Anybody know how to extract the standard deviation for a ResampleResult/BenchmarkResult in mlr3?
The implemented metrics seems to be returning only the average value.
measures <- list(
mlr3::msr("classif.fbeta", predict_sets = "train", id = "fbeta_train"),
mlr3::msr("classif.fbeta", id = "fbeta_test")
)
bmr$aggregate(measures)
Calculating standard errors from Resampling Results is not as straight-forward, as e.g. the folds of a cross-validation are not independent.
We are currently working on integrating some methods to calculate standard errors and confidence intervals, as well as providing new resampling techniques that allow for proper inference of the generalization error.
I have an XGBoost model that runs TFIDF vectorization and TruncateSVD reduction on text features. I want to understand feature importance of the model.
This is how I process text features in my dataset:
.......
tfidf = TfidfVectorizer(tokenizer=tokenize)
tfs = tfidf.fit_transform(token_dict)
svd = TruncatedSVD(n_components=15)
temp = pd.DataFrame(svd.fit_transform(tfs))
temp.rename(columns=lambda x: text_feature+'_'+str(x), inplace=True)
dataset=dataset.join(temp,how='inner')
.......
It works okayish and now I'm trying to understand importance of the features in the dataset. I generate the charts using:
xgb.plot_importance(model, max_num_features=15)
pyplot.show()
And get something similar to:
this chart
What would be the right way to "map" importance SVD dimensions to the dimensions of the initial dataset? So I know importance of summary and not summary_1, summary_2, summary_X.
Thanks
one thing you can try is getting the how important each original feature is to creating new features. you can get it using the following:
feature_importance_scores = np.abs(svd.components_).sum(axis=0)
feature_importance_scores /= feature_importance_scores.sum() # normalize to make it more clear
you can get the overall importance by multiplying these values with xgb.feature_importances_
I have a stacked learner where the output layer is a regr.ranger with params list(rf.quantreg = TRUE, rf.keep.inbag = TRUE). Is it possible to predict quantiles with GraphLearners like this?
I know that for a pure lrn('regr.ranger'), once trained, I can simply reference the ranger model directly and use that for quantile prediction:
predict(my_learner$model, data = my_test_data, type = "quantiles", quantiles = c(0.025, 0.975))
But for the stacked learner, I have other learners mediating between the features and regr.ranger, so it seems to me that I have to go via mlr3.
My GraphLearner consists of some feature coding, a regr.kknn learner and a regr.glm learner plus some extras. Then rf (a regr.ranger) sits at the output level:
I'm implementing a Multilayer Perceptron in Keras and using scikit-learn to perform cross-validation. For this, I was inspired by the code found in the issue Cross Validation in Keras
from sklearn.cross_validation import StratifiedKFold
def load_data():
# load your data using this function
def create model():
# create your model using this function
def train_and_evaluate__model(model, data[train], labels[train], data[test], labels[test)):
# fit and evaluate here.
if __name__ == "__main__":
X, Y = load_model()
kFold = StratifiedKFold(n_splits=10)
for train, test in kFold.split(X, Y):
model = None
model = create_model()
train_evaluate(model, X[train], Y[train], X[test], Y[test])
In my studies on neural networks, I learned that the knowledge representation of the neural network is in the synaptic weights and during the network tracing process, the weights that are updated to thereby reduce the network error rate and improve its performance. (In my case, I'm using Supervised Learning)
For better training and assessment of neural network performance, a common method of being used is cross-validation that returns partitions of the data set for training and evaluation of the model.
My doubt is...
In this code snippet:
for train, test in kFold.split(X, Y):
model = None
model = create_model()
train_evaluate(model, X[train], Y[train], X[test], Y[test])
We define, train and evaluate a new neural net for each of the generated partitions?
If my goal is to fine-tune the network for the entire dataset, why is it not correct to define a single neural network and train it with the generated partitions?
That is, why is this piece of code like this?
for train, test in kFold.split(X, Y):
model = None
model = create_model()
train_evaluate(model, X[train], Y[train], X[test], Y[test])
and not so?
model = None
model = create_model()
for train, test in kFold.split(X, Y):
train_evaluate(model, X[train], Y[train], X[test], Y[test])
Is my understanding of how the code works wrong? Or my theory?
If my goal is to fine-tune the network for the entire dataset
It is not clear what you mean by "fine-tune", or even what exactly is your purpose for performing cross-validation (CV); in general, CV serves one of the following purposes:
Model selection (choose the values of hyperparameters)
Model assessment
Since you don't define any search grid for hyperparameter selection in your code, it would seem that you are using CV in order to get the expected performance of your model (error, accuracy etc).
Anyway, for whatever reason you are using CV, the first snippet is the correct one; your second snippet
model = None
model = create_model()
for train, test in kFold.split(X, Y):
train_evaluate(model, X[train], Y[train], X[test], Y[test])
will train your model sequentially over the different partitions (i.e. train on partition #1, then continue training on partition #2 etc), which essentially is just training on your whole data set, and it is certainly not cross-validation...
That said, a final step after the CV which is often only implied (and frequently missed by beginners) is that, after you are satisfied with your chosen hyperparameters and/or model performance as given by your CV procedure, you go back and train again your model, this time with the entire available data.
You can use wrappers of the Scikit-Learn API with Keras models.
Given inputs x and y, here's an example of repeated 5-fold cross-validation:
from sklearn.model_selection import RepeatedKFold, cross_val_score
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
def buildmodel():
model= Sequential([
Dense(10, activation="relu"),
Dense(5, activation="relu"),
Dense(1)
])
model.compile(optimizer='adam', loss='mse', metrics=['mse'])
return(model)
estimator= KerasRegressor(build_fn=buildmodel, epochs=100, batch_size=10, verbose=0)
kfold= RepeatedKFold(n_splits=5, n_repeats=100)
results= cross_val_score(estimator, x, y, cv=kfold, n_jobs=2) # 2 cpus
results.mean() # Mean MSE
I think many of your questions will be answered if you read about nested cross-validation. This is a good way to "fine tune" the hyper parameters of your model. There's a thread here:
https://stats.stackexchange.com/questions/65128/nested-cross-validation-for-model-selection
The biggest issue to be aware of is "peeking" or circular logic. Essentially - you want to make sure that none of data used to assess model accuracy is seen during training.
One example where this might be problematic is if you are running something like PCA or ICA for feature extraction. If doing something like this, you must be sure to run PCA on your training set, and then apply the transformation matrix from the training set to the test set.
The main idea of testing your model performance is to perform the following steps:
Train a model on a training set.
Evaluate your model on a data not used during training process in order to simulate a new data arrival.
So basically - the data you should finally test your model should mimic the first data portion you'll get from your client/application to apply your model on.
So that's why cross-validation is so powerful - it makes every data point in your whole dataset to be used as a simulation of new data.
And now - to answer your question - every cross-validation should follow the following pattern:
for train, test in kFold.split(X, Y
model = training_procedure(train, ...)
score = evaluation_procedure(model, test, ...)
because after all, you'll first train your model and then use it on a new data. In your second approach - you cannot treat it as a mimicry of a training process because e.g. in second fold your model would have information kept from the first fold - which is not equivalent to your training procedure.
Of course - you could apply a training procedure which uses 10 folds of consecutive training in order to finetune network. But this is not cross-validation then - you'll need to evaluate this procedure using some kind of schema above.
The commented out functions make this a little less obvious, but the idea is to keep track of your model performance as you iterate through your folds and at the end provide either those lower level performance metrics or an averaged global performance. For example:
The train_evaluate function ideally would output some accuracy score for each split, which could be combined at the end.
def train_evaluate(model, x_train, y_train, x_test, y_test):
model.fit(x_train, y_train)
return model.score(x_test, y_test)
X, Y = load_model()
kFold = StratifiedKFold(n_splits=10)
scores = np.zeros(10)
idx = 0
for train, test in kFold.split(X, Y):
model = create_model()
scores[idx] = train_evaluate(model, X[train], Y[train], X[test], Y[test])
idx += 1
print(scores)
print(scores.mean())
So yes you do want to create a new model for each fold as the purpose of this exercise is to determine how your model as it is designed performs on all segments of the data, not just one particular segment that may or may not allow the model to perform well.
This type of approach becomes particularly powerful when applied along with a grid search over hyperparameters. In this approach you train a model with varying hyperparameters using the cross validation splits and keep track of the performance on splits and overall. In the end you will be able to get a much better idea of which hyperparameters allow the model to perform best. For a much more in depth explanation see sklearn Model Selection and pay particular attention to the sections of Cross Validation and Grid Search.
I use the following simple IsolationForest algorithm to detect the outliers of given dataset X of 20K samples and 16 features, I run the following
train_X, tesy_X, train_y, test_y = train_test_split(X, y, train_size=.8)
clf = IsolationForest()
clf.fit(X) # Notice I am using the entire dataset X when fitting!!
print (clf.predict(X))
I get the result:
[ 1 1 1 -1 ... 1 1 1 -1 1]
This question is: Is it logically correct to use the entire dataset X when fitting into IsolationForest or only train_X?
Yes, it is logically correct to ultimately train on the entire dataset.
With that in mind, you could measure the test set performance against the training set's performance. This could tell you if the test set is from a similar distribution as your training set.
If the test set scores anomalous as compared to the training set, then you can expect future data to be similar. In this case, I would like more data to have a more complete view of what is 'normal'.
If the test set scores similarly to the training set, I would be more comfortable with the final Isolation Forest trained on all data.
Perhaps you could use sklearn TimeSeriesSplit CV in this fashion to get a sense for how much data is enough for your problem?
Since this is unlabeled data to the anomaly detector, the more data the better when defining 'normal'.