(lowercase=False, token_pattern='[a-zA-Z0-9$&+:;=@#|<>^*()%-]+')
vocabulary = count.fit_transform([words])
feature_names = count.get_feature_names_out() | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
words = "Hello @friend, this is a good day. #good."
elif test_case_id == 2:
words = (
... | 926 | 109 | 5Sklearn | 2 | 1Origin | 109 |
Problem:
Here is my code:
count = CountVectorizer(lowercase = False)
vocabulary = count.fit_transform([words])
print(count.get_feature_names_out())
For example if:
words = "ha @ji me te no ru bu ru wa, @na n te ko to wa na ka tsu ta wa. wa ta shi da ke no mo na ri za, mo u to kku ni " \
"#de a 't te ta ka r... | count = CountVectorizer(lowercase=False, token_pattern='[a-zA-Z0-9$&+:;=@#|<>^*()%-]+')
vocabulary = count.fit_transform([words])
feature_names = count.get_feature_names_out() | import numpy as np
import copy
from sklearn.feature_extraction.text import CountVectorizer
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
words = "Hello @friend, this is a good day. #good."
elif test_case_id == 2:
words = (
... | 927 | 110 | 5Sklearn | 2 | 3Surface | 109 |
Problem:
I have set up a GridSearchCV and have a set of parameters, with I will find the best combination of parameters. My GridSearch consists of 12 candidate models total.
However, I am also interested in seeing the accuracy score of all of the 12, not just the best score, as I can clearly see by using the .best_sc... | full_results = pd.DataFrame(GridSearch_fitted.cv_results_) | import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import GridSearchCV
import sklearn
from sklearn.linear_model import LogisticRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
np.random.seed(42)
... | 928 | 111 | 5Sklearn | 1 | 1Origin | 111 |
Problem:
I have set up a GridSearchCV and have a set of parameters, with I will find the best combination of parameters. My GridSearch consists of 12 candidate models total.
However, I am also interested in seeing the accuracy score of all of the 12, not just the best score, as I can clearly see by using the .best_sc... | full_results = pd.DataFrame(GridSearch_fitted.cv_results_).sort_values(by="mean_fit_time") | import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import GridSearchCV
import sklearn
from sklearn.linear_model import LogisticRegression
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
np.random.seed(42)
... | 929 | 112 | 5Sklearn | 1 | 2Semantic | 111 |
Problem:
Hey all I am using sklearn.ensemble.IsolationForest, to predict outliers to my data.
Is it possible to train (fit) the model once to my clean data, and then save it to use it for later? For example to save some attributes of the model, so the next time it isn't necessary to call again the fit function to tra... | import pickle
with open('sklearn_model', 'wb') as f:
pickle.dump(fitted_model, f)
| import copy
import sklearn
from sklearn import datasets
from sklearn.svm import SVC
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
iris = datasets.load_iris()
X = iris.data[:100, :2]
y = iris.target[:100]
mo... | 930 | 113 | 5Sklearn | 1 | 1Origin | 113 |
Problem:
I am using python and scikit-learn to find cosine similarity between item descriptions.
A have a df, for example:
items description
1fgg abcd ty
2hhj abc r
3jkl r df
I did following procedures:
1) tokenizing each description
2) transform the corpus into vector space using tf-idf
3) calcul... | from sklearn.metrics.pairwise import cosine_similarity
response = tfidf.fit_transform(df['description']).toarray()
tf_idf = response
cosine_similarity_matrix = np.zeros((len(df), len(df)))
for i in range(len(df)):
for j in range(len(df)):
cosine_similarity_matrix[i, j] = cosine_similarity([tf_idf[i, :]], [... | import numpy as np
import pandas as pd
import copy
from sklearn.feature_extraction.text import TfidfVectorizer
import sklearn
from sklearn.metrics.pairwise import cosine_similarity
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
df = pd.DataFra... | 931 | 114 | 5Sklearn | 2 | 1Origin | 114 |
Problem:
Is it possible in PyTorch to change the learning rate of the optimizer in the middle of training dynamically (I don't want to define a learning rate schedule beforehand)?
So let's say I have an optimizer:
optim = torch.optim.SGD(..., lr=0.01)
Now due to some tests which I perform during training, I realize ... | for param_group in optim.param_groups:
param_group['lr'] = 0.001
| import torch
import copy
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
class MyAttentionBiLSTM(nn.Module):
def __init__(self):
super(MyAttentionBiLSTM, self).__init__()
... | 932 | 0 | 3Pytorch | 1 | 1Origin | 0 |
Problem:
I have written a custom model where I have defined a custom optimizer. I would like to update the learning rate of the optimizer when loss on training set increases.
I have also found this: https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate where I can write a scheduler, however, that is ... | for param_group in optim.param_groups:
param_group['lr'] = 0.001
| import torch
import copy
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
class MyAttentionBiLSTM(nn.Module):
def __init__(self):
super(MyAttentionBiLSTM, self).__init__()
... | 933 | 1 | 3Pytorch | 1 | 3Surface | 0 |
Problem:
Is it possible in PyTorch to change the learning rate of the optimizer in the middle of training dynamically (I don't want to define a learning rate schedule beforehand)?
So let's say I have an optimizer:
optim = torch.optim.SGD(..., lr=0.005)
Now due to some tests which I perform during training, I realize... | for param_group in optim.param_groups:
param_group['lr'] = 0.0005
| import torch
import copy
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
class MyAttentionBiLSTM(nn.Module):
def __init__(self):
super(MyAttentionBiLSTM, self).__init__()
... | 934 | 2 | 3Pytorch | 1 | 3Surface | 0 |
Problem:
I have written a custom model where I have defined a custom optimizer. I would like to update the learning rate of the optimizer when loss on training set increases.
I have also found this: https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate where I can write a scheduler, however, that is ... | for param_group in optim.param_groups:
param_group['lr'] = 0.0005 | import torch
import copy
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
class MyAttentionBiLSTM(nn.Module):
def __init__(self):
super(MyAttentionBiLSTM, self).__init__()
... | 935 | 3 | 3Pytorch | 1 | 0Difficult-Rewrite | 0 |
Problem:
I want to load a pre-trained word2vec embedding with gensim into a PyTorch embedding layer.
How do I get the embedding weights loaded by gensim into the PyTorch embedding layer?
here is my current code
word2vec = Word2Vec(sentences=common_texts, vector_size=100, window=5, min_count=1, workers=4)
And I need to... | weights = torch.FloatTensor(word2vec.wv.vectors)
embedding = torch.nn.Embedding.from_pretrained(weights)
embedded_input = embedding(input_Tensor) | import torch
import copy
from gensim.models import Word2Vec
from gensim.test.utils import common_texts
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
input_Tensor = torch.LongTensor([1, 2, 3, 4, 5, 6, 7])
return in... | 936 | 4 | 3Pytorch | 1 | 1Origin | 4 |
Problem:
I want to load a pre-trained word2vec embedding with gensim into a PyTorch embedding layer.
How do I get the embedding weights loaded by gensim into the PyTorch embedding layer?
here is my current code
And I need to embed my input data use this weights. Thanks
A:
runnable code
<code>
import numpy as np
imp... | # def get_embedded_input(input_Tensor):
weights = torch.FloatTensor(word2vec.wv.vectors)
embedding = torch.nn.Embedding.from_pretrained(weights)
embedded_input = embedding(input_Tensor)
# return embedded_input
return embedded_input
| import torch
import copy
from gensim.models import Word2Vec
from gensim.test.utils import common_texts
from torch import nn
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
input_Tensor = torch.LongTensor([1, 2, 3, 4, 5, 6, 7])
return inp... | 937 | 5 | 3Pytorch | 1 | 3Surface | 4 |
Problem:
I'd like to convert a torch tensor to pandas dataframe but by using pd.DataFrame I'm getting a dataframe filled with tensors instead of numeric values.
import torch
import pandas as pd
x = torch.rand(4,4)
px = pd.DataFrame(x)
Here's what I get when clicking on px in the variable explorer:
0 1 2 3
ten... | px = pd.DataFrame(x.numpy()) | import numpy as np
import pandas as pd
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.rand(4, 4)
elif test_case_id == 2:
x = torch.rand(6, 6)
re... | 938 | 6 | 3Pytorch | 2 | 1Origin | 6 |
Problem:
I'm trying to convert a torch tensor to pandas DataFrame.
However, the numbers in the data is still tensors, what I actually want is numerical values.
This is my code
import torch
import pandas as pd
x = torch.rand(4,4)
px = pd.DataFrame(x)
And px looks like
0 1 2 3
tensor(0.3880) tensor(0.4598) ten... | px = pd.DataFrame(x.numpy()) | import numpy as np
import pandas as pd
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.rand(4, 4)
elif test_case_id == 2:
x = torch.rand(6, 6)
re... | 939 | 7 | 3Pytorch | 2 | 3Surface | 6 |
Problem:
I'd like to convert a torch tensor to pandas dataframe but by using pd.DataFrame I'm getting a dataframe filled with tensors instead of numeric values.
import torch
import pandas as pd
x = torch.rand(6,6)
px = pd.DataFrame(x)
Here's what I get when clicking on px in the variable explorer:
... | px = pd.DataFrame(x.numpy()) | import numpy as np
import pandas as pd
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.rand(4, 4)
elif test_case_id == 2:
x = torch.rand(6, 6)
re... | 940 | 8 | 3Pytorch | 2 | 3Surface | 6 |
Problem:
I'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 1 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error
TypeError: indexing a tenso... | C = B[:, A_log.bool()] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_log = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_log = torch.BoolTensor([True, Fal... | 941 | 9 | 3Pytorch | 3 | 1Origin | 9 |
Problem:
I want to use a logical index to slice a torch tensor. Which means, I want to select the columns that get a '1' in the logical index.
I tried but got some errors:
TypeError: indexing a tensor with an object of type ByteTensor. The only supported types are integers, slices, numpy scalars and torch.LongTensor o... | C = B[:, A_logical.bool()] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_logical = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_logical = torch.BoolTensor([T... | 942 | 10 | 3Pytorch | 3 | 3Surface | 9 |
Problem:
I'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 1 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error
TypeError: indexing a tenso... | C = B[:, A_log.bool()] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_log = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_log = torch.BoolTensor([True, Fal... | 943 | 11 | 3Pytorch | 3 | 3Surface | 9 |
Problem:
I'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 0 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error
TypeError: indexing a tenso... | for i in range(len(A_log)):
if A_log[i] == 1:
A_log[i] = 0
else:
A_log[i] = 1
C = B[:, A_log.bool()] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_log = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_log = torch.BoolTensor([True, Fal... | 944 | 12 | 3Pytorch | 3 | 2Semantic | 9 |
Problem:
I'm trying to slice a PyTorch tensor using a logical index on the columns. I want the columns that correspond to a 1 value in the index vector. Both slicing and logical indexing are possible, but are they possible together? If so, how? My attempt keeps throwing the unhelpful error
TypeError: indexing a tenso... | # def solve(A_log, B):
### BEGIN SOLUTION
C = B[:, A_log.bool()]
### END SOLUTION
# return C
return C
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_log = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_log = torch.BoolTensor([True, Fal... | 945 | 13 | 3Pytorch | 3 | 3Surface | 9 |
Problem:
I want to use a logical index to slice a torch tensor. Which means, I want to select the columns that get a '0' in the logical index.
I tried but got some errors:
TypeError: indexing a tensor with an object of type ByteTensor. The only supported types are integers, slices, numpy scalars and torch.LongTensor o... | for i in range(len(A_log)):
if A_log[i] == 1:
A_log[i] = 0
else:
A_log[i] = 1
C = B[:, A_log.bool()] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
A_log = torch.LongTensor([0, 1, 0])
B = torch.LongTensor([[1, 2, 3], [4, 5, 6]])
elif test_case_id == 2:
A_log = torch.BoolTensor([True, Fal... | 946 | 14 | 3Pytorch | 3 | 0Difficult-Rewrite | 9 |
Problem:
I'm trying to slice a PyTorch tensor using an index on the columns. The index, contains a list of columns that I want to select in order. You can see the example later.
I know that there is a function index_select. Now if I have the index, which is a LongTensor, how can I apply index_select to get the expecte... | C = B.index_select(1, idx) | import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
idx = torch.LongTensor([1, 2])
B = torch.LongTensor([[2, 1, 3], [5, 4, 6]])
elif test_case_id == 2:
idx = torch.LongTens... | 947 | 15 | 3Pytorch | 2 | 0Difficult-Rewrite | 9 |
Problem:
How to convert a numpy array of dtype=object to torch Tensor?
array([
array([0.5, 1.0, 2.0], dtype=float16),
array([4.0, 6.0, 8.0], dtype=float16)
], dtype=object)
A:
<code>
import pandas as pd
import torch
import numpy as np
x_array = load_data()
</code>
x_tensor = ... # put solution in this variab... | x_tensor = torch.from_numpy(x_array.astype(float)) | import numpy as np
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
x = np.array(
[
np.array([0.5, 1.0, 2.0], dtype=np.float16),
np.array([4.0, 6.0, 8.0], dtype=np.f... | 948 | 16 | 3Pytorch | 2 | 1Origin | 16 |
Problem:
How to convert a numpy array of dtype=object to torch Tensor?
x = np.array([
np.array([1.23, 4.56, 9.78, 1.23, 4.56, 9.78], dtype=np.double),
np.array([4.0, 4.56, 9.78, 1.23, 4.56, 77.77], dtype=np.double),
np.array([1.23, 4.56, 9.78, 1.23, 4.56, 9.78], dtype=np.double),
np.array([4.0, 4.56, ... | x_tensor = torch.from_numpy(x_array.astype(float)) | import numpy as np
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
x = np.array(
[
np.array([0.5, 1.0, 2.0], dtype=np.float16),
np.array([4.0, 6.0, 8.0], dtype=np.f... | 949 | 17 | 3Pytorch | 2 | 3Surface | 16 |
Problem:
How to convert a numpy array of dtype=object to torch Tensor?
array([
array([0.5, 1.0, 2.0], dtype=float16),
array([4.0, 6.0, 8.0], dtype=float16)
], dtype=object)
A:
<code>
import pandas as pd
import torch
import numpy as np
x_array = load_data()
def Convert(a):
# return the solution in this fu... | # def Convert(a):
### BEGIN SOLUTION
t = torch.from_numpy(a.astype(float))
### END SOLUTION
# return t
# x_tensor = Convert(x_array)
return t
| import numpy as np
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
x = np.array(
[
np.array([0.5, 1.0, 2.0], dtype=np.float16),
np.array([4.0, 6.0, 8.0], dtype=np.f... | 950 | 18 | 3Pytorch | 2 | 3Surface | 16 |
Problem:
How to batch convert sentence lengths to masks in PyTorch?
For example, from
lens = [3, 5, 4]
we want to get
mask = [[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 0]]
Both of which are torch.LongTensors.
A:
<code>
import numpy as np
import pandas as pd
import torch
lens = load_data()
</c... | max_len = max(lens)
mask = torch.arange(max_len).expand(len(lens), max_len) < lens.unsqueeze(1)
mask = mask.type(torch.LongTensor) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
lens = torch.LongTensor([3, 5, 4])
elif test_case_id == 2:
lens = torch.LongTensor([3, 2, 4, 6, 5])
return lens
def generate_ans(data):
... | 951 | 19 | 3Pytorch | 2 | 1Origin | 19 |
Problem:
How to batch convert sentence lengths to masks in PyTorch?
For example, from
lens = [1, 9, 3, 5]
we want to get
mask = [[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0, 0, 0]]
Both of which are torch.LongTensors.
A:
<code... | max_len = max(lens)
mask = torch.arange(max_len).expand(len(lens), max_len) < lens.unsqueeze(1)
mask = mask.type(torch.LongTensor) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
lens = torch.LongTensor([3, 5, 4])
elif test_case_id == 2:
lens = torch.LongTensor([3, 2, 4, 6, 5])
return lens
def generate_ans(data):
... | 952 | 20 | 3Pytorch | 2 | 3Surface | 19 |
Problem:
How to batch convert sentence lengths to masks in PyTorch?
For example, from
lens = [3, 5, 4]
we want to get
mask = [[0, 0, 1, 1, 1],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 1]]
Both of which are torch.LongTensors.
A:
<code>
import numpy as np
import pandas as pd
import torch
lens = load_data()
</c... | max_len = max(lens)
mask = torch.arange(max_len).expand(len(lens), max_len) > (max_len - lens.unsqueeze(1) - 1)
mask = mask.type(torch.LongTensor) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
lens = torch.LongTensor([3, 5, 4])
elif test_case_id == 2:
lens = torch.LongTensor([3, 2, 4, 6, 5])
return lens
def generate_ans(data):
... | 953 | 21 | 3Pytorch | 2 | 2Semantic | 19 |
Problem:
How to batch convert sentence lengths to masks in PyTorch?
For example, from
lens = [3, 5, 4]
we want to get
mask = [[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 0]]
Both of which are torch.LongTensors.
A:
<code>
import numpy as np
import pandas as pd
import torch
lens = load_data()
def... | # def get_mask(lens):
### BEGIN SOLUTION
max_len = max(lens)
mask = torch.arange(max_len).expand(len(lens), max_len) < lens.unsqueeze(1)
mask = mask.type(torch.LongTensor)
### END SOLUTION
# return mask
# mask = get_mask(lens)
return mask
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
lens = torch.LongTensor([3, 5, 4])
elif test_case_id == 2:
lens = torch.LongTensor([3, 2, 4, 6, 5])
return lens
def generate_ans(data):
... | 954 | 22 | 3Pytorch | 2 | 3Surface | 19 |
Problem:
Consider I have 2D Tensor, index_in_batch * diag_ele. How can I get a 3D Tensor index_in_batch * Matrix (who is a diagonal matrix, construct by drag_ele)?
The torch.diag() construct diagonal matrix only when input is 1D, and return diagonal element when input is 2D.
A:
<code>
import numpy as np
import pan... | Tensor_3D = torch.diag_embed(Tensor_2D) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
a = torch.rand(2, 3)
elif test_case_id == 2:
a = torch.rand(4, 5)
return a
def generate_ans(data):
... | 955 | 23 | 3Pytorch | 2 | 1Origin | 23 |
Problem:
Consider I have 2D Tensor, index_in_batch * diag_ele. How can I get a 3D Tensor index_in_batch * Matrix (who is a diagonal matrix, construct by drag_ele)?
The torch.diag() construct diagonal matrix only when input is 1D, and return diagonal element when input is 2D.
A:
<code>
import numpy as np
import pan... | # def Convert(t):
### BEGIN SOLUTION
result = torch.diag_embed(t)
### END SOLUTION
# return result
# Tensor_3D = Convert(Tensor_2D)
return result
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
a = torch.rand(2, 3)
elif test_case_id == 2:
a = torch.rand(4, 5)
return a
def generate_ans(data):
... | 956 | 24 | 3Pytorch | 2 | 3Surface | 23 |
Problem:
In pytorch, given the tensors a of shape (1X11) and b of shape (1X11), torch.stack((a,b),0) would give me a tensor of shape (2X11)
However, when a is of shape (2X11) and b is of shape (1X11), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".
Because the two tensor ... | ab = torch.cat((a, b), 0) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.randn(2, 11)
b = torch.randn(1, 11)
elif test_case_id == 2:
torch.random.manual_seed(7)
... | 957 | 25 | 3Pytorch | 2 | 1Origin | 25 |
Problem:
In pytorch, given the tensors a of shape (114X514) and b of shape (114X514), torch.stack((a,b),0) would give me a tensor of shape (228X514)
However, when a is of shape (114X514) and b is of shape (24X514), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".
Because t... | ab = torch.cat((a, b), 0) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.randn(2, 11)
b = torch.randn(1, 11)
elif test_case_id == 2:
torch.random.manual_seed(7)
... | 958 | 26 | 3Pytorch | 2 | 3Surface | 25 |
Problem:
In pytorch, given the tensors a of shape (1X11) and b of shape (1X11), torch.stack((a,b),0) would give me a tensor of shape (2X11)
However, when a is of shape (2X11) and b is of shape (1X11), torch.stack((a,b),0) will raise an error cf. "the two tensor size must exactly be the same".
Because the two tensor ... | # def solve(a, b):
### BEGIN SOLUTION
ab = torch.cat((a, b), 0)
### END SOLUTION
# return ab
# ab = solve(a, b)
return ab
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.randn(2, 11)
b = torch.randn(1, 11)
elif test_case_id == 2:
torch.random.manual_seed(7)
... | 959 | 27 | 3Pytorch | 2 | 3Surface | 25 |
Problem:
Given a 3d tenzor, say: batch x sentence length x embedding dim
a = torch.rand((10, 1000, 96))
and an array(or tensor) of actual lengths for each sentence
lengths = torch .randint(1000,(10,))
outputs tensor([ 370., 502., 652., 859., 545., 964., 566., 576.,1000., 803.])
How to fill tensor ‘a’ with zeros af... | for i_batch in range(10):
a[i_batch, lengths[i_batch]:, :] = 0 | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.rand((10, 1000, 96))
lengths = torch.randint(1000, (10,))
return a, lengths
def generate_ans(dat... | 960 | 28 | 3Pytorch | 1 | 1Origin | 28 |
Problem:
Given a 3d tenzor, say: batch x sentence length x embedding dim
a = torch.rand((10, 1000, 96))
and an array(or tensor) of actual lengths for each sentence
lengths = torch .randint(1000,(10,))
outputs tensor([ 370., 502., 652., 859., 545., 964., 566., 576.,1000., 803.])
How to fill tensor ‘a’ with 2333 aft... | for i_batch in range(10):
a[i_batch, lengths[i_batch]:, :] = 2333 | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.rand((10, 1000, 96))
lengths = torch.randint(1000, (10,))
return a, lengths
def generate_ans(dat... | 961 | 29 | 3Pytorch | 1 | 3Surface | 28 |
Problem:
Given a 3d tenzor, say: batch x sentence length x embedding dim
a = torch.rand((10, 1000, 23))
and an array(or tensor) of actual lengths for each sentence
lengths = torch .randint(1000,(10,))
outputs tensor([ 137., 152., 165., 159., 145., 264., 265., 276.,1000., 203.])
How to fill tensor ‘a’ with 0 before... | for i_batch in range(10):
a[i_batch, :lengths[i_batch], :] = 0 | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.rand((10, 1000, 23))
lengths = torch.randint(1000, (10,))
return a, lengths
def generate_ans(dat... | 962 | 30 | 3Pytorch | 1 | 2Semantic | 28 |
Problem:
Given a 3d tenzor, say: batch x sentence length x embedding dim
a = torch.rand((10, 1000, 23))
and an array(or tensor) of actual lengths for each sentence
lengths = torch .randint(1000,(10,))
outputs tensor([ 137., 152., 165., 159., 145., 264., 265., 276.,1000., 203.])
How to fill tensor ‘a’ with 2333 bef... | for i_batch in range(10):
a[i_batch, :lengths[i_batch], :] = 2333 | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.rand((10, 1000, 23))
lengths = torch.randint(1000, (10,))
return a, lengths
def generate_ans(dat... | 963 | 31 | 3Pytorch | 1 | 0Difficult-Rewrite | 28 |
Problem:
I have this code:
import torch
list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:
ValueError: only one element tensors can be converted to Python scalars
How can I convert the list of tensors to a tensor of tensors ... | tensor_of_tensors = torch.stack((list_of_tensors)) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
return list_of_tensors
def generate_ans(data):
... | 964 | 32 | 3Pytorch | 1 | 1Origin | 32 |
Problem:
How to convert a list of tensors to a tensor of tensors?
I have tried torch.tensor() but it gave me this error message
ValueError: only one element tensors can be converted to Python scalars
my current code is here:
import torch
list = [ torch.randn(3), torch.randn(3), torch.randn(3)]
new_tensors = torch.te... | new_tensors = torch.stack((list)) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
list = [torch.randn(3), torch.randn(3), torch.randn(3)]
return list
def generate_ans(data):
list = data
ne... | 965 | 33 | 3Pytorch | 1 | 3Surface | 32 |
Problem:
I have this code:
import torch
list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:
ValueError: only one element tensors can be converted to Python scalars
How can I convert the list of tensors to a tensor of tensors ... | # def Convert(lt):
### BEGIN SOLUTION
tt = torch.stack((lt))
### END SOLUTION
# return tt
# tensor_of_tensors = Convert(list_of_tensors)
return tt
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
return list_of_tensors
def generate_ans(data):
... | 966 | 34 | 3Pytorch | 1 | 3Surface | 32 |
Problem:
I have this code:
import torch
list_of_tensors = [ torch.randn(3), torch.randn(3), torch.randn(3)]
tensor_of_tensors = torch.tensor(list_of_tensors)
I am getting the error:
ValueError: only one element tensors can be converted to Python scalars
How can I convert the list of tensors to a tensor of tensors ... | tensor_of_tensors = torch.stack((list_of_tensors)) | import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
list_of_tensors = [torch.randn(3), torch.randn(3), torch.randn(3)]
return list_of_tensors
def generate... | 967 | 35 | 3Pytorch | 1 | 0Difficult-Rewrite | 32 |
Problem:
I have the following torch tensor:
tensor([[-0.2, 0.3],
[-0.5, 0.1],
[-0.4, 0.2]])
and the following numpy array: (I can convert it to something else if necessary)
[1 0 1]
I want to get the following tensor:
tensor([0.3, -0.5, 0.2])
i.e. I want the numpy array to index each sub-element of my ten... | idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
idx = np.array([1, 0, 1], dtype=np.int32)
elif test_cas... | 968 | 36 | 3Pytorch | 2 | 1Origin | 36 |
Problem:
I have the following torch tensor:
tensor([[-22.2, 33.3],
[-55.5, 11.1],
[-44.4, 22.2]])
and the following numpy array: (I can convert it to something else if necessary)
[1 1 0]
I want to get the following tensor:
tensor([33.3, 11.1, -44.4])
i.e. I want the numpy array to index each sub-element ... | idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
idx = np.array([1, 0, 1], dtype=np.int32)
elif test_cas... | 969 | 37 | 3Pytorch | 3 | 3Surface | 36 |
Problem:
I have the following torch tensor:
tensor([[-0.2, 0.3],
[-0.5, 0.1],
[-0.4, 0.2]])
and the following numpy array: (I can convert it to something else if necessary)
[1 0 1]
I want to get the following tensor:
tensor([-0.2, 0.1, -0.4])
i.e. I want the numpy array to index each sub-element of my te... | idx = 1 - idx
idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.tensor([[-0.2, 0.3], [-0.5, 0.1], [-0.4, 0.2]])
idx = np.array([1, 0, 1], dtype=np.int32)
elif test_cas... | 970 | 38 | 3Pytorch | 2 | 2Semantic | 36 |
Problem:
I have the tensors:
ids: shape (70,1) containing indices like [[1],[0],[2],...]
x: shape(70,3,2)
ids tensor encodes the index of bold marked dimension of x which should be selected. I want to gather the selected slices in a resulting vector:
result: shape (70,2)
Background:
I have some scores (shape = (... | idx = ids.repeat(1, 2).view(70, 1, 2)
result = torch.gather(x, 1, idx)
result = result.squeeze(1) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.arange(70 * 3 * 2).view(70, 3, 2)
ids = torch.randint(0, 3, size=(70, 1))
return ids, x
def generate... | 971 | 39 | 3Pytorch | 1 | 1Origin | 39 |
Problem:
I have the tensors:
ids: shape (30,1) containing indices like [[2],[1],[0],...]
x: shape(30,3,114)
ids tensor encodes the index of bold marked dimension of x which should be selected. I want to gather the selected slices in a resulting vector:
result: shape (30,114)
Background:
I have some scores (shape... | idx = ids.repeat(1, 114).view(30, 1, 114)
result = torch.gather(x, 1, idx)
result = result.squeeze(1) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.arange(30 * 3 * 114).view(30, 3, 114)
ids = torch.randint(0, 3, size=(30, 1))
return ids, x
def gene... | 972 | 40 | 3Pytorch | 1 | 3Surface | 39 |
Problem:
I have the tensors:
ids: shape (70,3) containing indices like [[0,1,0],[1,0,0],[0,0,1],...]
x: shape(70,3,2)
ids tensor encodes the index of bold marked dimension of x which should be selected (1 means selected, 0 not). I want to gather the selected slices in a resulting vector:
result: shape (70,2)
Back... | ids = torch.argmax(ids, 1, True)
idx = ids.repeat(1, 2).view(70, 1, 2)
result = torch.gather(x, 1, idx)
result = result.squeeze(1) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
torch.random.manual_seed(42)
if test_case_id == 1:
x = torch.arange(70 * 3 * 2).view(70, 3, 2)
select_ids = torch.randint(0, 3, size=(70, 1))
ids = torch.zeros(si... | 973 | 41 | 3Pytorch | 1 | 2Semantic | 39 |
Problem:
I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.
I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1... | y = torch.argmax(softmax_output, dim=1).view(-1, 1)
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
softmax_output = torch.FloatTensor(
[[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
)
elif test_case_id == 2:
softmax_ou... | 974 | 42 | 3Pytorch | 2 | 1Origin | 42 |
Problem:
I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.
I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1... | y = torch.argmax(softmax_output, dim=1).view(-1, 1)
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
softmax_output = torch.FloatTensor(
[[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
)
elif test_case_id == 2:
softmax_ou... | 975 | 43 | 3Pytorch | 2 | 3Surface | 42 |
Problem:
I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.
I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1... | y = torch.argmin(softmax_output, dim=1).view(-1, 1)
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
softmax_output = torch.FloatTensor(
[[0.2, 0.1, 0.7], [0.6, 0.1, 0.3], [0.4, 0.5, 0.1]]
)
elif test_case_id == 2:
softmax_ou... | 976 | 44 | 3Pytorch | 2 | 2Semantic | 42 |
Problem:
I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.
I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1... | # def solve(softmax_output):
y = torch.argmax(softmax_output, dim=1).view(-1, 1)
# return y
# y = solve(softmax_output)
return y
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
softmax_output = torch.FloatTensor(
[[0.2, 0.1, 0.7], [0.6, 0.2, 0.2], [0.1, 0.8, 0.1]]
)
elif test_case_id == 2:
softmax_ou... | 977 | 45 | 3Pytorch | 2 | 3Surface | 42 |
Problem:
I have a logistic regression model using Pytorch, where my input is high-dimensional and my output must be a scalar - 0, 1 or 2.
I'm using a linear layer combined with a softmax layer to return a n x 3 tensor, where each column represents the probability of the input falling in one of the three classes (0, 1... | # def solve(softmax_output):
### BEGIN SOLUTION
y = torch.argmin(softmax_output, dim=1).detach()
### END SOLUTION
# return y
# y = solve(softmax_output)
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
softmax_output = torch.FloatTensor(
[[0.2, 0.1, 0.7], [0.6, 0.1, 0.3], [0.4, 0.5, 0.1]]
)
elif test_case_id == 2:
softmax_ou... | 978 | 46 | 3Pytorch | 2 | 0Difficult-Rewrite | 42 |
Problem:
I am doing an image segmentation task. There are 7 classes in total so the final outout is a tensor like [batch, 7, height, width] which is a softmax output. Now intuitively I wanted to use CrossEntropy loss but the pytorch implementation doesn't work on channel wise one-hot encoded vector
So I was planning ... | loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(images, labels) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
images = torch.randn(5, 3, 4, 4)
labels = torch.LongTensor(5, 4, 4).random_(3)
return images, labels
def g... | 979 | 47 | 3Pytorch | 1 | 1Origin | 47 |
Problem:
I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cn... | cnt_equal = int((A == B).sum()) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (1000,))
torch.random.manual_seed(7)
B = torch.... | 980 | 48 | 3Pytorch | 1 | 1Origin | 48 |
Problem:
I have two tensors of dimension 11 * 1. I want to check how many of the 11 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
cnt_eq... | cnt_equal = int((A == B).sum()) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (11,))
torch.random.manual_seed(7)
B = torch.ra... | 981 | 49 | 3Pytorch | 1 | 3Surface | 48 |
Problem:
I have two tensors of dimension like 1000 * 1. I want to check how many of the elements are not equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code... | cnt_not_equal = int(len(A)) - int((A == B).sum()) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (10,))
torch.random.manual_seed(7)
B = torch.ra... | 982 | 50 | 3Pytorch | 1 | 2Semantic | 48 |
Problem:
I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
def Count(... | # def Count(A, B):
### BEGIN SOLUTION
cnt_equal = int((A == B).sum())
### END SOLUTION
# return cnt_equal
# cnt_equal = Count(A, B)
return cnt_equal
| import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (1000,))
torch.random.manual_seed(7)
B = torch.... | 983 | 51 | 3Pytorch | 1 | 3Surface | 48 |
Problem:
I have two tensors of dimension (2*x, 1). I want to check how many of the last x elements are equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</code>
... | cnt_equal = int((A[int(len(A) / 2):] == B[int(len(A) / 2):]).sum()) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (100,))
torch.random.manual_seed(7)
B = torch.r... | 984 | 52 | 3Pytorch | 1 | 0Difficult-Rewrite | 48 |
Problem:
I have two tensors of dimension (2*x, 1). I want to check how many of the last x elements are not equal in the two tensors. I think I should be able to do this in few lines like Numpy but couldn't find a similar function.
A:
<code>
import numpy as np
import pandas as pd
import torch
A, B = load_data()
</co... | cnt_not_equal = int((A[int(len(A) / 2):] != B[int(len(A) / 2):]).sum()) | import numpy as np
import torch
import copy
import tokenize, io
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
A = torch.randint(2, (1000,))
torch.random.manual_seed(7)
B = torch.... | 985 | 53 | 3Pytorch | 1 | 0Difficult-Rewrite | 48 |
Problem:
Let's say I have a 5D tensor which has this shape for example : (1, 3, 10, 40, 1). I want to split it into smaller equal tensors (if possible) according to a certain dimension with a step equal to 1 while preserving the other dimensions.
Let's say for example I want to split it according to the fourth dimens... | Temp = a.unfold(3, chunk_dim, 1)
tensors_31 = []
for i in range(Temp.shape[3]):
tensors_31.append(Temp[:, :, :, i, :].view(1, 3, 10, chunk_dim, 1).numpy())
tensors_31 = torch.from_numpy(np.array(tensors_31)) | import numpy as np
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.randn(1, 3, 10, 40, 1)
return a
def generate_ans(data):
a = data
Temp = a... | 986 | 54 | 3Pytorch | 1 | 1Origin | 54 |
Problem:
Let's say I have a 5D tensor which has this shape for example : (1, 3, 40, 10, 1). I want to split it into smaller equal tensors (if possible) according to a certain dimension with a step equal to 1 while preserving the other dimensions.
Let's say for example I want to split it according to the third dimensi... | Temp = a.unfold(2, chunk_dim, 1)
tensors_31 = []
for i in range(Temp.shape[2]):
tensors_31.append(Temp[:, :, i, :, :].view(1, 3, chunk_dim, 10, 1).numpy())
tensors_31 = torch.from_numpy(np.array(tensors_31)) | import numpy as np
import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
a = torch.randn(1, 3, 40, 10, 1)
return a
def generate_ans(data):
a = data
Temp = a... | 987 | 55 | 3Pytorch | 1 | 2Semantic | 54 |
Problem:
This question may not be clear, so please ask for clarification in the comments and I will expand.
I have the following tensors of the following shape:
mask.size() == torch.Size([1, 400])
clean_input_spectrogram.size() == torch.Size([1, 400, 161])
output.size() == torch.Size([1, 400, 161])
mask is comprised... | output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[:, mask[0].to(torch.bool), :] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
mask = torch.tensor([[0, 1, 0]]).to(torch.int32)
clean_input_spectrogram = torch.rand((1, 3, 2))
output = t... | 988 | 56 | 3Pytorch | 1 | 1Origin | 56 |
Problem:
This question may not be clear, so please ask for clarification in the comments and I will expand.
I have the following tensors of the following shape:
mask.size() == torch.Size([1, 400])
clean_input_spectrogram.size() == torch.Size([1, 400, 161])
output.size() == torch.Size([1, 400, 161])
mask is comprised... | for i in range(len(mask[0])):
if mask[0][i] == 1:
mask[0][i] = 0
else:
mask[0][i] = 1
output[:, mask[0].to(torch.bool), :] = clean_input_spectrogram[:, mask[0].to(torch.bool), :] | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
mask = torch.tensor([[0, 1, 0]]).to(torch.int32)
clean_input_spectrogram = torch.rand((1, 3, 2))
output = t... | 989 | 57 | 3Pytorch | 1 | 2Semantic | 56 |
Problem:
I may be missing something obvious, but I can't find a way to compute this.
Given two tensors, I want to keep elements with the minimum absolute values, in each one of them as well as the sign.
I thought about
sign_x = torch.sign(x)
sign_y = torch.sign(y)
min = torch.min(torch.abs(x), torch.abs(y))
in orde... | mins = torch.min(torch.abs(x), torch.abs(y))
xSigns = (mins == torch.abs(x)) * torch.sign(x)
ySigns = (mins == torch.abs(y)) * torch.sign(y)
finalSigns = xSigns.int() | ySigns.int()
signed_min = mins * finalSigns | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
x = torch.randint(-10, 10, (5,))
y = torch.randint(-20, 20, (5,))
return x, y
def generate_ans(data):
... | 990 | 58 | 3Pytorch | 1 | 1Origin | 58 |
Problem:
I may be missing something obvious, but I can't find a way to compute this.
Given two tensors, I want to keep elements with the maximum absolute values, in each one of them as well as the sign.
I thought about
sign_x = torch.sign(x)
sign_y = torch.sign(y)
max = torch.max(torch.abs(x), torch.abs(y))
in orde... | maxs = torch.max(torch.abs(x), torch.abs(y))
xSigns = (maxs == torch.abs(x)) * torch.sign(x)
ySigns = (maxs == torch.abs(y)) * torch.sign(y)
finalSigns = xSigns.int() | ySigns.int()
signed_max = maxs * finalSigns | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
x = torch.randint(-10, 10, (5,))
y = torch.randint(-20, 20, (5,))
return x, y
def generate_ans(data):
... | 991 | 59 | 3Pytorch | 1 | 2Semantic | 58 |
Problem:
I may be missing something obvious, but I can't find a way to compute this.
Given two tensors, I want to keep elements with the minimum absolute values, in each one of them as well as the sign.
I thought about
sign_x = torch.sign(x)
sign_y = torch.sign(y)
min = torch.min(torch.abs(x), torch.abs(y))
in orde... | # def solve(x, y):
### BEGIN SOLUTION
mins = torch.min(torch.abs(x), torch.abs(y))
xSigns = (mins == torch.abs(x)) * torch.sign(x)
ySigns = (mins == torch.abs(y)) * torch.sign(y)
finalSigns = xSigns.int() | ySigns.int()
signed_min = mins * finalSigns
### END SOLUTION
# return signed_mi... | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
x = torch.randint(-10, 10, (5,))
y = torch.randint(-20, 20, (5,))
return x, y
def generate_ans(data):
... | 992 | 60 | 3Pytorch | 1 | 3Surface | 58 |
Problem:
I have a trained PyTorch model and I want to get the confidence score of predictions in range (0-1). The code below is giving me a score but its range is undefined. I want the score in a defined range of (0-1) using softmax. Any idea how to get this?
conf, classes = torch.max(output.reshape(1, 3), 1)
My code... | '''
training part
'''
# X, Y = load_iris(return_X_y=True)
# lossFunc = torch.nn.CrossEntropyLoss()
# opt = torch.optim.Adam(MyNet.parameters(), lr=0.001)
# for batch in range(0, 50):
# for i in range(len(X)):
# x = MyNet(torch.from_numpy(X[i]).float()).reshape(1, 3)
# y = torch.tensor(Y[i]).long().u... | import torch
import copy
import sklearn
from sklearn.datasets import load_iris
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
X, y = load_iris(return_X_y=True)
input = torch.from_numpy(X[42]).float()
torch.manual_seed(4... | 993 | 61 | 3Pytorch | 1 | 1Origin | 61 |
Problem:
I have two tensors that should together overlap each other to form a larger tensor. To illustrate:
a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
a = [[1 2 3] b = [[5 6 7]
[1 2 3]] [5 6 7]]
I want to combine the two tensors and have them partially overlap by... | c = (a[:, -1:] + b[:, :1]) / 2
result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
elif test_case_id == 2:
a = torch.Tensor([[3, 2, 1], [1, 2... | 994 | 62 | 3Pytorch | 3 | 1Origin | 62 |
Problem:
I have two tensors that should together overlap each other to form a larger tensor. To illustrate:
a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
a = [[1 2 3] b = [[5 6 7]
[1 2 3]] [5 6 7]]
I want to combine the two tensors and have them partially overlap by... | # def solve(a, b):
### BEGIN SOLUTION
c = (a[:, -1:] + b[:, :1]) / 2
result = torch.cat((a[:, :-1], c, b[:, 1:]), dim=1)
### END SOLUTION
# return result
# result = solve(a, b)
return result
| import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
a = torch.Tensor([[1, 2, 3], [1, 2, 3]])
b = torch.Tensor([[5, 6, 7], [5, 6, 7]])
elif test_case_id == 2:
a = torch.Tensor([[3, 2, 1], [1, 2... | 995 | 63 | 3Pytorch | 3 | 3Surface | 62 |
Problem:
I have a tensor t, for example
1 2
3 4
5 6
7 8
And I would like to make it
0 0 0 0
0 1 2 0
0 3 4 0
0 5 6 0
0 7 8 0
0 0 0 0
I tried stacking with new=torch.tensor([0. 0. 0. 0.]) tensor four times but that did not work.
t = torch.arange(8).reshape(1,4,2).float()
print(t)
new=torch.tensor([[0., 0., 0.,0.]])
p... | result = torch.nn.functional.pad(t, (1, 1, 1, 1)) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
elif test_case_id == 2:
t = torch.LongTensor(
[[5, 6, 7], [2, 3, 4], [1, 2, 3], [... | 996 | 64 | 3Pytorch | 2 | 1Origin | 64 |
Problem:
I have a tensor t, for example
1 2
3 4
And I would like to make it
0 0 0 0
0 1 2 0
0 3 4 0
0 0 0 0
I tried stacking with new=torch.tensor([0. 0. 0. 0.]) tensor four times but that did not work.
t = torch.arange(4).reshape(1,2,2).float()
print(t)
new=torch.tensor([[0., 0., 0.,0.]])
print(new)
r = torch.stac... | result = torch.nn.functional.pad(t, (1, 1, 1, 1)) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
elif test_case_id == 2:
t = torch.LongTensor(
[[5, 6, 7], [2, 3, 4], [1, 2, 3], [... | 997 | 65 | 3Pytorch | 2 | 3Surface | 64 |
Problem:
I have a tensor t, for example
1 2
3 4
5 6
7 8
And I would like to make it
-1 -1 -1 -1
-1 1 2 -1
-1 3 4 -1
-1 5 6 -1
-1 7 8 -1
-1 -1 -1 -1
I tried stacking with new=torch.tensor([-1, -1, -1, -1,]) tensor four times but that did not work.
t = torch.arange(8).reshape(1,4,2).float()
print(t)
new=torch.tensor(... | result = torch.ones((t.shape[0] + 2, t.shape[1] + 2)) * -1
result[1:-1, 1:-1] = t | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
t = torch.LongTensor([[1, 2], [3, 4], [5, 6], [7, 8]])
elif test_case_id == 2:
t = torch.LongTensor(
[[5, 6, 7], [2, 3, 4], [1, 2, 3], [... | 998 | 66 | 3Pytorch | 2 | 2Semantic | 64 |
Problem:
I have batch data and want to dot() to the data. W is trainable parameters. How to dot between batch data and weights?
Here is my code below, how to fix it?
hid_dim = 32
data = torch.randn(10, 2, 3, hid_dim)
data = data.view(10, 2*3, hid_dim)
W = torch.randn(hid_dim) # assume trainable parameters via nn.Para... | W = W.unsqueeze(0).unsqueeze(0).expand(*data.size())
result = torch.sum(data * W, 2)
result = result.view(10, 2, 3) | import torch
import copy
def generate_test_case(test_case_id):
def define_test_input(test_case_id):
if test_case_id == 1:
torch.random.manual_seed(42)
hid_dim = 32
data = torch.randn(10, 2, 3, hid_dim)
data = data.view(10, 2 * 3, hid_dim)
W = tor... | 999 | 67 | 3Pytorch | 1 | 1Origin | 67 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.