subsample | Randomly sample lines from a csv tsv
kandi X-RAY | subsample Summary
kandi X-RAY | subsample Summary
Randomly sample lines from a csv, tsv, or other line-based data file
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of subsample
subsample Key Features
subsample Examples and Code Snippets
Lines of Code : 7
License : No License
Copydef _subsample_data(self, X, Y, n=10000):
if Y is not None:
X, Y = shuffle(X, Y)
return X[:n], Y[:n]
else:
X = shuffle(X)
return X[:n] class PartialStandardScaler(StandardScaler):
def transform(self, X, column_mask=None):
if column_mask is None:
return super().transform(X)
return (X[:,column_mask] - self.mean_[column_mask])/self.scale_[codef generator(X_data,y_data,batch_size):
while True:
for step in range(X_data.shape[0]//batch_size):
start=step*batch_size
end=step*(batch_size+1)
current_x=X_data.iloc[start]
current_y=y_dfrom tkinter import *
filepath = "Arrow.gif"
blakpath = "Black.gif"
class UI:
def _setcell(self, row, column):
self.btnarr[row][column].config(
image = self.blankVirtual, command = '')
def window(self, rows,df['date'] = pd.to_datetime(df['date'])
#if necessary sorting
#df = df.sort_values(['bid','date'])
df['prev'] = df.groupby('bid').cumcount()
df1 = df.groupby(['bid', pd.Grouper(freq='M', key='date')], sort=False).sample(n=1)
print (df1)class XGBoostQuantileRegressor(XGBRegressor):
def __init__(self, quant_alpha, max_depth=3, **kwargs):
self.quant_alpha = quant_alpha
super().__init__(max_depth=max_depth, **kwargs)
# other methods unchanged and omiparameters = {
'estimator__estimator__reg_alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100],
'estimator__estimator__max_depth': [10, 100, 1000]
}
seed = 42
np.random.seed(seed)
def subsample(df, size: int):
assert 0 < size < len(data)
subsample_indexes = np.random.randint(0, len(data), size)
return df.iloc[subsample_indexes, :].copy()
def sklearn.ensemble.StackingRegressor(estimators, final_estimator=None, *, cv=None, n_jobs=None, passthrough=False, verbose=0)
space = [
{'name': 'max_depth', 'type': 'discrete', 'domain': (2,3,4,5)},
{'name': 'learning_rate', 'type': 'continuous', 'domain': (0.01, 0.3)},
...
]
Community Discussions
Trending Discussions on subsample
QUESTION
I want to optimize my HPO of my lightgbm model. I used a Bayesian Optimization process to do so. Sadly my algorithm fails to converge.
MRE
...ANSWER
Answered 2022-Mar-21 at 22:34This is related to a change in scipy 1.8.0,
One should use -np.squeeze(res.fun) instead of -res.fun[0]
https://github.com/fmfn/BayesianOptimization/issues/300
The comments in the bug report indicate reverting to scipy 1.7.0 fixes this,
It seems the fix is been proposed in the BayesianOptimization package: https://github.com/fmfn/BayesianOptimization/pull/303
But this has not been merged and released yet, so you could either:
- fall back to scipy 1.7.0
- use the forked github version of BayesianOptimization with the patch (https://github.com/samFarrellDay/BayesianOptimization)
- apply the patch in issue 303 manually on your system
QUESTION
I have trained this model:
...ANSWER
Answered 2022-Mar-11 at 13:52If you change interaction_constraints=[] it will enforce that the features cannot interact.
If you want to verify that this is the case, you could interrogate the individual tree outputs by doing something like
QUESTION
I have a table and I would like the center all the columns except the first one. How to get around with this. many thanks in advance.
...ANSWER
Answered 2022-Mar-10 at 11:08I still don't understand why you are using a tabularx if you don't have an X column, anyway, you can centre columns like this:
QUESTION
I'm trying to port over some "parallel" Python code to Azure Databricks. The code runs perfectly fine locally, but somehow doesn't on Azure Databricks. The code leverages the multiprocessing library, and more specifically the starmap function.
The code goes like this:
...ANSWER
Answered 2021-Aug-22 at 09:31You should stop trying to invent the wheel, and instead start to leverage the built-in capabilities of Azure Databricks. Because Apache Spark (and Databricks) is the distributed system, machine learning on it should be also distributed. There are two approaches to that:
Training algorithm is implemented in the distributed fashion - there is a number of such algorithms packaged into Apache Spark and included into Databricks Runtimes
Use machine learning implementations designed to run on a single node, but train multiple models in parallel - that what typically happens during hyper-parameters optimization. And what is you're trying to do
Databricks runtime for machine learning includes the Hyperopt library that is designed for the efficient finding of best hyper-parameters without trying all combinations of the parameters, that allows to find them faster. It also include the SparkTrials API that is designed to parallelize computations for single-machine ML models such as scikit-learn. Documentation includes a number of examples of using that library with single-node ML algorithms, that you can use as a base for your work - for example, here is an example for scikit-learn.
P.S. When you're running the code with multiprocessing, then the code is executed only on the driver node, and the rest of the cluster isn't utilized at all.
QUESTION
I'm attempting to stack a BERT tensorflow model with and XGBoost model in python. To do this, I have trained the BERT model and and have a generator that takes the predicitons from BERT (which predicts a category) and yields a list which is the result of categorical data concatenated onto the BERT prediction. This doesn't train, however because it doesn't have a shape. The code I have is:
...ANSWER
Answered 2021-Aug-09 at 16:40def generator(X_data,y_data,batch_size):
while True:
for step in range(X_data.shape[0]//batch_size):
start=step*batch_size
end=step*(batch_size+1)
current_x=X_data.iloc[start]
current_y=y_data.iloc[start]
#Or if it's an numpy array just get the rows
yield current_x,current_y
Generator=generator(X,y)
batch_size=32
number_of_steps=X.shape[0]//batch_size
clf = xgb.XGBClassifier(max_depth=200, n_estimators=400, subsample=1, learning_rate=0.07, reg_lambda=0.1, reg_alpha=0.1,\
gamma=1)
for step in number_of_steps:
X_g,y_g=next(Generator)
clf.fit(X_g, y_g)
QUESTION
I am new to R so I apologize in advance. I sampled moths along an elevational gradient with a total of 8 different sites. I had unequal sampling nights per elevation. Because of my unequal sampling nights, I want to standardize my species by rarefying my species based on the individuals I got. I am confused about how to rarefy my species data. From the rarefy package (rarefy(x, sample, se = FALSE, MARGIN = 1), I don't understand how to specify my sample/subsample number. Is it going to be the minimum number of individuals I got from a site? Thank you very much
...ANSWER
Answered 2022-Feb-15 at 13:13Yes, to account for uneven sampling you would typically rarefy to the minimum number of individuals you got from a site. Below is an example:
QUESTION
I have trained an XGBoost Regressor model on data that has a different shape to the test data I intend to predict on. Is there a way to go around this or a model that can tolerate feature mismatches?
The input training data and test data got mismatched during One Hot Encoding of categorical features.
...ANSWER
Answered 2022-Jan-18 at 14:32Please check where 249-235=14 features are in test data.
Or fit on same data
best_xgb.fit(X[test_data.columns], y)
QUESTION
I am new to programming in R and with .shp files.
I am trying to take a subsample / subset of a .shp file that is so big, you can download this file from here: https://www.ine.es/ss/Satellite?L=es_ES&c=Page&cid=1259952026632&p=1259952026632&pagename=ProductosYServicios%2FPYSLayout (select the year 2021 and then go ahead).
I have tried several things but none of them work, neither is it worth passing it to sf because it would simply add one more column called geometry with the coordinates listed and that is not enough for me to put it later in the leaflet package.
I have tried this here but it doesn't work for me:
...ANSWER
Answered 2022-Jan-13 at 17:22Here's one approach:
QUESTION
I want to build a quantile regressor based on XGBRegressor, the scikit-learn wrapper class for XGBoost. I have the following two versions: the second version is simply trimmed from the first one, but it no longer works.
I am wondering why I need to put every parameters of XGBRegressor in its child class's initialization? What if I just want to take all the default parameter values except for max_depth?
(My XGBoost is of version 1.4.2.)
No.1 the full version that works as expected:
...ANSWER
Answered 2021-Dec-26 at 11:58I am not an expert with scikit-learn but it seems that one of the requirements of various objects used by this framework is that they can be cloned by calling the sklearn.base.clone method. This appears to be something that the existing XGBRegressor class does, so is something your subclass of XGBRegressor must also do.
What may help is to pass any other unexpected keyword arguments as a **kwargs parameter. In your constructor, kwargs will contain a dict of all of the other keyword parameters that weren't assigned to other constructor parameters. You can pass this dict of parameters on to the call to the superclass constructor by referring to them as **kwargs again: this will cause Python to expand them out:
QUESTION
I am currently working with a dataframe that contains observations with a corresponding timestamp. Please see below for a subsample of the data set.
Now, I would like to split the df into smaller segments based on the time difference in the second column. However, I don't want to create multiple new dfs, but I would like to assign different "ids" to different segments and do this for every factor in the first column.
I.e. let's say I have a cut off time of 0.4 days. Now, I want to go through the data frame and as soon as the time difference is bigger than 0.4 days, I would like that the ID from A.1, changes to A.2. The id then stays A.2 as long as the time difference is < 0.4 days. However, as soon as the time difference in the next row is >0.4 days the id should change to A.3, etc (please see desired output).
Subsample of dataset:
...ANSWER
Answered 2021-Dec-23 at 09:37In data.table:
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