one-hot-encoding

How can I handle unknown values for label encoding in sk-learn? The label encoder will only blow up with an exception that new labels were detected. What I want is the encoding of categorical variables via one-hot -encoder. However, sk-learn does not support strings for that. So I used a label encoder on each column. My problem is that in my cross-validation step of the pipeline unknown labels show up. The basic one-hot-encoder would have the option to ignore such cases. An apriori pandas.getDummies /cat.codes is not sufficient as the pipeline should work with real-life, fresh incoming data

I am new to scikit-learn library and have been trying to play with it for prediction of stock prices. I was going through its documentation and got stuck at the part where they explain OneHotEncoder() . Here is the code that they have used : >>> from sklearn.preprocessing import OneHotEncoder >>> enc = OneHotEncoder() >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]]) OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>, handle_unknown='error', n_values='auto', sparse=True) >>> enc.n_values_ array([2, 3, 4]) >>> enc.feature_indices_ array([0, 2, 5, 9]) >>> enc.transform

I have a dataframe looks like this: JOINED_CO GENDER EXEC_FULLNAME GVKEY YEAR CONAME BECAMECEO REJOIN LEFTOFC LEFTCO RELEFT REASON PAGE CO_PER_ROL 5622 NaN MALE Ira A. Eichner 1004 1992 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1993 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1994 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1995 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1996 AAR CORP 19550101 NaN 19961001

I have a list of label names which I enuemrated and created a dictionary: my_list = [b'airplane', b'automobile', b'bird', b'cat', b'deer', b'dog', b'frog', b'horse', b'ship', b'truck'] label_dict =dict(enumerate(my_list)) {0: b'airplane', 1: b'automobile', 2: b'bird', 3: b'cat', 4: b'deer', 5: b'dog', 6: b'frog', 7: b'horse', 8: b'ship', 9: b'truck'} Now I'm trying to cleaning map / apply the dict value to my target which is in an one-hot-encoded form. y_test[0] array([ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]) y_test[0].map(label_dict) should return: 'cat' I was playing around with (lambda key

What is the best way to make this One Hot encoded matrix array([[[1, 0, 0], [1, 0, 0], [0, 1, 0]], [[0, 0, 1], [0, 1, 0], [1, 0, 0]]]) as array([[0, 0, 1], [2, 1, 0]]) In other words, how to decode One Hot array? Use np.argmax along axis=2 - a.argmax(2) Sample run - In [186]: a Out[186]: array([[[1, 0, 0], [1, 0, 0], [0, 1, 0]], [[0, 0, 1], [0, 1, 0], [1, 0, 0]]]) In [187]: a.argmax(2) Out[187]: array([[0, 0, 1], [2, 1, 0]]) 来源： https://stackoverflow.com/questions/43017783/python-numpy-one-hot-to-regions

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 -- is there a straightforward way to connect these coefficients back to the individual features they

I have multi-class classification using RNN and here is my main code for RNN: def RNN(x, weights, biases): x = tf.unstack(x, input_size, 1) lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) stacked_lstm = rnn.MultiRNNCell([lstm_cell]*lstm_size, state_is_tuple=True) outputs, states = tf.nn.static_rnn(stacked_lstm, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights) + biases logits = RNN(X, weights, biases) prediction = tf.nn.softmax(logits) cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y)) optimizer = tf.train