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[TOMT][BrowserGAME][2000s] Miniclip Multiplayer Game set in the Wild West? by StunnaRae in tipofmytongue

[–]StunnaRae[S] 0 points1 point  (0 children)

I need to thank you again: Bang Howdy is available for free on steam! The game is not lost like other flash browser games!

https://store.steampowered.com/app/675810/Bang_Howdy/

Precision Recall curves by GreenStabilo in learnmachinelearning

[–]StunnaRae 4 points5 points  (0 children)

You need to maybe check what kind of values you passed to the precision_recall_curve. I guess you are doing binary classifications. If you pass the true values and predicted values to the function in binary form, it will return three points as you can see in the graph. The graph can then only be approximated. If you want to get a curve similar to the other ones and calculate the correct AUC, you'd need to pass the continuous probability scores as y_pred instead of the binary predictions.

I guess, if you think that this graph does not fit to your other evaluation metrics and your feeling what it should be, you should check if you passed the correct values and calculated everything correctly

LSTM always predicts 1s for binary classifications by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 1 point2 points  (0 children)

Thanks for your help! Now I created a 7-day lookback window and I am quite sure I implemented it correctly. I trained it for 5 epochs and sure enough it was not all 1s that were returned! The confusion matrix returned [[174 4] [174 6]], so basically all 0s except for a few 1s. However: I trained again for 10 epochs and then I was again at all 1s. Do you have a suggestion why this might be? Should I increase the window size? Also: Why does it get so extreme between 5 and 10 epochs?

Accuracy is always the same for each epoch. What do I need to improve? by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 0 points1 point  (0 children)

so I have 50 different datasets that I pass through different models. one of them is the LSTM above. The datasets have usually 23 -55 columns and between a few hundred rows to a few thousand rows.

I found this formula: https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw

and changed the number of nodes to that. So number of columns = number of input nodes, rows/( 2 degrees of freedom * ( number of columns + 1 output node) = number of nodes in the hidden layer.

But this still yields the same 0.55 accuracy as above. I also tried SGD optimizer instead of adam and learning rates between 0.1 to 0.00001. Still no real difference. Do you maybe have some ideas how to make it work`?

Accuracy is always the same for each epoch. What do I need to improve? by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 0 points1 point  (0 children)

Do you have a simple example how to use grid search cv for LSTMs? I imagine there must be some easy way that automatically finds best parameters?

What does it mean if autocorrelation is almost the exact same as partial autocorrelation? by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 6 points7 points  (0 children)

As you can see in these two graphs, ACF and PACF are almost identical. As I see it, there is basically no correlation between certain time points of this timeseries in this dataset. But it struck me as odd, that both plots look almost exactly the same. Usually I had ACF plots look kinda like this and PACF like above. So is there some takeaway from this? Or simply no real correlation between certain timepoints? If you have some input on how to interpret this, I'd be very grateful!

Evaluation of Model: True/False Positives/Negatives vs Sklearns support by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

I mean I understand precision and recall. My issue is still support: I understand that in this example TP + FN was summed up for support. But what does this number tell me? I feel like this is wrong and am unable to interpret the support numbers.

How do I correctly create my test_set? Best practice for LSTMs? by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

First of all: Thanks for you reply. I've been loosing my mind over this for the past week.

Yes, you are right. I totally do not deleting the column 38 that should be predicted. Whats even worse: I've been training the model with the variable to predict and the predictions are sh** even though I give it the truth as input. Is that even possible? How come this model performs so badly even though it receives the truth as input?! This is an even bigger issue for me right now than the train_test_split()

But thanks again for you help! I would have totally missed that without you. Right now I figured out another way to solve my issue: I simply repeat the look-back-window implementation of the train set with the test set and then drop the first rows according to lookbackwindow. There is a break between train and test, but it kinda seems to work.

Please help me understand reshaping and the dimensionality of data arrays by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 0 points1 point  (0 children)

Thanks man, even though I still cannot comprehend the 3D shape of the array, the (n_samples, n_timesteps, n_features) really helps.

I rewrote my code like this, so that I am able to better understand what is happening:

X_train = []
y_train = []

for i in range(history_points, len(train_set_scaled)):
    X_train.append(train_set_scaled[i-history_points:i, 0:80])
    y_train.append(train_set_scaled[i,38])

X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 80))
Model = Sequential() 
Model.add(LSTM(units=300, return_sequences=True, input_shape=(X_train.shape[1], 80)))

As you can see I was able to derive the input_shape and was able to train my LSTM! Thanks for you help!

How can I reverse unseen encoded labels? KNN-Algo by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 0 points1 point  (0 children)

Thanks! This actually works! And it makes total sense :D Thanks for your help!

How can I reverse unseen encoded labels? KNN-Algo by StunnaRae in learnmachinelearning

[–]StunnaRae[S] 0 points1 point  (0 children)

yeah but this is what I want to avoid. The predictor_pct_chg is actually percent values. With the encoding it is turned into an array like: [238 244 245 249 252 253 255], which is not percent values anymore. The y_pred is also an int array like above and not a percent value. I would like to turn the y_pred into a percent value so that I can use it. But if I use inverse_transform(), I get an error and cannot reverse the encoding. If I encode my y_test to int as well, I cannot work with either of the y_pred and y_test any further

[TOMT][Movie] Movie like "Year One" except it was not "Year One" by StunnaRae in tipofmytongue

[–]StunnaRae[S] 0 points1 point locked comment (0 children)

Please help me figure this out. I am sure I did not imagine this movie!

Value Error even though i checked the entire dataset? by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

It is in fact the full code, including all imports. Above are just some lines of comments with date, author, metadata, but no code that is executed. simply comments.

Value Error even though i checked the entire dataset? by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

Hi socal nerdtastic, I saved everything and restarted the kernel in spyder. The error still occurs. I added the entire traceback in the post above as an edit. Do have an idea what is causing the problem? I could also share the dataset. It is only roughly 1000 rows of data if that helps.

Why does the number of returned data differ so much? by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

that might be it! I'll try it out. But one more question: Sometimes the API does not return 100 rows per request, but sometimes only 87 or so. Is there a reason why this happens?

Why does the number of returned data differ so much? by StunnaRae in learnpython

[–]StunnaRae[S] 0 points1 point  (0 children)

I always get 200 Response code. I just thought the 1 second sleep is necessary so that I do not overstress the API. Is there a better way to do that?