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
train_test_split splits arrays or matrices into random train and test subsets. That means that everytime you run it without specifying random_state, you will get a different result, this is expected behavior. For example: Run 1: >>> a, b = np.arange(10).reshape((5, 2)), range(5) >>> train_test_split(a, b) [array([[6, 7], [8, 9], [4, 5]]), array([[2, 3], [0, 1]]), … 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
I am not sure if previous versions of pandas prevented this but now the following snippet works perfectly for me and produces exactly what you want without having to use apply >>> import pandas as pd >>> from sklearn.preprocessing import MinMaxScaler >>> scaler = MinMaxScaler() >>> dfTest = pd.DataFrame({‘A’:[14.00,90.20,90.95,96.27,91.21], ‘B’:[103.02,107.26,110.35,114.23,114.68], ‘C’:[‘big’,’small’,’big’,’small’,’small’]}) >>> dfTest[[‘A’, ‘B’]] = … Read more
Here’s a small kmeans that uses any of the 20-odd distances in scipy.spatial.distance, or a user function. Comments would be welcome (this has had only one user so far, not enough); in particular, what are your N, dim, k, metric ? #!/usr/bin/env python # kmeans.py using any of the 20-odd metrics in scipy.spatial.distance # kmeanssample … Read more
This is kind of overkill but let’s give it a go. First lets use statsmodel to find out what the p-values should be import pandas as pd import numpy as np from sklearn import datasets, linear_model from sklearn.linear_model import LinearRegression import statsmodels.api as sm from scipy import stats diabetes = datasets.load_diabetes() X = diabetes.data y … Read more
sklearn >= 0.22.0 sklearn.metrics has a mean_squared_error function with a squared kwarg (defaults to True). Setting squared to False will return the RMSE. from sklearn.metrics import mean_squared_error rms = mean_squared_error(y_actual, y_predicted, squared=False) sklearn < 0.22.0 sklearn.metrics has a mean_squared_error function. The RMSE is just the square root of whatever it returns. from sklearn.metrics import mean_squared_error … Read more
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
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
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