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1. #SETTING AN ARRAY ELEMENT WITH A SEQUENCE SKLEARN HOW TO#
2. #SETTING AN ARRAY ELEMENT WITH A SEQUENCE SKLEARN CODE#

ValueError: setting an array element with a sequence. > 1993 return np.asarray(self._values, dtype=dtype) usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in _array_(self, dtype)ġ992 def _array_(self, dtype: NpDtype | None = None) -> np.ndarray: > 2 clf.fit(pomt_X_train.squeeze(), pomt_Y_train)ĥ X_train_prediction = clf.predict(pomt_X_train) Check a similar question here in stackoverflow: ValueError: setting an array element with a sequence. fit() and finish the data using PCA or scaler. and next If my data was ok, what i must do before, to make it possible to do some.

#SETTING AN ARRAY ELEMENT WITH A SEQUENCE SKLEARN HOW TO#

must I need change the data format or type or something if yes, tell me must be like that, and how to be like that if possible.

#SETTING AN ARRAY ELEMENT WITH A SEQUENCE SKLEARN CODE#

My Python code is: import numpy as np import matplotlib.pyplot as plt import csv from sklearn.cluster import KMeans The following code reads the first row and terminates the loop with open trainDataXY. Its probably because '' does not have the same number of elements in all its arrays. ValueError: setting an array element with a sequence. ValueError Traceback (most recent call last) The first row is Y and remaining 644 rows are X. The above exception was the direct cause of the following exception: TypeError: only size-1 arrays can be converted to Python scalars TypeError Traceback (most recent call last) Pomt_Y_train = pomt_Y_train.astype("int")Ĭlf = OneVsOneClassifier(SVC(C = 1, verbose=True)) Pomt_X_train, pomt_X_test, pomt_Y_train, pomt_Y_test = train_test_split(pomt_train_x, pomt_train_y, test_size= (count / pomt_train_x.shape), stratify=pomt_train_y) Pomt_train_x = pd.DataFrame(columns=)įeature_dict = from testing")

You should transform all of the values as numbers before passing to TSNE.

# These are used to map the data to their appropriate column on each pass TSNE will break if you try to pass an array as one element. Model = om_pretrained('bert-base-uncased') Tokenizer = om_pretrained('bert-base-uncased',do_lower_case=True,max_length=1024) Testing_data = pd.read_csv('/dev.tsv', sep='\t') Using GridSearchCV from Sklearn: ValueError: setting an array element with a sequence. You can find the data I'm using here: training_data = pd.read_csv('/train.tsv', sep='\t') How do I work around this? I'd really appreciate some help. However, I get the error "setting an array element with a sequence," presumably because I am trying to call fit on a dataframe of arrays rather than scalar values. machine learning - Getting 'ValueError: setting an array element with a sequence.' when attempting to fit mixed-type data - Data Science Stack Exchange Getting 'ValueError: setting an array element with a sequence. I am trying to call scikit learn fit functions on dataframes where the elements of each column are numpy arrays.