547 lines
23 KiB
Python
547 lines
23 KiB
Python
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"""
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usage :
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optional :
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--num_gpu number of gpus to use will default to 1
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--epoch steps per epoch default to 256
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"""
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import tensorflow as tf
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from tensorflow.contrib.layers import l2_regularizer
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import numpy as np
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import pandas as pd
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import time
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import os
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import sys
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from params import SYS_ARGS
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from bridge import Binary
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import json
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ['CUDA_VISIBLE_DEVICES'] = "0"
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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# STEPS_PER_EPOCH = int(SYS_ARGS['epoch']) if 'epoch' in SYS_ARGS else 256
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# NUM_GPUS = 1 if 'num_gpu' not in SYS_ARGS else int(SYS_ARGS['num_gpu'])
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# BATCHSIZE_PER_GPU = 2000
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# TOTAL_BATCHSIZE = BATCHSIZE_PER_GPU * NUM_GPUS
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class void :
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pass
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class GNet :
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"""
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This is the base class of a generative network functions, the details will be implemented in the subclasses.
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An instance of this class is accessed as follows
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object.layers.normalize applies batch normalization or otherwise
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obect.get.variables instanciate variables on cpu and return a reference (tensor)
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"""
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def __init__(self,**args):
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self.layers = void()
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self.layers.normalize = self.normalize
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self.get = void()
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self.get.variables = self._variable_on_cpu
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self.NUM_GPUS = 1
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self.X_SPACE_SIZE = args['real'].shape[1] if 'real' in args else 854
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self.G_STRUCTURE = [128,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE]
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self.D_STRUCTURE = [self.X_SPACE_SIZE,256,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE*2, self.X_SPACE_SIZE] #-- change 854 to number of diagnosis
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# self.NUM_LABELS = 8 if 'label' not in args elif len(args['label'].shape) args['label'].shape[1]
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if 'label' in args and len(args['label'].shape) == 2 :
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self.NUM_LABELS = args['label'].shape[1]
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elif 'label' in args and len(args['label']) == 1 :
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self.NUM_LABELS = args['label'].shape[0]
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else:
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self.NUM_LABELS = 8
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self.Z_DIM = 128 #self.X_SPACE_SIZE
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self.BATCHSIZE_PER_GPU = args['real'].shape[0] if 'real' in args else 256
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self.TOTAL_BATCHSIZE = self.BATCHSIZE_PER_GPU * self.NUM_GPUS
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self.STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)
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self.MAX_EPOCHS = 10 if 'max_epochs' not in args else int(args['max_epochs'])
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self.ROW_COUNT = args['real'].shape[0] if 'real' in args else 100
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self.CONTEXT = args['context']
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self.ATTRIBUTES = {"id":args['column_id'] if 'column_id' in args else None,"synthetic":args['column'] if 'column' in args else None}
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self._REAL = args['real'] if 'real' in args else None
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self._LABEL = args['label'] if 'label' in args else None
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self.init_logs(**args)
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def init_logs(self,**args):
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self.log_dir = args['logs'] if 'logs' in args else 'logs'
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self.mkdir(self.log_dir)
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#
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#
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for key in ['train','output'] :
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self.mkdir(os.sep.join([self.log_dir,key]))
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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def load_meta(self,column):
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"""
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This function is designed to accomodate the uses of the sub-classes outside of a strict dependency model.
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Because prediction and training can happen independently
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"""
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_name = os.sep.join([self.out_dir,'meta-'+column+'.json'])
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if os.path.exists(_name) :
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attr = json.loads((open(_name)).read())
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for key in attr :
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value = attr[key]
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setattr(self,key,value)
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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def log_meta(self,**args) :
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object = {
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'CONTEXT':self.CONTEXT,
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'ATTRIBUTES':self.ATTRIBUTES,
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'BATCHSIZE_PER_GPU':self.BATCHSIZE_PER_GPU,
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'Z_DIM':self.Z_DIM,
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"X_SPACE_SIZE":self.X_SPACE_SIZE,
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"D_STRUCTURE":self.D_STRUCTURE,
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"G_STRUCTURE":self.G_STRUCTURE,
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"NUM_GPUS":self.NUM_GPUS,
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"NUM_LABELS":self.NUM_LABELS,
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"MAX_EPOCHS":self.MAX_EPOCHS,
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"ROW_COUNT":self.ROW_COUNT
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}
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if args and 'key' in args and 'value' in args :
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key = args['key']
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value= args['value']
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object[key] = value
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_name = os.sep.join([self.out_dir,'meta-'+SYS_ARGS['column']])
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f = open(_name+'.json','w')
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f.write(json.dumps(object))
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def mkdir (self,path):
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if not os.path.exists(path) :
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os.mkdir(path)
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def normalize(self,**args):
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"""
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This function will perform a batch normalization on an network layer
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inputs input layer of the neural network
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name name of the scope the
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labels labels (attributes not synthesized) by default None
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n_labels number of labels default None
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"""
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inputs = args['inputs']
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name = args['name']
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labels = None if 'labels' not in args else args['labels']
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n_labels= None if 'n_labels' not in args else args['n_labels']
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shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
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mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
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shape = inputs.shape[1].value
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offset_m = self.get.variables(shape=[n_labels,shape], name='offset'+name,
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initializer=tf.zeros_initializer)
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scale_m = self.get.variables(shape=[n_labels,shape], name='scale'+name,
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initializer=tf.ones_initializer)
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offset = tf.nn.embedding_lookup(offset_m, labels)
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scale = tf.nn.embedding_lookup(scale_m, labels)
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result = tf.nn.batch_normalization(inputs, mean, var, offset, scale, 1e-8)
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return result
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def _variable_on_cpu(self,**args):
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"""
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This function makes sure variables/tensors are not created on the GPU but rather on the CPU
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"""
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name = args['name']
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shape = args['shape']
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initializer=None if 'initializer' not in args else args['initializer']
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with tf.device('/cpu:0') :
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cpu_var = tf.compat.v1.get_variable(name,shape,initializer= initializer)
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return cpu_var
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def average_gradients(self,tower_grads):
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average_grads = []
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for grad_and_vars in zip(*tower_grads):
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grads = []
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for g, _ in grad_and_vars:
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expanded_g = tf.expand_dims(g, 0)
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grads.append(expanded_g)
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grad = tf.concat(axis=0, values=grads)
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grad = tf.reduce_mean(grad, 0)
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v = grad_and_vars[0][1]
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grad_and_var = (grad, v)
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average_grads.append(grad_and_var)
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return average_grads
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class Generator (GNet):
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"""
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This class is designed to handle generation of candidate datasets for this it will aggregate a discriminator, this allows the generator not to be random
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"""
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def __init__(self,**args):
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GNet.__init__(self,**args)
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self.discriminator = Discriminator(**args)
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def loss(self,**args):
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fake = args['fake']
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label = args['label']
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y_hat_fake = self.discriminator.network(inputs=fake, label=label)
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all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
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tf.add_to_collection('glosses', loss)
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return loss, loss
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def load_meta(self, column):
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super().load_meta(column)
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self.discriminator.load_meta(column)
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def network(self,**args) :
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"""
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This function will build the network that will generate the synthetic candidates
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:inputs matrix of data that we need
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:dim dimensions of ...
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"""
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x = args['inputs']
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tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
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label = args['label']
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with tf.compat.v1.variable_scope('G', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
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for i, dim in enumerate(self.G_STRUCTURE[:-1]):
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kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, dim])
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h1 = self.normalize(inputs=tf.matmul(x, kernel),shift=0, name='cbn' + str(i), labels=label, n_labels=self.NUM_LABELS)
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h2 = tf.nn.relu(h1)
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x = x + h2
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tmp_dim = dim
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i = len(self.G_STRUCTURE) - 1
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#
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# This seems to be an extra hidden layer:
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# It's goal is to map continuous values to discrete values (pre-trained to do this)
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kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, self.G_STRUCTURE[-1]])
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h1 = self.normalize(inputs=tf.matmul(x, kernel), name='cbn' + str(i),
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labels=label, n_labels=self.NUM_LABELS)
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h2 = tf.nn.tanh(h1)
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x = x + h2
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# This seems to be the output layer
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#
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kernel = self.get.variables(name='W_' + str(i+1), shape=[self.Z_DIM, self.X_SPACE_SIZE])
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bias = self.get.variables(name='b_' + str(i+1), shape=[self.X_SPACE_SIZE])
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x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
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return x
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class Discriminator(GNet):
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def __init__(self,**args):
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GNet.__init__(self,**args)
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def network(self,**args):
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"""
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This function will apply a computational graph on a dataset passed in with the associated labels and the last layer must have a single output (neuron)
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:inputs
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:label
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"""
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x = args['inputs']
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print ()
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print (x[:3,:])
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print()
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label = args['label']
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with tf.compat.v1.variable_scope('D', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
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for i, dim in enumerate(self.D_STRUCTURE[1:]):
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kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[i], dim])
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bias = self.get.variables(name='b_' + str(i), shape=[dim])
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print (["\t",bias,kernel])
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x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
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x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
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i = len(self.D_STRUCTURE)
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kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[-1], 1])
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bias = self.get.variables(name='b_' + str(i), shape=[1])
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y = tf.add(tf.matmul(x, kernel), bias)
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return y
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def loss(self,**args) :
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"""
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This function compute the loss of
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:real
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:fake
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:label
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"""
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real = args['real']
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fake = args['fake']
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label = args['label']
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epsilon = tf.random.uniform(shape=[self.BATCHSIZE_PER_GPU,1],minval=0,maxval=1)
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x_hat = real + epsilon * (fake - real)
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y_hat_fake = self.network(inputs=fake, label=label)
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y_hat_real = self.network(inputs=real, label=label)
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y_hat = self.network(inputs=x_hat, label=label)
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grad = tf.gradients(y_hat, [x_hat])[0]
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slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
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gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
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all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
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loss = w_distance + 10 * gradient_penalty + sum(all_regs)
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tf.add_to_collection('dlosses', loss)
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return w_distance, loss
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class Train (GNet):
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def __init__(self,**args):
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GNet.__init__(self,**args)
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self.generator = Generator(**args)
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self.discriminator = Discriminator(**args)
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self._REAL = args['real']
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self._LABEL= args['label']
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# print ([" *** ",self.BATCHSIZE_PER_GPU])
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self.log_meta()
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def load_meta(self, column):
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"""
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This function will delegate the calls to load meta data to it's dependents
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column name
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"""
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super().load_meta(column)
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self.generator.load_meta(column)
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self.discriminator.load_meta(column)
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def loss(self,**args):
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"""
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This function will compute a "tower" loss of the generated candidate against real data
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Training will consist in having both generator and discriminators
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:scope
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:stage
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:real
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:label
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"""
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scope = args['scope']
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stage = args['stage']
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real = args['real']
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label = args['label']
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label = tf.cast(label, tf.int32)
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#
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# @TODO: Ziqi needs to explain what's going on here
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m = [[i] for i in np.arange(self._LABEL.shape[1]-2)]
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label = label[:, 1] * len(m) + tf.squeeze(
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tf.matmul(label[:, 2:], tf.constant(m, dtype=tf.int32))
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)
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# label = label[:,1] * 4 + tf.squeeze( label[:,2]*[[0],[1],[2],[3]] )
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z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
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fake = self.generator.network(inputs=z, label=label)
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if stage == 'D':
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w, loss = self.discriminator.loss(real=real, fake=fake, label=label)
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losses = tf.get_collection('dlosses', scope)
|
||
|
|
else:
|
||
|
|
w, loss = self.generator.loss(fake=fake, label=label)
|
||
|
|
losses = tf.get_collection('glosses', scope)
|
||
|
|
|
||
|
|
total_loss = tf.add_n(losses, name='total_loss')
|
||
|
|
|
||
|
|
return total_loss, w
|
||
|
|
def input_fn(self):
|
||
|
|
"""
|
||
|
|
This function seems to produce
|
||
|
|
"""
|
||
|
|
features_placeholder = tf.compat.v1.placeholder(shape=self._REAL.shape, dtype=tf.float32)
|
||
|
|
labels_placeholder = tf.compat.v1.placeholder(shape=self._LABEL.shape, dtype=tf.float32)
|
||
|
|
dataset = tf.data.Dataset.from_tensor_slices((features_placeholder, labels_placeholder))
|
||
|
|
dataset = dataset.repeat(10000)
|
||
|
|
dataset = dataset.batch(batch_size=self.BATCHSIZE_PER_GPU)
|
||
|
|
dataset = dataset.prefetch(1)
|
||
|
|
iterator = dataset.make_initializable_iterator()
|
||
|
|
# next_element = iterator.get_next()
|
||
|
|
# init_op = iterator.initializer
|
||
|
|
return iterator, features_placeholder, labels_placeholder
|
||
|
|
|
||
|
|
def network(self,**args):
|
||
|
|
# def graph(stage, opt):
|
||
|
|
# global_step = tf.get_variable(stage+'_step', [], initializer=tf.constant_initializer(0), trainable=False)
|
||
|
|
stage = args['stage']
|
||
|
|
opt = args['opt']
|
||
|
|
tower_grads = []
|
||
|
|
per_gpu_w = []
|
||
|
|
iterator, features_placeholder, labels_placeholder = self.input_fn()
|
||
|
|
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
|
||
|
|
for i in range(self.NUM_GPUS):
|
||
|
|
with tf.device('/gpu:%d' % i):
|
||
|
|
with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
|
||
|
|
(real, label) = iterator.get_next()
|
||
|
|
loss, w = self.loss(scope=scope, stage=stage, real=self._REAL, label=self._LABEL)
|
||
|
|
tf.get_variable_scope().reuse_variables()
|
||
|
|
vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
|
||
|
|
grads = opt.compute_gradients(loss, vars_)
|
||
|
|
tower_grads.append(grads)
|
||
|
|
per_gpu_w.append(w)
|
||
|
|
|
||
|
|
grads = self.average_gradients(tower_grads)
|
||
|
|
apply_gradient_op = opt.apply_gradients(grads)
|
||
|
|
|
||
|
|
mean_w = tf.reduce_mean(per_gpu_w)
|
||
|
|
train_op = apply_gradient_op
|
||
|
|
return train_op, mean_w, iterator, features_placeholder, labels_placeholder
|
||
|
|
def apply(self,**args):
|
||
|
|
# max_epochs = args['max_epochs'] if 'max_epochs' in args else 10
|
||
|
|
REAL = self._REAL
|
||
|
|
LABEL= self._LABEL
|
||
|
|
with tf.device('/cpu:0'):
|
||
|
|
opt_d = tf.compat.v1.train.AdamOptimizer(1e-4)
|
||
|
|
opt_g = tf.compat.v1.train.AdamOptimizer(1e-4)
|
||
|
|
|
||
|
|
train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = self.network(stage='D', opt=opt_d)
|
||
|
|
train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = self.network(stage='G', opt=opt_g)
|
||
|
|
# saver = tf.train.Saver()
|
||
|
|
saver = tf.compat.v1.train.Saver()
|
||
|
|
init = tf.global_variables_initializer()
|
||
|
|
|
||
|
|
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
|
||
|
|
sess.run(init)
|
||
|
|
sess.run(iterator_d.initializer,
|
||
|
|
feed_dict={features_placeholder_d: REAL, labels_placeholder_d: LABEL})
|
||
|
|
sess.run(iterator_g.initializer,
|
||
|
|
feed_dict={features_placeholder_g: REAL, labels_placeholder_g: LABEL})
|
||
|
|
|
||
|
|
for epoch in range(1, self.MAX_EPOCHS + 1):
|
||
|
|
start_time = time.time()
|
||
|
|
w_sum = 0
|
||
|
|
for i in range(self.STEPS_PER_EPOCH):
|
||
|
|
for _ in range(2):
|
||
|
|
_, w = sess.run([train_d, w_distance])
|
||
|
|
w_sum += w
|
||
|
|
sess.run(train_g)
|
||
|
|
duration = time.time() - start_time
|
||
|
|
|
||
|
|
assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
|
||
|
|
|
||
|
|
format_str = 'epoch: %d, w_distance = %f (%.1f)'
|
||
|
|
print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
|
||
|
|
if epoch % self.MAX_EPOCHS == 0:
|
||
|
|
|
||
|
|
_name = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']])
|
||
|
|
# saver.save(sess, self.train_dir, write_meta_graph=False, global_step=epoch)
|
||
|
|
saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
|
||
|
|
#
|
||
|
|
#
|
||
|
|
|
||
|
|
class Predict(GNet):
|
||
|
|
"""
|
||
|
|
This class uses synthetic data given a learned model
|
||
|
|
"""
|
||
|
|
def __init__(self,**args):
|
||
|
|
GNet.__init__(self,**args)
|
||
|
|
self.generator = Generator(**args)
|
||
|
|
self.values = values
|
||
|
|
def load_meta(self, column):
|
||
|
|
super().load_meta(column)
|
||
|
|
self.generator.load_meta(column)
|
||
|
|
def apply(self,**args):
|
||
|
|
# print (self.train_dir)
|
||
|
|
model_dir = os.sep.join([self.train_dir,self.ATTRIBUTES['synthetic']+'-'+str(self.MAX_EPOCHS)])
|
||
|
|
demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
|
||
|
|
tf.compat.v1.reset_default_graph()
|
||
|
|
z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
|
||
|
|
y = tf.compat.v1.placeholder(shape=[self.BATCHSIZE_PER_GPU, self.NUM_LABELS], dtype=tf.int32)
|
||
|
|
ma = [[i] for i in np.arange(self.NUM_LABELS - 2)]
|
||
|
|
label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
|
||
|
|
|
||
|
|
fake = self.generator.network(inputs=z, label=label)
|
||
|
|
init = tf.compat.v1.global_variables_initializer()
|
||
|
|
saver = tf.compat.v1.train.Saver()
|
||
|
|
with tf.compat.v1.Session() as sess:
|
||
|
|
|
||
|
|
# sess.run(init)
|
||
|
|
saver.restore(sess, model_dir)
|
||
|
|
labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
|
||
|
|
|
||
|
|
labels= demo
|
||
|
|
f = sess.run(fake,feed_dict={y:labels})
|
||
|
|
#
|
||
|
|
# if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
|
||
|
|
#
|
||
|
|
|
||
|
|
df = ( pd.DataFrame(np.round(f).astype(np.int32),columns=values))
|
||
|
|
# i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
|
||
|
|
# df = (i * df).sum(axis=1)
|
||
|
|
#
|
||
|
|
# In case we are dealing with actual values like diagnosis codes we can perform
|
||
|
|
#
|
||
|
|
r = np.zeros((self.ROW_COUNT,1))
|
||
|
|
for col in df :
|
||
|
|
i = np.where(df[col])[0]
|
||
|
|
r[i] = col
|
||
|
|
df = pd.DataFrame(r,columns=[self.ATTRIBUTES['synthetic']])
|
||
|
|
|
||
|
|
return df.to_dict(orient='list')
|
||
|
|
# count = str(len(os.listdir(self.out_dir)))
|
||
|
|
# _name = os.sep.join([self.out_dir,self.CONTEXT+'-'+count+'.csv'])
|
||
|
|
# df.to_csv(_name,index=False)
|
||
|
|
|
||
|
|
|
||
|
|
# output.extend(np.round(f))
|
||
|
|
|
||
|
|
# for m in range(2):
|
||
|
|
# for n in range(2, self.NUM_LABELS):
|
||
|
|
# idx1 = (demo[:, m] == 1)
|
||
|
|
# idx2 = (demo[:, n] == 1)
|
||
|
|
# idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
|
||
|
|
# num = np.sum(idx)
|
||
|
|
# print ("_____________________")
|
||
|
|
# print (idx1)
|
||
|
|
# print (idx2)
|
||
|
|
# print (idx)
|
||
|
|
# print (num)
|
||
|
|
# print ("_____________________")
|
||
|
|
# nbatch = int(np.ceil(num / self.BATCHSIZE_PER_GPU))
|
||
|
|
# label_input = np.zeros((nbatch*self.BATCHSIZE_PER_GPU, self.NUM_LABELS))
|
||
|
|
# label_input[:, n] = 1
|
||
|
|
# label_input[:, m] = 1
|
||
|
|
# output = []
|
||
|
|
# for i in range(nbatch):
|
||
|
|
# f = sess.run(fake,feed_dict={y: label_input[i* self.BATCHSIZE_PER_GPU:(i+1)* self.BATCHSIZE_PER_GPU]})
|
||
|
|
# output.extend(np.round(f))
|
||
|
|
# output = np.array(output)[:num]
|
||
|
|
# print ([m,n,output])
|
||
|
|
|
||
|
|
# np.save(self.out_dir + str(m) + str(n), output)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__' :
|
||
|
|
#
|
||
|
|
# Now we get things done ...
|
||
|
|
column = SYS_ARGS['column']
|
||
|
|
column_id = SYS_ARGS['id'] if 'id' in SYS_ARGS else 'person_id'
|
||
|
|
df = pd.read_csv(SYS_ARGS['raw-data'])
|
||
|
|
LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
|
||
|
|
|
||
|
|
context = SYS_ARGS['raw-data'].split(os.sep)[-1:][0][:-4]
|
||
|
|
if set(['train','learn']) & set(SYS_ARGS.keys()):
|
||
|
|
|
||
|
|
df = pd.read_csv(SYS_ARGS['raw-data'])
|
||
|
|
|
||
|
|
# cols = SYS_ARGS['column']
|
||
|
|
# _map,_df = (Binary()).Export(df)
|
||
|
|
# i = np.arange(_map[column]['start'],_map[column]['end'])
|
||
|
|
max_epochs = np.int32(SYS_ARGS['max_epochs']) if 'max_epochs' in SYS_ARGS else 10
|
||
|
|
# REAL = _df[:,i]
|
||
|
|
REAL = pd.get_dummies(df[column]).astype(np.float32).values
|
||
|
|
LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
|
||
|
|
trainer = Train(context=context,max_epochs=max_epochs,real=REAL,label=LABEL,column=column,column_id=column_id)
|
||
|
|
trainer.apply()
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
#
|
||
|
|
# We should train upon this data
|
||
|
|
#
|
||
|
|
# -- we need to convert the data-frame to binary matrix, given a column
|
||
|
|
#
|
||
|
|
pass
|
||
|
|
elif 'generate' in SYS_ARGS:
|
||
|
|
values = df[column].unique().tolist()
|
||
|
|
values.sort()
|
||
|
|
p = Predict(context=context,label=LABEL,values=values)
|
||
|
|
p.load_meta(column)
|
||
|
|
r = p.apply()
|
||
|
|
print (df)
|
||
|
|
print ()
|
||
|
|
df[column] = r[column]
|
||
|
|
print (df)
|
||
|
|
|
||
|
|
else:
|
||
|
|
print (SYS_ARGS.keys())
|
||
|
|
print (__doc__)
|
||
|
|
pass
|
||
|
|
|