sklearn | implement Scikit Learn for Python in C | Machine Learning library
kandi X-RAY | sklearn Summary
kandi X-RAY | sklearn Summary
Trying to implement Scikit Learn for Python in C++. SOURCE NEEDED: preprocessing.h, proecessing.cpp and statx.h. StandardScaler will standardize features by removing the mean and scaling to unit variance. ref: Scikit Learn docs. SOURCE NEEDED: preprocessing.h, proecessing.cpp and statx.h. SOURCE NEEDED: preprocessing.h and preprocessing.cpp. Label encoding is the process of encoding the categorical data into numerical data. For example if a column in the dataset contains country values like GERMANY, FRANCE, ITALY then label encoder will convert this categorical data into numerical data like this. HEADERS NEEDED: lsr.h and lsr.cpp. Training and saving the model. Loading the saved model. Classification male - female using height, weight, foot size and saving the model. HEADERS / SOURCE NEEDED: naive_bayes.h, naive_bayes.cpp, json.h. Please do not get confused with the word "regression" in Logistic regression. It is generally used for classification problems. The heart of the logistic regession is sigmoid activation function. An activation function is a function which takes any input value and outputs value within a certain case. In our case(sigmoid), it returns between 0 and 1. In the image, you can see the output(y) of sigmoid activation function for -3 >= x <= 3. The idea behind the logistic regression is taking the output from linear regression, i.e., y = mx+c and applying logistic function 1/(1+e^-y) which outputs the value between 0 and 1. We can clearly see this is a binary classifier, i.e., for example, it can be used for classifying binary datasets like predicting whether it is a male or a female using certain parameters. But we can use this logistic regression to classify multi-class problems too with some modifications. Here, we are using the one vs rest principle. That is training many linear regression models, for example, if the class count is 10, it will train 10 Linear Regression models by changing the class values with 1 as the class value to predict the probability and 0 to the rest. If you don't understand, here is a detailed explanation: We are going to take a simple classification problem to classify whether it is a male or female.
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QUESTION
When displaying summary_plot, the color bar does not show.
...ANSWER
Answered 2021-Dec-26 at 21:17I had the same problem as you did, and I found that the solution was to downgrade matplotlib to 3.4.3.. It appears SHAP isn't optimized for matplotlib 3.5.1 yet.
QUESTION
The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :
...ANSWER
Answered 2022-Mar-27 at 23:14After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.
QUESTION
How to change colors in decision tree plot using sklearn.tree.plot_tree without using graphviz as in this question: Changing colors for decision tree plot created using export graphviz?
...ANSWER
Answered 2021-Dec-27 at 14:35Many matplotlib functions follow the color cycler to assign default colors, but that doesn't seem to apply here.
The following approach loops through the generated annotation texts (artists) and the clf tree structure to assign colors depending on the majority class and the impurity (gini). Note that we can't use alpha, as a transparent background would show parts of arrows that are usually hidden.
QUESTION
Given an sklearn tranformer t, is there a way to determine whether t changes columns/column order of any given input dataset X, without applying it to the data?
For example with t = sklearn.preprocessing.StandardScaler there is a 1-to-1 mapping between the columns of X and t.transform(X), namely X[:, i] -> t.transform(X)[:, i], whereas this is obviously not the case for sklearn.decomposition.PCA.
A corollary of that would be: Can we know, how the columns of the input will change by applying t, e.g. which columns an already fitted sklearn.feature_selection.SelectKBest chooses.
I am not looking for solutions to specific transformers, but a solution applicable to all or at least a wide selection of transformers.
Feel free to implement your own Pipeline class or wrapper if necessary.
...ANSWER
Answered 2021-Nov-23 at 15:01I found a partial answer. Both StandardScaler and SelectKBest have .get_feature_names_out methods. I did not find the time to investigate further.
QUESTION
I am trying code from this page. I ran up to the part LR (tf-idf) and got the similar results
After that I decided to try GridSearchCV. My questions below:
1)
...ANSWER
Answered 2021-Dec-09 at 23:12You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01
QUESTION
So I was trying to convert my data's timestamps from Unix timestamps to a more readable date format. I created a simple Java program to do so and write to a .csv file, and that went smoothly. I tried using it for my model by one-hot encoding it into numbers and then turning everything into normalized data. However, after my attempt to one-hot encode (which I am not sure if it even worked), my normalization process using make_column_transformer failed.
...ANSWER
Answered 2021-Dec-09 at 20:59using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.
QUESTION
Hi all I am having trouble understanding how to use the output of sklearn.calibration.CalibratedClassifierCV.
I have calibrated my binary classifier using this method, and results are greatly improved. However I am not sure how to interpret the results. sklearn guide states that, after calibration,
the output of
predict_probamethod can be directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify the samples such that among the samples to which it gave a predict_proba value close to 0.8, approximately 80% actually belong to the positive class.
Now I would like to reduce false positive by applying a cutoff at .6 for the model to predict label True. Without the calibration, I would have simply used my_model.predict_proba() > .6.
However, it seems that after calibration the meaning of predict_proba has changed, so I am not sure if I can do that anymore.
From a quick testing it seems that predict and predict_proba follow the same logic I would expect before calibration. The output of:
...ANSWER
Answered 2021-Dec-03 at 13:03For me, you can actually use predict_proba() after calibration to apply a different cutoff.
What happens within class CalibratedClassifierCV (as you noticed) is effectively that the output of predict() is based on the output of predict_proba() (see here for reference), i.e. np.argmax(self.predict_proba(X), axis=1) == self.predict(X).
On the other side, for the non-calibrated classifier that you're passing to CalibratedClassifierCV (depending on whether it is a probabilistic classifier or not) the above equality may or may not hold (e.g. it does not for an SVC() classifier - see here, for instance, for some other details on this).
QUESTION
I need to import function from different python scripts, which will used inside preprocessing.py file. I was not able to find a way to pass the dependent files to SKLearnProcessor Object, due to which I am getting ModuleNotFoundError.
Code:
...ANSWER
Answered 2021-Nov-25 at 12:44This isn't supported in SKLearnProcessor. You'd need to package your dependencies in docker image and create a custom Processor (e.g. a ScriptProcessor with the image_uri of the docker image you created.)
QUESTION
I need to measure similarity between feature vectors using CCA module. I saw sklearn has a good CCA module available: https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.CCA.html
In different papers I reviewed, I saw that the way to measure similarity using CCA is to calculate the mean of the correlation coefficients, for example as done in this following notebook example: https://github.com/google/svcca/blob/1f3fbf19bd31bd9b76e728ef75842aa1d9a4cd2b/tutorials/001_Introduction.ipynb
How to calculate the correlation coefficients (as shown in the notebook) using sklearn CCA module?
...ANSWER
Answered 2021-Nov-16 at 10:07In reference to the notebook you provided which is a supporting artefact to and implements ideas from the following two papers
- "SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability". Neural Information Processing Systems (NeurIPS) 2017
- "Insights on Representational Similarity in Deep Neural Networks with Canonical Correlation". Neural Information Processing Systems (NeurIPS) 2018
The authors there calculate 50 = min(A_fake neurons, B_fake neurons) components and plot the correlations between the transformed vectors of each component (i.e. 50).
With the help of the below code, using sklearn CCA, I am trying to reproduce their Toy Example. As we'll see the correlation plots match. The sanity check they used in the notebook came very handy - it passed seamlessly with this code as well.
QUESTION
I have run the sklearn.manifold.TSNE example code from the sklearn documentation, but I got the error described in the questions' title.
I have already tried updating my sklearn version to the latest one (by !pip install -U scikit-learn) (scikit-learn=1.0.1). However, the problem is still there.
Does anyone know how to fix it?
- python = 3.7.12
- sklearn= 1.0.1
Example code:
...ANSWER
Answered 2021-Nov-03 at 12:01Delete learning_rate='auto' solved my problem.
Thanks @FlaviaGiammarino comment!!
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