one-hot-encoding

问题 I am running a machine learning model (Ridge Regression w/ Cross-Validation) using scikit-learn's RidgeCV() method. My data set has 5 categorical features and 2 numerical ones, so I started with LabelEncoder() to convert the categorical features to integers, and then I applied OneHotEncoder() to make several new feature columns of 0s and 1s, in order to apply my Machine Learning model. My X_train is now a numpy array, and after fitting the model I am getting its coefficients, so I'm wondering

问题 I'm a newbie in machine learning. I have a image dataset which contains 6 classes each one with 800 train & 200 validation images. I'm using keras to train the model model. Previously I used sparse_categorical_crossentropy as loss parameter to compile the model as I was supplying integer(total no. of classes) which ran with no problem. The code as follows: import numpy as np from keras import applications from keras import Model from keras.models import Sequential from keras.layers import

问题 Im trying to solve a problem that has arisen with the productionisation of an XGBoost model. My current problem is the column order in the training data is not replicated identically in the column order in the production data I need to score. The issue has arisen from the onehot encoding step. Where not all levels of each variable are present in the production scoring data that was in the training data. This causes the scoring to come out with inconsistent and incorrect results, or the

问题 Let's say I have two dictionaries with shared and unshared keys: d1 = {'a': 1, 'b': 2} d2 = {'b': 4, 'c': 3} How would I concatenate them into a dataframe that's akin to one-hot enoding? a b c 1 2 4 3 回答1: If you want the same result as what you are showing... pd.DataFrame([d1, d2], dtype=object).fillna('') a b c 0 1 2 1 4 3 If you want to fill missing values with zero and keep a int dtype ... pd.concat(dict(enumerate(map(pd.Series, [d1, d2])))).unstack(fill_value=0) a b c 0 1 2 0 1 0 4 3 Or

问题 After using Pandas get_dummies on 3 categorical columns to get a one hot-encoded Dataframe, I've trained (with some success) a Perceptron model. Now I would like to predict the result from a new observation, that it is not hot-encoded. Is there any way to record the get_dummies column mapping to re-use it? 回答1: There is no automatic procedure to do it at the moment, to my knowledge. In the future release of sklearn CategoricalEncoder will be very handy for this job. You can already get your

问题 I am trying with a sample dataFrame : data = [['Alex','USA',0],['Bob','India',1],['Clarke','SriLanka',0]] df = pd.DataFrame(data,columns=['Name','Country','Traget']) Now from here, I used get_dummies to convert string column to an integer: column_names=['Name','Country'] one_hot = pd.get_dummies(df[column_names]) After conversion the columns are: Age,Name_Alex,Name_Bob,Name_Clarke,Country_India,Country_SriLanka,Country_USA Slicing the data. x=df[["Name_Alex","Name_Bob","Name_Clarke","Country

问题 This question already has an answer here : Split a column into multiple binary dummy columns [duplicate] (1 answer) Closed 3 years ago . I have a dataframe with a column having multiple values ( comma separated ): mydf <- structure(list(Age = c(99L, 10L, 40L, 15L), Info = c("good, bad, sad", "nice, happy, joy", "NULL", "okay, nice, fun, wild, go"), Target = c("Boy", "Girl", "Boy", "Boy")), .Names = c("Age", "Info", "Target"), row.names = c(NA, 4L), class = "data.frame") > mydf Age Info Target

问题 I'm basically trying to one hot encode a column with values like this: tickers 1 [DIS] 2 [AAPL,AMZN,BABA,BAY] 3 [MCDO,PEP] 4 [ABT,ADBE,AMGN,CVS] 5 [ABT,CVS,DIS,ECL,EMR,FAST,GE,GOOGL] ... First I got all the set of all the tickers(which is about 467 tickers): all_tickers = list() for tickers in df.tickers: for ticker in tickers: all_tickers.append(ticker) all_tickers = set(all_tickers) Then I implemented One Hot Encoding this way: for i in range(len(df.index)): for ticker in all_tickers: if

问题 I am keep getting memory issues trying to encode string labels to one-hot encoding. There are around 5 million rows and around 10000 different labels. I have tried the following but keep getting memory errors: from sklearn import preprocessing lb = preprocessing.LabelBinarizer() label_fitter = lb.fit(y) y = label_fitter.transform(y) I also tried something like this: import numpy as np def one_hot_encoding(y): unique_values = set(y) label_length = len(unique_values) enu_uniq = zip(unique

问题 I've been trying to get an LSTM (LSTM followed by a linear layer in a custom model), working in Pytorch, but was getting the following error when calculating the loss: Assertion cur_target >= 0 && cur_target < n_classes' failed. I defined the loss function with: criterion = nn.CrossEntropyLoss() and then called with loss += criterion(output, target) I was giving the target with dimensions [sequence_length, number_of_classes], and output has dimensions [sequence_length, 1, number_of_classes].