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Non absolute counts histogram for imbalanced groups


Non absolute counts histogram for imbalanced groups

By : A Tank Called Frank
Date : November 19 2020, 03:01 PM
around this issue Currently, I can create the plot: , The solution is to combine both methods:
code :
scale_y_continuous(labels=percent) +


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Histogram of column counts and row counts with NA data

Histogram of column counts and row counts with NA data


By : Mozart Tan
Date : March 29 2020, 07:55 AM
seems to work fine You can get the counts of non-missing values in the columns (minus the first) and rows this way:
code :
# Toy data to test
df <- data.frame(X1 = c(1, 1, NA, 3, NA), X2 = c(3, 4, NA, 1, 5), X3 = c(3, 4, 6, 1, 8))

# Now generate vectors of the counts
column.counts <- colSums(!is.na(df[,2:ncol(df)]))
row.counts <- rowSums(!is.na(df))
Create variable based on counts of groups and sub groups in data table

Create variable based on counts of groups and sub groups in data table


By : vivekananthan krishn
Date : March 29 2020, 07:55 AM
Any of those help I have many student records. I need to create two new variable. One should display the count of Unitcode (ie enrolments) for each student_ID for each Year. , A similar option using dplyr would be
code :
library(dplyr)
record %>%
     group_by(student_ID, Year) %>%
     summarise(unitcodes=n(), fails=sum(Grade=='Fail'))
Compute the difference of observation counts between groups depending on the values the groups contain

Compute the difference of observation counts between groups depending on the values the groups contain


By : David Högberg
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I have the following two data frames in mylist. For each data frame, I would like to compute the difference between the number of observations of the group (identified by "type") that contains the maxiumum value ("value") and the number of observations of the other group. , One trick here is to see that your calculation can be simplified:
code :
[number in group] - [number not in group]
= [number in group] - ([number of rows] - [number in group])
= [number in group] - [number of rows] + [number in group]
= 2 * [number in group] - [number of rows]
lapply(mylist, function(x) {2*sum(x$type==x$type[which.max(x$value)])-nrow(x)})
[[1]]
[1] -3

[[2]]
[1] 3
How to resample text (imbalanced groups) in a pipeline?

How to resample text (imbalanced groups) in a pipeline?


By : Roland
Date : March 29 2020, 07:55 AM
around this issue You should use the Pipeline implemented in the imblearn package, not the one from sklearn. E.g., this code runs fine:
code :
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB

from imblearn.over_sampling import RandomOverSampler
from imblearn.pipeline import Pipeline


data = [['round red fruit that is sweet','apple'],['long yellow fruit with a peel','banana'],
    ['round green fruit that is soft and sweet','pear'], ['red fruit that is common', 'apple'],
    ['tiny fruits that grow in bunches','grapes'],['purple fruits', 'grapes'], ['yellow and long', 'banana'],
    ['round, small, green', 'grapes'], ['can be red, green, or purple', 'grapes'], ['tiny fruits', 'grapes'],
    ['small fruits', 'grapes']]

df = pd.DataFrame(data, columns=['Description','Type'])

X_train, X_test, y_train, y_test = train_test_split(df['Description'],
    df['Type'], random_state=0)

text_clf = Pipeline([('vect', CountVectorizer()),
                    ('tfidf', TfidfTransformer()),
                    ('RUS', RandomOverSampler()),
                    ('clf', MultinomialNB())])
text_clf = text_clf.fit(X_train, y_train)
y_pred = text_clf.predict(X_test)

print('Score:',text_clf.score(X_test, y_test))
Python 3 histogram: how to get counts and bins with plt.hist(), but without displaying the histogram in screen?

Python 3 histogram: how to get counts and bins with plt.hist(), but without displaying the histogram in screen?


By : snow_monkey
Date : March 29 2020, 07:55 AM
I wish this help you Instead of plt.hist, you can use numpy.histogram. Example:
code :
>>> import numpy as np
>>> x = np.random.randint(0, 5, size=10)
>>> x
array([3, 2, 0, 2, 1, 0, 2, 0, 0, 3])
>>> counts, bin_edges = np.histogram(x, bins=3)
>>> counts
array([4, 1, 5])
>>> bin_edges
array([0., 1., 2., 3.])
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