ValueError: negative dimensions are not allowed
The problem is because of size mismatch.
The train_labels
is actually is the classes of all data. The size of train
and train_labels
should match.
Learner
Updated on July 23, 2022Comments
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Learner almost 2 years
I am playing around with some data from a Kaggle competition on text_analysis, and I keep getting this rather weird error described in the title whenever I try to fit my algorithm. I looked it up, and it had something to with my matrix being to densely populated with nonzero elements while presented as a sparse matrix. I reckon this problem lies with my train_labels below in the code, the labels consist of 24 columns which isn't very common to begin with, labels are floats between 0 and 1 (including 0 and 1). Despite having some idea on what the problem is, I have no idea on how to tackle it properly, and my previous tries haven't worked out so well. Do you guys have any suggestions on how I could solve this?
Code:
import numpy as np import pandas as p import nltk from sklearn.feature_extraction.text import TfidfVectorizer import os from sklearn.linear_model import RidgeCV dir = "C:/Users/Anonymous/Desktop/KAGA FOLDER/Hashtags" def clean_the_text(data): alist = [] data = nltk.word_tokenize(data) for j in data: alist.append(j.rstrip('\n')) alist = " ".join(alist) return alist def loop_data(data): for i in range(len(data)): data[i] = clean_the_text(data[i]) return data if __name__ == "__main__": print("loading data") train_text = loop_data(list(np.array(p.read_csv(os.path.join(dir,"train.csv")))[:,1])) test_set = loop_data(list(np.array(p.read_csv(os.path.join(dir,"test.csv")))[:,1])) train_labels = np.array(p.read_csv(os.path.join(dir,"train.csv")))[:,4:] #Vectorizing vectorizer = TfidfVectorizer(max_features = 10000,strip_accents = "unicode",analyzer = "word") ridge_classifier = RidgeCV(alphas = [0.001,0.01,0.1,1,10]) all_data = train_text + test_set train_length = len(train_text) print("fitting Vectorizer") vectorizer.fit(all_data) print("transforming text") all_data = vectorizer.transform(all_data) train = all_data[:train_length] test = all_data[train_length:] print("fitting and selecting models") ridge_classifier.fit(train,train_labels) print("predicting") pred = ridge_classifier.predict(test) np.savetxt(dir +"submission.csv", pred, fmt = "%d", delimiter = ",") print("submission_file created")
Traceback:
Traceback (most recent call last): File "C:\Users\Anonymous\workspace\final_submission\src\linearSVM.py", line 56, in <module> ridge_classifier.fit(train,train_labels) File "C:\Python27\lib\site-packages\sklearn\linear_model\ridge.py", line 817, in fit estimator.fit(X, y, sample_weight=sample_weight) File "C:\Python27\lib\site-packages\sklearn\linear_model\ridge.py", line 724, in fit v, Q, QT_y = _pre_compute(X, y) File "C:\Python27\lib\site-packages\sklearn\linear_model\ridge.py", line 609, in _pre_compute K = safe_sparse_dot(X, X.T, dense_output=True) File "C:\Python27\lib\site-packages\sklearn\utils\extmath.py", line 78, in safe_sparse_dot ret = a * b File "C:\Python27\lib\site-packages\scipy\sparse\base.py", line 303, in __mul__ return self._mul_sparse_matrix(other) File "C:\Python27\lib\site-packages\scipy\sparse\compressed.py", line 520, in _mul_sparse_matrix indices = np.empty(nnz, dtype=np.intc) ValueError: negative dimensions are not allowed
I suspect that my labels are the problem, so here are the labels:
In [12]: undefined import pandas as pd import numpy as np import os dir = "C:\Users\Anonymous\Desktop\KAGA FOLDER\Hashtags" labels = np.array(pd.read_csv(os.path.join(dir,"train.csv")))[:,4:] labels Out[12]: array([[0.0, 0.0, 1.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, ..., 0.0, 0.0, 0.0], ..., [0.0, 0.0, 0.0, ..., 1.0, 0.0, 0.0], [0.0, 0.385, 0.41, ..., 0.0, 0.0, 0.0], [0.0, 0.20199999999999999, 0.395, ..., 0.0, 0.0, 0.0]], dtype=object) In [13]: undefined labels.shape Out[13]: (77946L, 24L)
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Learner over 10 yearsI'm sorry, but I'm a bit confused. Since I'm getting the labels out of the train.csv shouldn't the size match with train?
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shan.B over 10 yearsYou are reading form train.csv, but you are doing some processes on the training data. You append training and test data into
all_data
and get a part of it as train. How you obtaintrain_length
seems confusing to me. Please try to print the size oftrain
this would verify or disprove my theory. -
Learner over 10 yearsThe shape of train is: (77946, 10000).
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shan.B over 10 yearsDo you really have 10000 attributes on a classification, that's big.
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Learner over 10 yearsNo, there I was only using one attribute, while the whole dataset has 3 attributes. So I'm definitely screwing up here. Thanks for pointing this out!
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Learner over 10 yearsOk never mind, I just made a mistake while initializing a variable. Checked my data reading and that seems fine to.