classifier | Organize files in your directory | Runtime Evironment library
kandi X-RAY | classifier Summary
kandi X-RAY | classifier Summary
Organize files in your directory instantly, by classifying them into different folders
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Top functions reviewed by kandi - BETA
- Main function
- Classify formats
- Create the default config file
- Find files by date
- Move a file to another folder
- Format text argument
- Format argument
- Check configuration file
classifier Key Features
classifier Examples and Code Snippets
Lines of Code : 51
License : No License
Copydef main():
Xtrain, Ytrain, Xtest, Ytest, word2idx = get_data()
# convert to numpy arrays
Xtrain = np.array(Xtrain)
Ytrain = np.array(Ytrain)
# convert Xtrain to indicator matrix
N = len(Xtrain)
V = len(word2idx) + 1
def classifier(train_data, train_target, classes, point, k=5):
"""
Classifies the point using the KNN algorithm
k closest points are found (ranked in ascending order of euclidean distance)
Params:
:train_data: Set of points that a def fit(self, X, Y, M=None):
N, D = X.shape
if M is None:
M = int(np.sqrt(D))
self.models = []
self.features = []
for b in range(self.B):
tree = DecisionTreeClassifier()
# sample features
features = np.ra class Human():
def __init__(self):
self.family = 'Hominidae'
self.order = 'Primate'
class Bear():
def __init__(self):
self.family = 'Ursidae'
self.order = 'Carnivora'
class Lion():
def __init__m = xgb.XGBClassifier(max_depth=2, n_estimators=3).fit(X, y)
m.get_booster().get_dump()
m.get_booster().dump_model("out.txt")
m.get_booster().trees_to_dataframe()
import os
from statistics import mean
from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.model_selection import cross_val_score
import sklweka.jvm as jvm
from sklweka.cclass_weights = compute_class_weight(
class_weight = "balanced",
classes = np.unique(train_classes),
y = train_classes arch = models.alexnet(); pic_x = 227
dummy_input = torch.zeros((1,3, pic_x, pic_x))
torch.onnx.export(arch, dummy_input, "alexnet.onnx", verbose=True, export_params=True, )
graph(%input.1 : Float(1, 3, 2predict_x=model.predict(X_test)
classes_x=np.argmax(predict_x,axis=1)
for param in model.base_model.parameters():
param.requires_grad = False
Community Discussions
Trending Discussions on classifier
QUESTION
The classifier script I wrote is working fine and recently added weight balancing to the fitting. Since I added the weight estimate function using 'sklearn' library I get the following error :
...ANSWER
Answered 2022-Mar-27 at 23:14After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.
QUESTION
Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/
This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?
I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:
...ANSWER
Answered 2021-Aug-19 at 03:49This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference
update to
QUESTION
I try to implement a fully-connected model for classification using the MNIST dataset. A part of the code is the following:
...ANSWER
Answered 2022-Mar-10 at 08:19You could start off with a custom training loop using tf.GradientTape:
QUESTION
There was introduced a new feature Gradle managed devices (see for example here: https://developer.android.com/studio/preview/features?hl=fr)
The setup seems to be pretty straightforward, just copy a few lines to the module level build.gradle file and everything should work.
Sadly it is not the case for me and I strive for some advice, please. The code is red and the script doesn't succeed. See my build.gradle.kts file:
The underlined ManagedVirtualDevice shows the following error:
My Android studio version is Android Studio Bumblebee | 2021.1.1 Canary 11 Build #AI-211.7628.21.2111.7676841, built on August 26, 2021.
Syncing Gradle shows this:
...ANSWER
Answered 2021-Oct-15 at 11:43Just ran into the same issue - you need to instantiate a ManagedVirtualDevice object and configure it, before adding it to your devices list:
QUESTION
I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:
AttributeError: module
'__main__'has no attribute 'GensimWord2VecVectorizer'
I am creating the model on Google Colab.
Code in Jupyter Notebook:
...ANSWER
Answered 2022-Feb-24 at 11:48Import GensimWord2VecVectorizer in your Flask Web app python file.
QUESTION
This worked fine for me be building under Java 8. Now under Java 17.01 I get this when I do mvn deploy.
mvn install works fine. I tried 3.6.3 and 3.8.4 and updated (I think) all my plugins to the newest versions.
Any ideas?
...ANSWER
Answered 2022-Feb-11 at 22:39Update: Version 1.6.9 has been released and should fix this issue! 🎉
This is actually a known bug, which is now open for quite a while: OSSRH-66257. There are two known workarounds:
1. Open ModulesAs a workaround, use --add-opens to give the library causing the problem access to the required classes:
QUESTION
I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.
ANSWER
Answered 2022-Feb-02 at 08:53I tried with scikeras but I got errors because it doesn't accept not-numerical inputs (in our case the input is in str format). So I came back to the standard keras wrapper.
The focal point here is that the model is not built correctly. The TextVectorization must be put inside the Sequential model like shown in the official documentation.
So the build_model function becomes:
QUESTION
I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.
However, I run into the following error:
...ANSWER
Answered 2021-Dec-31 at 08:18As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this
set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1
QUESTION
I have built a pixel classifier for images, and for each pixel in the image, I want to define to which pre-defined color cluster it belongs. It works, but at some 5 minutes per image, I think I am doing something unpythonic that can for sure be optimized.
How can we map the function directly over the list of lists?
...ANSWER
Answered 2021-Jul-23 at 07:41Just quick speedups:
- You can omit
math.sqrt() - Create dictionary of colors instead of a list (that way you don't have to search for the index each iteration)
- use
min()instead ofsorted()
QUESTION
I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.
I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data:
ANSWER
Answered 2021-Dec-17 at 15:33In your example, you're using a DecisionTreeClassifier which by default support targets of dimension (n, m) where m > 1.
However if you want to have as result the marginal probability of each class then use the OneVsRestClassifier.
Notice that CalibratedClassifierCV expects target to be 1d so the "trick" is to extend it to support Multilabel Classification with MultiOutputClassifier.
Full Example
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Python 2.7 / Python 3.4
Linux / OSX / Windows
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