one-hot-encoding

问题 I am using apache Spark ML lib to handle categorical features using one hot encoding. After writing the below code I am getting a vector c_idx_vec as output of one hot encoding. I do understand how to interpret this output vector but I am unable to figure out how to convert this vector into columns so that I get a new transformed dataframe.Take this dataset for example: >>> fd = spark.createDataFrame( [(1.0, "a"), (1.5, "a"), (10.0, "b"), (3.2, "c")], ["x","c"]) >>> ss = StringIndexer

问题 I'm using Julia's DataFrames.jl package. In it, I have a dataframe with a columns containing a list of strings (e.g. ["Type A", "Type B", "Type D"]). How does one then performs a one-hot encoding? I wasn't able to find a pre-built function in the DataFrames.jl package. Here is an example of what I want to do: Original Dataframe col1 | col2 | 102 |[a] | 103 |[a,b] | 102 |[c,b] | After One-hot encoding col1 | a | b | c | 102 | 1 | 0 | 0 | 103 | 1 | 1 | 0 | 102 | 0 | 1 | 1 | 回答1: It is easy

问题 Let, in my dataframe df, I have a column my_category in which I have different values, and I can view the value counts using: df.groupBy("my_category").count().show() value count a 197 b 166 c 210 d 5 e 2 f 9 g 3 Now, I'd like to apply One Hot Encoding (OHE) on this column, but for the top N frequent values only (say N = 3 ), and put all the rest infrequent values in a dummy column (say, "default"). E.g., the output should be something like: a b c default 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 ... 0

问题 Let, in my dataframe df, I have a column my_category in which I have different values, and I can view the value counts using: df.groupBy("my_category").count().show() value count a 197 b 166 c 210 d 5 e 2 f 9 g 3 Now, I'd like to apply One Hot Encoding (OHE) on this column, but for the top N frequent values only (say N = 3 ), and put all the rest infrequent values in a dummy column (say, "default"). E.g., the output should be something like: a b c default 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 ... 0

问题 I am trying to predict 'Full_Time_Home_Goals' My code is: import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_absolute_error from sklearn.ensemble import RandomForestRegressor import os import xlrd import datetime import numpy as np # Set option to display all the rows and columns in the dataset. If there are more rows, adjust number accordingly. pd.set_option('display.max_rows', 5000) pd.set

问题 I want to run a RF classification just like it's specified in 'randomForest' but still use the k-fold repeated cross validation method (code below). How do I stop caret from creating dummy variables out of my categorical ones? I read that this may be due to One-Hot-Encoding, but not sure how to change this. I would be very greatful for some example lines on how to fix this! database: > str(river) 'data.frame': 121 obs. of 13 variables: $ stat_bino : Factor w/ 2 levels "0","1": 2 2 1 1 2 2 2 2

问题 I have extension(example .exe,.py,.xml,.doc etc) table in my dataframe. after running on terminal I am getting above error on large data set. encoder = OneHotEncoder(handle_unknown='ignore') encoder.fit(features['Extension'].values.reshape(-1, 1)) temp = encoder.transform(features['Extension'].values.reshape(-1, 1)).toarray() #GETTING ERROR on this print("Size of array in bytes",getsizeof(temp)) print("Array :-",temp) print("Shape :- ",features.shape, temp.shape) features.drop(columns=[

问题 Why I am getting same results for different words? import keras keras.__version__ '1.0.0' import theano theano.__version__ '0.8.1' from keras.preprocessing.text import one_hot one_hot('START', 43) [26] one_hot('children', 43) [26] 回答1: unicity non-guaranteed in one hot encoding see one hot keras documentation 回答2: From the Keras source code, you can see that the words are hashed modulo the output dimension (43, in your case): def one_hot(text, n, filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n'

问题 I am wondering how I can keep track of the original columns of a dataset once I perform data preprocessing on it? In the below code df_columns would tell me that column 0 in df_array is A , column 1 is B and so forth... However when once I encode categorical column B df_columns is no longer valid for keeping track of df_dummies import pandas as pd import numpy as np animal = ['dog','cat','horse'] df = pd.DataFrame({'A': np.random.rand(9), 'B': [animal[np.random.randint(3)] for i in range(9)],

问题 Could anyone help with an an explanation of what axis is in TensorFlow 's one_hot function? According to the documentation: axis: The axis to fill (default: -1, a new inner-most axis) Closest I came to an answer on SO was an explanation relevant to Pandas: Not sure if the context is just as applicable. 回答1: Here's an example: x = tf.constant([0, 1, 2]) ... is the input tensor and N=4 (each index is transformed into 4D vector). axis=-1 Computing one_hot_1 = tf.one_hot(x, 4).eval() yields a (3,