On Windows all of your multiprocessing-using code must be guarded by if __name__ == “__main__”: So to be safe, I would put all of your the code currently at the top-level of your script in a main() function, and then just do this at the top-level: if __name__ == “__main__”: main() See the “Safe importing … Read more
Despite the plethora of wrong answers here that attempt to circumvent the error by numerically manipulating the predictions, the root cause of your error is a theoretical and not computational issue: you are trying to use a classification metric (accuracy) in a regression (i.e. numeric prediction) model (LinearRegression), which is meaningless. Just like the majority … Read more
Classifiers are just objects that can be pickled and dumped like any other. To continue your example: import cPickle # save the classifier with open(‘my_dumped_classifier.pkl’, ‘wb’) as fid: cPickle.dump(gnb, fid) # load it again with open(‘my_dumped_classifier.pkl’, ‘rb’) as fid: gnb_loaded = cPickle.load(fid) Edit: if you are using a sklearn Pipeline in which you have custom … Read more
Arguably, the real challenge in such cases is to be sure that you compare apples to apples. And in your case, it seems that you don’t. Our best friend is always the relevant documentation, combined with simple experiments. So… Although scikit-learn’s LinearRegression() (i.e. your 1st R-squared) is fitted by default with fit_intercept=True (docs), this is … Read more
I’m in Python 3.8.5. It sounds too simple to be real, but I had this same issue and all I did was reinstall numpy. Gone. pip install –upgrade numpy or pip uninstall numpy pip install numpy
This might happen inside scikit, and it depends on what you’re doing. I recommend reading the documentation for the functions you’re using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria. EDIT: How could I miss that: np.isnan(mat.any()) #and gets False np.isfinite(mat.all()) #and gets True … Read more
We can also use sklearn.preprocessing.MultiLabelBinarizer: Often we want to use sparse DataFrame for the real world data in order to save a lot of RAM. Sparse solution (for Pandas v0.25.0+) from sklearn.preprocessing import MultiLabelBinarizer mlb = MultiLabelBinarizer(sparse_output=True) df = df.join( pd.DataFrame.sparse.from_spmatrix( mlb.fit_transform(df.pop(‘Col3’)), index=df.index, columns=mlb.classes_)) result: In [38]: df Out[38]: Col1 Col2 Apple Banana Grape Orange … Read more
I believe that this answer is more correct than the other answers here: from sklearn.tree import _tree def tree_to_code(tree, feature_names): tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else “undefined!” for i in tree_.feature ] print “def tree({}):”.format(“, “.join(feature_names)) def recurse(node, depth): indent = ” ” * depth if tree_.feature[node] != _tree.TREE_UNDEFINED: … Read more
You can easily do this though, df.apply(LabelEncoder().fit_transform) EDIT2: In scikit-learn 0.20, the recommended way is OneHotEncoder().fit_transform(df) as the OneHotEncoder now supports string input. Applying OneHotEncoder only to certain columns is possible with the ColumnTransformer. EDIT: Since this original answer is over a year ago, and generated many upvotes (including a bounty), I should probably extend … Read more
Numpy solution. We will shuffle the whole dataset first (df.sample(frac=1, random_state=42)) and then split our data set into the following parts: 60% – train set, 20% – validation set, 20% – test set In [305]: train, validate, test = \ np.split(df.sample(frac=1, random_state=42), [int(.6*len(df)), int(.8*len(df))]) In [306]: train Out[306]: A B C D E 0 0.046919 … Read more