shap | game theoretic approach to explain the output | Machine Learning library
kandi X-RAY | shap Summary
kandi X-RAY | shap Summary
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
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Lines of Code : 34License : Permissive (MIT)
Copyfrom keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
import keras.backend as K
import numpy as np
import json
import shap
# load pre-trained model and choose two images to explain
model = VGG16(weights='im import xgboost
import shap
# train an XGBoost model
X, y = shap.datasets.boston()
model = xgboost.XGBRegressor().fit(X, y)
# explain the model's predictions using SHAP
# (same syntax works for LightGBM, CatBoost, scikit-learn, transformers, Spark, import sklearn
import shap
from sklearn.model_selection import train_test_split
# print the JS visualization code to the notebook
shap.initjs()
# train a SVM classifier
X_train,X_test,Y_train,Y_test = train_test_split(*shap.datasets.iris(), test_si /*
* Requires:
* psiturk.js
* utils.js
*/
// Initalize psiturk object
var psiTurk = new PsiTurk(uniqueId, adServerLoc, mode);
var mycondition = condition; // these two variables are passed by the psiturk server process
var mycounterbal !function(e){function t(o){if(n[o])return n[o].exports;var r=n[o]={i:o,l:!1,exports:{}};return e[o].call(r.exports,r,r.exports,t),r.l=!0,r.exports}var n={};return t.m=e,t.c=n,t.i=function(e){return e},t.d=function(e,n,o){t.o(e,n)||Object.defineProper
function AssertException(message) { this.message = message; }
AssertException.prototype.toString = function () {
return 'AssertException: ' + this.message;
};
function assert(exp, message) {
if (!exp) {
throw new AssertException(message);
}
}
# Select model
shap_model = model_linear_regression(pipe=LINEAR_PIPE, inverse=True)
# fit model
model_fitted = shap_model.fit(X_train, y_train)
# Summarize model (not necessary but makes things faster
# If not being summarized replace allfrom sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from shap import TreeExplainer
X, y = load_breast_cancer(return_X_y=True)
model = RandomForestClassifier(max_depth=5, n_estimators=100).fiimport numpy as np
from shap import KernelExplainer
def f(x):
y = x[:, 0] ** 2.5 + 3 * x[:, 1] + 10
return np.array(y)
x = np.arange(20).reshape((10,2))
explainer = KernelExplainer(f, x)
shap_values = explainer.shap_values(x)
<10 print("Prediction From Adding SHAP Values to Base Value : ", explainer.expected_value + shap_vals.sum())
AttributeError: 'list' object has no attribute 'sum'
print("Prediction From Adding SHAP Values to Base VaCommunity Discussions
Trending Discussions on shap
QUESTION
I am working on a binary classification using random forest model, neural networks in which am using SHAP to explain the model predictions. I followed the tutorial and wrote the below code to get the waterfall plot shown below
...ANSWER
Answered 2022-Apr-05 at 09:38If I recall correctly, you can do something like this with pandas
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
I want to use the python Shap module to interpret user supplied nonlinear functions. I'll take just one simple example as a representative, but it cannot run successfully. I would like to ask if Shap can be used for this simple model and if yes how to implement it.
Here is my code.
...ANSWER
Answered 2022-Apr-04 at 08:35Did you mean this: 10 datapoints, 2 features, 1 outcome?
QUESTION
I am working on a binary classification using random forest and trying out SHAP to explain the model predictions.
However, I would like to convert the SHAP local explanation plots with values into a pandas dataframe for each instance.
Is there any one here who can help me with exporting SHAP local explanations to pandas dataframe for each instance?
I know that SHAPASH has .to_pandas() method but couldn't find anything like that in SHAP
I tried something like below based on the SO post here but it doesn't help
...ANSWER
Answered 2022-Mar-25 at 03:23If you have a model like this:
QUESTION
I'm trying to create a force_plot for my Random Forest model that has two classes (1 and 2), but I am a bit confused about the parameters for the force_plot.
I have two different force_plot parameters I can provide the following:
...ANSWER
Answered 2022-Mar-22 at 18:14Let's try reproducible:
QUESTION
I need to plot how each feature impacts the predicted probability for each sample from my LightGBM binary classifier. So I need to output Shap values in probability, instead of normal Shap values. It does not appear to have any options to output in term of probability.
The example code below is what I use to generate dataframe of Shap values and do a force_plot for the first data sample. Does anyone know how I should modify the code to change the output?
I'm new to Shap value and the Shap package. Thanks a lot in advance.
ANSWER
Answered 2022-Mar-14 at 13:40You can consider running your output values through a softmax() function. For reference, it is defined as :
QUESTION
I'm using shap.utils.hclust to figure out which features are redundant and following the documentation
Reproducible example:
...ANSWER
Answered 2022-Mar-20 at 16:16- Underneath, even tree models for classification are regression tasks. SHAP calls it "raw" feature output space, Tensorflow would call it logits. To convert raw to proba space sigmoid or softmax are used. So, answering your first question:
QUESTION
I am trying to build a dashboard of my NLP project. So I am using a BERT model for the predictions, the SHAP package for the visualization and Streamlit for creating dashboard:
...ANSWER
Answered 2021-Dec-10 at 05:15This can be visualized with Streamlit Components and latest SHAP v0.36+ (which define a new getjs method), to plot JS SHAP plots
(some plots like
summary_plotare actuallyMatplotliband can be plotted withst.pyplot)
QUESTION
I am training a TensorFlow RNN model using LSTM layers to determine if sound is coming more from the right or left in a stereo audio signal. The model training goes smoothly, then, once it is done training, I get an Invalid Argument Error as shown below. Does anyone know what could be causing this? I have tried fixing it using the solution to a similar question found here, but to no avail.
I do not understand why it is expecting a tensor of shape [32,2]. Did I define that somewhere I am unaware of?
Here is my code:
...ANSWER
Answered 2022-Feb-08 at 07:13You get this error because you hard-coded the batch size in the first LSTM layer and the number of data samples is not evenly divisible by 100. You have to take care of the remainder somehow. I would recommend removing the batch size from the first layer and only entering the batch size in model.fit. This way your model will be able to handle the remaining smaller batch(es). Here is an example:
QUESTION
We use to spin cluster with below configurations. It used to run fine till last week but now failing with error ERROR: Failed cleaning build dir for libcst Failed to build libcst ERROR: Could not build wheels for libcst which use PEP 517 and cannot be installed directly
ANSWER
Answered 2022-Jan-19 at 21:50Seems you need to upgrade pip, see this question.
But there can be multiple pips in a Dataproc cluster, you need to choose the right one.
For init actions, at cluster creation time,
/opt/conda/defaultis a symbolic link to either/opt/conda/miniconda3or/opt/conda/anaconda, depending on which Conda env you choose, the default is Miniconda3, but in your case it is Anaconda. So you can run either/opt/conda/default/bin/pip install --upgrade pipor/opt/conda/anaconda/bin/pip install --upgrade pip.For custom images, at image creation time, you want to use the explicit full path,
/opt/conda/anaconda/bin/pip install --upgrade pipfor Anaconda, or/opt/conda/miniconda3/bin/pip install --upgrade pipfor Miniconda3.
So, you can simply use /opt/conda/anaconda/bin/pip install --upgrade pip for both init actions and custom images.
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