bug fixes: design improvements
This commit is contained in:
Steve Nyemba
parent
71097103da
commit
97bae5ef92
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@ -1,2 +1,17 @@
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import data.params as params
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from data.params import SYS_ARGS
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import transport
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from multiprocessing import Process, Queue
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from data.maker import prepare
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class Trainer (Process) :
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pass
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class Maker(Process):
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pass
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if __name__ == '__main__' :
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logger = transport.factory.instance(SYS_ARGS['store']['logger'])
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223
data/gan.py
223
data/gan.py
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@ -111,15 +111,15 @@ class GNet :
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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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if self.logger :
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#
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# We will clear the logs from the data-store
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#
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column = self.ATTRIBUTES['synthetic']
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db = self.logger.db
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if db[column].count() > 0 :
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db.backup.insert({'name':column,'logs':list(db[column].find()) })
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db[column].drop()
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# if self.logger :
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# We will clear the logs from the data-store
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# column = self.ATTRIBUTES['synthetic']
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# db = self.logger.db
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# if db[column].count() > 0 :
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# db.backup.insert({'name':column,'logs':list(db[column].find()) })
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# db[column].drop()
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def load_meta(self,column):
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"""
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@ -127,7 +127,7 @@ class GNet :
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Because prediction and training can happen independently
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"""
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# suffix = "-".join(column) if isinstance(column,list)else column
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suffix = self.get.suffix()
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suffix = self.CONTEXT #self.get.suffix()
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_name = os.sep.join([self.out_dir,'meta-'+suffix+'.json'])
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if os.path.exists(_name) :
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attr = json.loads((open(_name)).read())
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@ -159,7 +159,7 @@ class GNet :
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value= args['value']
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object[key] = value
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# suffix = "-".join(self.column) if isinstance(self.column,list) else self.column
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suffix = self.get.suffix()
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suffix = self.CONTEXT #self.get.suffix()
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_name = os.sep.join([self.out_dir,'meta-'+suffix])
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f = open(_name+'.json','w')
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@ -351,7 +351,7 @@ class Train (GNet):
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self.discriminator = Discriminator(**args)
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self._REAL = args['real']
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self._LABEL= args['label'] if 'label' in args else None
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self.column = args['column']
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# self.column = args['column']
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# print ([" *** ",self.BATCHSIZE_PER_GPU])
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self.meta = self.log_meta()
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@ -438,6 +438,11 @@ class Train (GNet):
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per_gpu_w = []
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iterator, features_placeholder, labels_placeholder = self.input_fn()
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with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
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#
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# @TODO: Find a way to handle this across multiple CPU in case the GPU are not available
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# - abstract hardware specification
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# - determine if the GPU/CPU are busy
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#
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for i in range(self.NUM_GPUS):
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with tf.device('/gpu:%d' % i):
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with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
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@ -510,7 +515,7 @@ class Train (GNet):
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# if epoch % self.MAX_EPOCHS == 0:
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if epoch in [5,10,20,50,75, self.MAX_EPOCHS] :
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# suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
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suffix = self.get.suffix()
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suffix = self.CONTEXT #self.get.suffix()
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_name = os.sep.join([self.train_dir,suffix])
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# saver.save(sess, self.train_dir, write_meta_graph=False, global_step=epoch)
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saver.save(sess, _name, write_meta_graph=False, global_step=epoch)
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@ -539,7 +544,8 @@ class Predict(GNet):
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# self.MISSING_VALUES = np.nan_to_num(np.nan)
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# if 'no_value' in args and args['no_value'] not in ['na','','NA'] :
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# self.MISSING_VALUES = args['no_value']
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self.MISSING_VALUES = args['missing']
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self.MISSING_VALUES = args['missing'] if 'missing' in args else []
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# self.MISSING_VALUES = args['no_value']
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# self.MISSING_VALUES = int(args['no_value']) if args['no_value'].isnumeric() else np.na if args['no_value'] in ['na','NA','N/A'] else args['no_value']
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@ -548,9 +554,56 @@ class Predict(GNet):
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self.generator.load_meta(column)
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self.ROW_COUNT = self.oROW_COUNT
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def apply(self,**args):
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suffix = self.CONTEXT #self.get.suffix()
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model_dir = os.sep.join([self.train_dir,suffix+'-'+str(self.MAX_EPOCHS)])
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demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
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#
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# setup computational graph
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tf.compat.v1.reset_default_graph()
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z = tf.random.normal(shape=[self.ROW_COUNT, self.Z_DIM])
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y = tf.compat.v1.placeholder(shape=[self.ROW_COUNT, self.NUM_LABELS], dtype=tf.int32)
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if self._LABEL is not None :
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ma = [[i] for i in np.arange(self.NUM_LABELS - 2)]
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label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
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else:
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label = None
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fake = self.generator.network(inputs=z, label=label)
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init = tf.compat.v1.global_variables_initializer()
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saver = tf.compat.v1.train.Saver()
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df = pd.DataFrame()
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CANDIDATE_COUNT = args['candidates'] if 'candidates' in args else 1 #0 if self.ROW_COUNT < 1000 else 100
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candidates = []
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with tf.compat.v1.Session() as sess:
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saver.restore(sess, model_dir)
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if self._LABEL is not None :
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# labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
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labels= demo
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else:
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labels = None
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for i in np.arange(CANDIDATE_COUNT) :
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if labels :
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_matrix = sess.run(fake,feed_dict={y:labels})
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else:
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_matrix = sess.run(fake)
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#
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# if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
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# The code below will insure we have some acceptable cardinal relationships between id and synthetic values
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#
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# df = pd.DataFrame(np.round(f)).astype(np.int32)
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candidates.append (np.round(_matrix).astype(np.int64))
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# return candidates[0] if len(candidates) == 1 else candidates
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return candidates
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def _apply(self,**args):
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# print (self.train_dir)
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# suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
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suffix = self.get.suffix()
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suffix = self.CONTEXT #self.get.suffix()
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model_dir = os.sep.join([self.train_dir,suffix+'-'+str(self.MAX_EPOCHS)])
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demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
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tf.compat.v1.reset_default_graph()
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@ -567,11 +620,12 @@ class Predict(GNet):
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init = tf.compat.v1.global_variables_initializer()
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saver = tf.compat.v1.train.Saver()
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df = pd.DataFrame()
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CANDIDATE_COUNT = 10 #0 if self.ROW_COUNT < 1000 else 100
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CANDIDATE_COUNT = 5 #0 if self.ROW_COUNT < 1000 else 100
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NTH_VALID_CANDIDATE = count = np.random.choice(np.arange(2,60),2)[0]
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with tf.compat.v1.Session() as sess:
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# sess.run(init)
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saver.restore(sess, model_dir)
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if self._LABEL is not None :
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labels = np.zeros((self.ROW_COUNT,self.NUM_LABELS) )
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@ -585,109 +639,110 @@ class Predict(GNet):
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__ratio=0
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for i in np.arange(CANDIDATE_COUNT) :
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if labels :
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f = sess.run(fake,feed_dict={y:labels})
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_matrix = sess.run(fake,feed_dict={y:labels})
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else:
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f = sess.run(fake)
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_matrix = sess.run(fake)
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#
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# if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
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# The code below will insure we have some acceptable cardinal relationships between id and synthetic values
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#
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# df = pd.DataFrame(np.round(f)).astype(np.int32)
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df = pd.DataFrame(np.round(f),dtype=int)
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found.append (np.round(_matrix).astype(np.int64))
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# df = pd.DataFrame(np.round(_matrix),dtype=int)
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p = 0 not in df.sum(axis=1).values
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x = df.sum(axis=1).values
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# x = df.sum(axis=1).values
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if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size :
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ratio.append(np.divide( np.sum(x), x.size))
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found.append(df)
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# if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size :
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# ratio.append(np.divide( np.sum(x), x.size))
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# found.append(df)
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# break
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if len(found) == CANDIDATE_COUNT:
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# # break
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# if len(found) == CANDIDATE_COUNT:
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break
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else:
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__x__ = df if __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __x__
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__ratio = np.divide( np.sum(x), x.size) if __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __ratio
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continue
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# break
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# else:
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# __x__ = df if __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __x__
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# __ratio = np.divide( np.sum(x), x.size) if __x__ is None or np.where(x > 0)[0].size > np.where(__x__ > 0)[0].size else __ratio
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# continue
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# i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
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# df = (i * df).sum(axis=1)
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#
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# In case we are dealing with actual values like diagnosis codes we can perform
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#
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N = len(found)
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_index = [i for i in range(0,N) if found[i].shape[1] == len(self.values)]
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if not _index and not found :
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df = __x__
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INDEX = -1
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else :
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if not _index :
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INDEX = np.random.choice(np.arange(len(found)),1)[0]
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INDEX = ratio.index(np.max(ratio))
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else:
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INDEX = _index[0]
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# N = len(found)
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# _index = [i for i in range(0,N) if found[i].shape[1] == len(self.values)]
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# if not _index and not found :
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# df = __x__
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# INDEX = -1
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# else :
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# if not _index :
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# INDEX = np.random.choice(np.arange(len(found)),1)[0]
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# INDEX = ratio.index(np.max(ratio))
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# else:
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# INDEX = _index[0]
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df = found[INDEX]
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columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]
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# df = found[INDEX]
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# columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]
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# r = np.zeros((self.ROW_COUNT,len(columns)))
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# r = np.zeros(self.ROW_COUNT)
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if self.logger :
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info = {"found":len(found),"rows":df.shape[0],"cols":df.shape[1],"expected":len(self.values)}
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if df.shape[1] > len(self.values) :
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df = df.iloc[:len(self.values)]
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if INDEX > 0 :
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info =dict(info ,**{"selected":INDEX, "ratio": ratio[INDEX] })
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else :
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# if self.logger :
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# info = {"found":len(found),"rows":df.shape[0],"cols":df.shape[1],"expected":len(self.values)}
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# if df.shape[1] > len(self.values) :
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# df = df.iloc[:len(self.values)]
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# if INDEX > 0 :
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# info =dict(info ,**{"selected":INDEX, "ratio": ratio[INDEX] })
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# else :
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info['selected'] = -1
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info['ratio'] = __ratio
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info['partition'] = self.PARTITION
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self.logger.write({"module":"gan-generate","action":"generate","input":info})
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# df.columns = self.values
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if len(found) or df.columns.size <= len(self.values):
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ii = df.apply(lambda row: np.sum(row) == 0 ,axis=1)
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missing = []
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if ii.sum() > 0 :
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#
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# If the generator had a reductive effect we should be able to get random values from either :
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# - The space of outliers
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# - existing values for smaller spaces that have suffered over training
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#
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# info['selected'] = -1
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# info['ratio'] = __ratio
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# info['partition'] = self.PARTITION
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# self.logger.write({"module":"gan-generate","action":"generate","input":info})
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# # df.columns = self.values
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# if len(found) or df.columns.size <= len(self.values):
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# ii = df.apply(lambda row: np.sum(row) == 0 ,axis=1)
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# missing = []
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# if ii.sum() > 0 :
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# #
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# # If the generator had a reductive effect we should be able to get random values from either :
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# # - The space of outliers
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# # - existing values for smaller spaces that have suffered over training
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# #
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N = ii.sum()
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missing_values = self.MISSING_VALUES if self.MISSING_VALUES else self.values
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missing = np.random.choice(missing_values,N)
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# missing = []
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#
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# @TODO:
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# Log the findings here in terms of ratio, missing, candidate count
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# print ([np.max(ratio),len(missing),len(found),i])
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i = np.where(ii == 0)[0]
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# N = ii.sum()
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# missing_values = self.MISSING_VALUES if self.MISSING_VALUES else self.values
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# missing = np.random.choice(missing_values,N)
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# # missing = []
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# #
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# # @TODO:
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# # Log the findings here in terms of ratio, missing, candidate count
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# # print ([np.max(ratio),len(missing),len(found),i])
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# i = np.where(ii == 0)[0]
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df = pd.DataFrame( df.iloc[i].apply(lambda row: self.values[np.random.choice(np.where(row != 0)[0],1)[0]] ,axis=1))
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df.columns = columns
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df = df[columns[0]].append(pd.Series(missing))
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# df = pd.DataFrame( df.iloc[i].apply(lambda row: self.values[np.random.choice(np.where(row != 0)[0],1)[0]] ,axis=1))
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# df.columns = columns
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# df = df[columns[0]].append(pd.Series(missing))
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if self.logger :
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# if self.logger :
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info= {"missing": i.size,"rows":df.shape[0],"cols":1,'partition':self.PARTITION}
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self.logger.write({"module":"gan-generate","action":"compile.io","input":info})
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# info= {"missing": i.size,"rows":df.shape[0],"cols":1,'partition':self.PARTITION}
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# self.logger.write({"module":"gan-generate","action":"compile.io","input":info})
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# print(df.head())
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tf.compat.v1.reset_default_graph()
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df = pd.DataFrame(df)
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df.columns = columns
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np.random.shuffle(df[columns[0]].values)
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return df.to_dict(orient='list')
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# df = pd.DataFrame(df)
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# df.columns = columns
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# np.random.shuffle(df[columns[0]].values)
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# return df.to_dict(orient='list')
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return _matrix
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if __name__ == '__main__' :
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@ -14,6 +14,11 @@ import data.gan as gan
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from transport import factory
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from data.bridge import Binary
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import threading as thread
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from data.maker import prepare
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import copy
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import os
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import json
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class ContinuousToDiscrete :
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ROUND_UP = 2
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@staticmethod
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@ -77,8 +82,62 @@ class ContinuousToDiscrete :
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def train (**_args):
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"""
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:params sql
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:params store
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"""
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#
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# Let us prepare the data by calling the utility function
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#
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if 'file' in _args :
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#
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# We are reading data from a file
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_args['data'] = pd.read_csv(_args['file'])
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else:
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#
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# data will be read from elsewhere (a data-store)...
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pass
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# if 'ignore' in _args and 'columns' in _args['ignore']:
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def train (**args) :
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_inputhandler = prepare.Input(**_args)
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values,_matrix = _inputhandler.convert()
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args = {"real":_matrix,"context":_args['context']}
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_map = {}
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if 'store' in _args :
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#
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# This
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args['store'] = copy.deepcopy(_args['store']['logs'])
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args['store']['args']['doc'] = _args['context']
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logger = factory.instance(**args['store'])
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args['logger'] = logger
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for key in _inputhandler._map :
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beg = _inputhandler._map[key]['beg']
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end = _inputhandler._map[key]['end']
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values = _inputhandler._map[key]['values'].tolist()
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||||
_map[key] = {"beg":beg,"end":end,"values":np.array(values).astype(str).tolist()}
|
||||
info = {"rows":_matrix.shape[0],"cols":_matrix.shape[1],"map":_map}
|
||||
logger.write({"module":"gan-train","action":"data-prep","context":_args['context'],"input":info})
|
||||
|
||||
args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
|
||||
args ['max_epochs'] = _args['max_epochs']
|
||||
args['matrix_size'] = _matrix.shape[0]
|
||||
args['batch_size'] = 2000
|
||||
args['partition'] = 0 if 'partition' not in _args else _args['partition']
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
|
||||
|
||||
trainer = gan.Train(**args)
|
||||
#
|
||||
# @TODO: Write the map.json in the output directory for the logs
|
||||
#
|
||||
f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']),'w')
|
||||
f.write(json.dumps(_map))
|
||||
f.close()
|
||||
|
||||
trainer.apply()
|
||||
pass
|
||||
def _train (**args) :
|
||||
"""
|
||||
This function is intended to train the GAN in order to learn about the distribution of the features
|
||||
:column columns that need to be synthesized (discrete)
|
||||
|
|
@ -122,18 +181,53 @@ def train (**args) :
|
|||
# If the s
|
||||
trainer = gan.Train(**args)
|
||||
trainer.apply()
|
||||
def post(**args):
|
||||
"""
|
||||
This uploads the tensorflow checkpoint to a data-store (mongodb, biguqery, s3)
|
||||
|
||||
"""
|
||||
pass
|
||||
def get(**args):
|
||||
"""
|
||||
This function will restore a checkpoint from a persistant storage on to disk
|
||||
"""
|
||||
pass
|
||||
def generate(**args):
|
||||
def generate(**_args):
|
||||
"""
|
||||
This function will generate a set of records, before we must load the parameters needed
|
||||
:param data
|
||||
:param context
|
||||
:param logs
|
||||
"""
|
||||
f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
|
||||
_map = json.loads(f.read())
|
||||
f.close()
|
||||
if 'file' in _args :
|
||||
df = pd.read_csv(_args['file'])
|
||||
else:
|
||||
df = _args['data'] if not isinstance(_args['data'],str) else pd.read_csv(_args['data'])
|
||||
args = {"context":_args['context'],"max_epochs":_args['max_epochs'],"candidates":_args['candidates']}
|
||||
args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
|
||||
args ['max_epochs'] = _args['max_epochs']
|
||||
# args['matrix_size'] = _matrix.shape[0]
|
||||
args['batch_size'] = 2000
|
||||
args['partition'] = 0 if 'partition' not in _args else _args['partition']
|
||||
args['row_count'] = df.shape[0]
|
||||
#
|
||||
# @TODO: perhaps get the space of values here ... (not sure it's a good idea)
|
||||
#
|
||||
_args['map'] = _map
|
||||
_inputhandler = prepare.Input(**_args)
|
||||
values,_matrix = _inputhandler.convert()
|
||||
args['values'] = np.array(values)
|
||||
if 'gpu' in _args :
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(_args['gpu'])
|
||||
handler = gan.Predict (**args)
|
||||
handler.load_meta(None)
|
||||
#
|
||||
# Let us now format the matrices as we expect them to be
|
||||
#
|
||||
|
||||
candidates = handler.apply(candidates=args['candidates'])
|
||||
return [_inputhandler.revert(matrix=_matrix) for _matrix in candidates]
|
||||
|
||||
|
||||
|
||||
def _generate(**args):
|
||||
"""
|
||||
This function will generate a synthetic dataset on the basis of a model that has been learnt for the dataset
|
||||
@return pandas.DataFrame
|
||||
|
|
|
|||
|
|
@ -1,32 +0,0 @@
|
|||
import pandas as pd
|
||||
import data.maker
|
||||
from data.params import SYS_ARGS
|
||||
import json
|
||||
from scipy.stats import wasserstein_distance as wd
|
||||
import risk
|
||||
import numpy as np
|
||||
if 'config' in SYS_ARGS :
|
||||
ARGS = json.loads(open(SYS_ARGS['config']).read())
|
||||
if 'generate' not in SYS_ARGS :
|
||||
data.maker.train(**ARGS)
|
||||
else:
|
||||
#
|
||||
#
|
||||
ARGS['no_value'] = ''
|
||||
_df = data.maker.generate(**ARGS)
|
||||
odf = pd.read_csv (ARGS['data'])
|
||||
odf.columns = [name.lower() for name in odf.columns]
|
||||
column = ARGS['column'] if isinstance(ARGS['column'],list) else [ARGS['column']]
|
||||
# print (odf.head())
|
||||
# print (_df.head())
|
||||
print(odf.join(_df[column],rsuffix='_io'))
|
||||
# print (_df[column].risk.evaluate(flag='synth'))
|
||||
# print (odf[column].risk.evaluate(flag='original'))
|
||||
# _x = pd.get_dummies(_df[column]).values
|
||||
# y = pd.get_dummies(odf[column]).values
|
||||
# N = _df.shape[0]
|
||||
# print (np.mean([ wd(_x[i],y[i])for i in range(0,N)]))
|
||||
# print (wd(_x[0],y[0]) )
|
||||
|
||||
# column = SYS_ARGS['column']
|
||||
# odf = open(SYS_ARGS['data'])
|
||||
451
pipeline.py
451
pipeline.py
|
|
@ -9,7 +9,7 @@ import pandas as pd
|
|||
from google.oauth2 import service_account
|
||||
from google.cloud import bigquery as bq
|
||||
import data.maker
|
||||
|
||||
import copy
|
||||
from data.params import SYS_ARGS
|
||||
|
||||
#
|
||||
|
|
@ -69,53 +69,45 @@ class Components :
|
|||
This function will perform training on the basis of a given pointer that reads data
|
||||
|
||||
"""
|
||||
#
|
||||
# @TODO: we need to log something here about the parameters being passed
|
||||
# pointer = args['reader'] if 'reader' in args else lambda: Components.get(**args)
|
||||
df = args['data']
|
||||
schema = None
|
||||
if 'file' in args :
|
||||
|
||||
#
|
||||
# Now we can parse the arguments and submit the entire thing to training
|
||||
#
|
||||
|
||||
logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
|
||||
log_folder = args['logs'] if 'logs' in args else 'logs'
|
||||
PART_SIZE = int(args['part_size'])
|
||||
|
||||
partition = args['partition']
|
||||
log_folder = os.sep.join([log_folder,args['context'],str(partition)])
|
||||
_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
|
||||
_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
|
||||
if 'batch_size' in args :
|
||||
_args['batch_size'] = int(args['batch_size'])
|
||||
|
||||
_args['matrix_size'] = args['matrix_size'] if 'matrix_size' in args else 128 #
|
||||
# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
|
||||
#
|
||||
if int(args['num_gpu']) > 1 :
|
||||
_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
|
||||
df = pd.read_csv(args['file'])
|
||||
del args['file']
|
||||
elif 'data' not in args :
|
||||
reader = factory.instance(**args['store']['source'])
|
||||
if 'row_limit' in args :
|
||||
df = reader.read(sql=args['sql'],limit=args['row_limit'])
|
||||
else:
|
||||
df = reader.read(sql=args['sql'])
|
||||
schema = reader.meta(table=args['from']) if hasattr(reader,'meta') and 'from' in args else None
|
||||
else:
|
||||
_args['gpu'] = 0
|
||||
_args['num_gpu'] = 1
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
|
||||
_args['partition'] = int(partition)
|
||||
_args['continuous']= args['continuous'] if 'continuous' in args else []
|
||||
_args['store'] = {'type':'mongo.MongoWriter','args':{'dbname':'aou','doc':args['context']}}
|
||||
_args['data'] = args['data']
|
||||
df = args['data']
|
||||
|
||||
# print (['partition ',partition,df.value_source_concept_id.unique()])
|
||||
#
|
||||
# @log :
|
||||
# Logging information about the training process for this partition (or not)
|
||||
#
|
||||
|
||||
info = {"rows":df.shape[0],"cols":df.shape[1], "partition":int(partition),"logs":_args['logs']}
|
||||
# df = df.fillna('')
|
||||
if schema :
|
||||
_schema = {}
|
||||
for _item in schema :
|
||||
_type = int
|
||||
_value = 0
|
||||
if _item.field_type == 'FLOAT' :
|
||||
_type =float
|
||||
elif _item.field_type != 'INTEGER' :
|
||||
_type = str
|
||||
_value = ''
|
||||
_schema[_item.name] = _type
|
||||
df[_item.name] = df[_item.name].fillna(_value).astype(_type)
|
||||
args['schema'] = _schema
|
||||
# df[_item.name] = df[_item.name].astype(_type)
|
||||
_args = copy.deepcopy(args)
|
||||
# _args['store'] = args['store']['source']
|
||||
_args['data'] = df
|
||||
|
||||
logger.write({"module":"train","action":"train","input":info})
|
||||
data.maker.train(**_args)
|
||||
|
||||
if 'autopilot' in ( list(args.keys())) :
|
||||
print (['autopilot mode enabled ....'])
|
||||
print (['autopilot mode enabled ....',args['context']])
|
||||
self.generate(args)
|
||||
|
||||
pass
|
||||
|
|
@ -129,141 +121,167 @@ class Components :
|
|||
"""
|
||||
This function will generate data and store it to a given,
|
||||
"""
|
||||
logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
|
||||
log_folder = args['logs'] if 'logs' in args else 'logs'
|
||||
partition = args['partition'] if 'partition' in args else ''
|
||||
log_folder = os.sep.join([log_folder,args['context'],str(partition)])
|
||||
store = args['store']['logs']
|
||||
store['doc'] = args['context']
|
||||
logger = factory.instance(**store) #type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
|
||||
|
||||
_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
|
||||
_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
|
||||
ostore = args['store']['target']
|
||||
writer = factory.instance(**ostore)
|
||||
# log_folder = args['logs'] if 'logs' in args else 'logs'
|
||||
# partition = args['partition'] if 'partition' in args else ''
|
||||
# log_folder = os.sep.join([log_folder,args['context'],str(partition)])
|
||||
|
||||
# _args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
|
||||
# _args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
|
||||
# _args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
|
||||
if 'batch_size' in args :
|
||||
_args['batch_size'] = int(args['batch_size'])
|
||||
# if 'batch_size' in args :
|
||||
# _args['batch_size'] = int(args['batch_size'])
|
||||
|
||||
if int(args['num_gpu']) > 1 :
|
||||
_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
|
||||
else:
|
||||
_args['gpu'] = 0
|
||||
_args['num_gpu'] = 1
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
|
||||
# _args['no_value']= args['no_value']
|
||||
_args['matrix_size'] = args['matrix_size'] if 'matrix_size' in args else 128
|
||||
# if int(args['num_gpu']) > 1 :
|
||||
# _args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
|
||||
# else:
|
||||
# _args['gpu'] = 0
|
||||
# _args['num_gpu'] = 1
|
||||
# os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
|
||||
# # _args['no_value']= args['no_value']
|
||||
# _args['matrix_size'] = args['matrix_size'] if 'matrix_size' in args else 128
|
||||
|
||||
|
||||
# MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
|
||||
PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
|
||||
# # MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
|
||||
# PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
|
||||
|
||||
# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
|
||||
# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
|
||||
# reader = args['reader']
|
||||
# df = reader()
|
||||
df = args['reader']() if 'reader' in args else args['data']
|
||||
schema = args['schema'] if 'schema' in args else None
|
||||
if 'file' in args :
|
||||
|
||||
# if 'slice' in args and 'max_rows' in args['slice']:
|
||||
|
||||
# max_rows = args['slice']['max_rows']
|
||||
# if df.shape[0] > max_rows :
|
||||
# print (".. slicing ")
|
||||
# i = np.random.choice(df.shape[0],max_rows,replace=False)
|
||||
# df = df.iloc[i]
|
||||
df = pd.read_csv(args['file'])
|
||||
else:
|
||||
if 'data' not in args :
|
||||
reader = factory.instance(**args['store']['source'])
|
||||
if 'row_limit' in args :
|
||||
df = reader.read(sql=args['sql'],limit=args['row_limit'])
|
||||
else:
|
||||
df = reader.read(sql=args['sql'])
|
||||
if 'schema' not in args and hasattr(reader,'meta'):
|
||||
schema = reader.meta(table=args['from'])
|
||||
|
||||
|
||||
else:
|
||||
#
|
||||
# This will account for autopilot mode ...
|
||||
df = args['data']
|
||||
|
||||
# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
|
||||
# if partition != '' :
|
||||
# columns = args['columns']
|
||||
# df = np.array_split(df[columns].values,PART_SIZE)
|
||||
# df = pd.DataFrame(df[ int (partition) ],columns = columns)
|
||||
# max_rows = int(args['partition_max_rows']) if 'partition_max_rows' in args else 1000000
|
||||
# N = np.divide(df.shape[0],max_rows).astype(int) + 1
|
||||
info = {"name":args['columns'],"parition":int(partition),"gpu":_args["gpu"],"rows":int(df.shape[0]),"cols":int(df.shape[1]),"space":df[args['columns'][0]].unique().size, "part_size":int(PART_SIZE)}
|
||||
logger.write({"module":"generate","action":"partition","input":info})
|
||||
_args['partition'] = int(partition)
|
||||
_args['continuous']= args['continuous'] if 'continuous' in args else []
|
||||
#
|
||||
# How many rows sub-partition must we divide this into ?
|
||||
# let us fix the data types here every _id field will be an np.int64...
|
||||
#
|
||||
|
||||
schema = args['schema']
|
||||
for item in schema :
|
||||
if item.field_type == 'INTEGER' and df[item.name].dtype != np.int64:
|
||||
df[item.name] = np.array(df[item.name].values,dtype=np.int64)
|
||||
elif item.field_type == 'STRING' and df[item.name].dtype != object :
|
||||
df[item.name] = np.array(df[item.name],dtype=object)
|
||||
|
||||
|
||||
|
||||
# for name in df.columns.tolist():
|
||||
|
||||
# if name.endswith('_id') :
|
||||
# if df[name].isnull().sum() > 0 and name not in ['unique_device_id']:
|
||||
# df[name].fillna(np.nan_to_num(np.nan),inplace=True)
|
||||
# df[name] = df[name].astype(int)
|
||||
_info = {"module":"gan-prep","action":"read","shape":{"rows":df.shape[0],"columns":df.shape[0]}}
|
||||
|
||||
|
||||
_dc = pd.DataFrame()
|
||||
# for mdf in df :
|
||||
_args['data'] = df
|
||||
args['data'] = df
|
||||
args['candidates'] = 1 if 'candidates' not in args else int(args['candidates'])
|
||||
|
||||
_dc = _dc.append(data.maker.generate(**_args))
|
||||
candidates = (data.maker.generate(**args))
|
||||
if 'sql.BQWriter' in ostore['type'] :
|
||||
#table = ".".join([ostore['['dataset'],args['context']])
|
||||
# writer = factory.instance(**ostore)
|
||||
_columns = None
|
||||
skip_columns = []
|
||||
_schema = [{"name":field.name,"type":field.field_type,"description":field.description} for field in schema] if schema else []
|
||||
for _df in candidates :
|
||||
#
|
||||
# we need to format the fields here to make sure we have something cohesive
|
||||
#
|
||||
|
||||
#
|
||||
# We need to post the generate the data in order to :
|
||||
# 1. compare immediately
|
||||
# 2. synthetic copy
|
||||
#
|
||||
if not skip_columns :
|
||||
# _columns = set(df.columns) - set(_df.columns)
|
||||
if 'ignore' in args and 'columns' in args['ignore'] :
|
||||
|
||||
cols = _dc.columns.tolist()
|
||||
for name in args['ignore']['columns'] :
|
||||
for _name in _df.columns:
|
||||
if _name in name:
|
||||
skip_columns.append(_name)
|
||||
#
|
||||
# We perform a series of set operations to insure that the following conditions are met:
|
||||
# - the synthetic dataset only has fields that need to be synthesized
|
||||
# - The original dataset has all the fields except those that need to be synthesized
|
||||
#
|
||||
|
||||
data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io') #-- will be used for comparison (store this in big query)
|
||||
#
|
||||
# performing basic analytics on the synthetic data generated (easy to quickly asses)
|
||||
#
|
||||
info = {"module":"generate","action":"io.metrics","input":{"rows":data_comp.shape[0],"partition":partition,"logs":[]}}
|
||||
_df = _df[list(set(_df.columns) - set(skip_columns))]
|
||||
|
||||
#
|
||||
# @TODO: Send data over to a process for analytics
|
||||
if set(df.columns) & set(_df.columns) :
|
||||
_columns = set(df.columns) - set(_df.columns)
|
||||
df = df[_columns]
|
||||
|
||||
base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
|
||||
cols = _dc.columns.tolist()
|
||||
for name in cols :
|
||||
_args['data'][name] = _dc[name]
|
||||
#
|
||||
# Let us merge the dataset here and and have a comprehensive dataset
|
||||
|
||||
#
|
||||
#-- Let us store all of this into bigquery
|
||||
prefix = args['notify']+'.'+_args['context']
|
||||
partition = str(partition)
|
||||
table = '_'.join([prefix,partition,'io']).replace('__','_')
|
||||
folder = os.sep.join([args['logs'],args['context'],partition,'output'])
|
||||
if 'file' in args :
|
||||
_df = pd.DataFrame.join(df,_df)
|
||||
|
||||
_fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
|
||||
_pname = os.sep.join([folder,table])+'.csv'
|
||||
data_comp.to_csv( _pname,index=False)
|
||||
_args['data'].to_csv(_fname,index=False)
|
||||
|
||||
_id = 'path'
|
||||
writer.write(_df,schema=_schema,table=args['from'])
|
||||
# writer.write(df,table=table)
|
||||
pass
|
||||
else:
|
||||
pass
|
||||
|
||||
credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
|
||||
_pname = os.sep.join([folder,table+'.csv'])
|
||||
_fname = table.replace('_io','_full_io')
|
||||
partial = '.'.join(['io',args['context']+'_partial_io'])
|
||||
complete= '.'.join(['io',args['context']+'_full_io'])
|
||||
data_comp.to_csv(_pname,index=False)
|
||||
if 'dump' in args :
|
||||
print (_args['data'].head())
|
||||
else:
|
||||
Components.lock.acquire()
|
||||
data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
|
||||
_args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000)
|
||||
Components.lock.release()
|
||||
_id = 'dataset'
|
||||
info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
|
||||
if partition :
|
||||
info ['partition'] = int(partition)
|
||||
logger.write({"module":"generate","action":"write","input":info} )
|
||||
|
||||
# #
|
||||
# # We need to post the generate the data in order to :
|
||||
# # 1. compare immediately
|
||||
# # 2. synthetic copy
|
||||
# #
|
||||
|
||||
# cols = _dc.columns.tolist()
|
||||
|
||||
# data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io') #-- will be used for comparison (store this in big query)
|
||||
# #
|
||||
# # performing basic analytics on the synthetic data generated (easy to quickly asses)
|
||||
# #
|
||||
# info = {"module":"generate","action":"io.metrics","input":{"rows":data_comp.shape[0],"partition":partition,"logs":[]}}
|
||||
|
||||
# #
|
||||
# # @TODO: Send data over to a process for analytics
|
||||
|
||||
# base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
|
||||
# cols = _dc.columns.tolist()
|
||||
# for name in cols :
|
||||
# _args['data'][name] = _dc[name]
|
||||
|
||||
# #
|
||||
# #-- Let us store all of this into bigquery
|
||||
# prefix = args['notify']+'.'+_args['context']
|
||||
# partition = str(partition)
|
||||
# table = '_'.join([prefix,partition,'io']).replace('__','_')
|
||||
# folder = os.sep.join([args['logs'],args['context'],partition,'output'])
|
||||
# if 'file' in args :
|
||||
|
||||
# _fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
|
||||
# _pname = os.sep.join([folder,table])+'.csv'
|
||||
# data_comp.to_csv( _pname,index=False)
|
||||
# _args['data'].to_csv(_fname,index=False)
|
||||
|
||||
# _id = 'path'
|
||||
# else:
|
||||
|
||||
# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
|
||||
# _pname = os.sep.join([folder,table+'.csv'])
|
||||
# _fname = table.replace('_io','_full_io')
|
||||
# partial = '.'.join(['io',args['context']+'_partial_io'])
|
||||
# complete= '.'.join(['io',args['context']+'_full_io'])
|
||||
# data_comp.to_csv(_pname,index=False)
|
||||
# if 'dump' in args :
|
||||
# print (_args['data'].head())
|
||||
# else:
|
||||
# Components.lock.acquire()
|
||||
# data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
|
||||
# _args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000)
|
||||
# Components.lock.release()
|
||||
# _id = 'dataset'
|
||||
# info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
|
||||
# if partition :
|
||||
# info ['partition'] = int(partition)
|
||||
# logger.write({"module":"generate","action":"write","input":info} )
|
||||
|
||||
|
||||
|
||||
|
|
@ -308,98 +326,95 @@ if __name__ == '__main__' :
|
|||
# Log what was initiated so we have context of this processing ...
|
||||
#
|
||||
# if 'listen' not in SYS_ARGS :
|
||||
if 'file' in args :
|
||||
DATA = pd.read_csv(args['file']) ;
|
||||
schema = []
|
||||
else:
|
||||
DATA = Components().get(args)
|
||||
client = bq.Client.from_service_account_json(args["private_key"])
|
||||
schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
|
||||
# if 'file' in args :
|
||||
# DATA = pd.read_csv(args['file']) ;
|
||||
# schema = []
|
||||
# else:
|
||||
# DATA = Components().get(args)
|
||||
# client = bq.Client.from_service_account_json(args["private_key"])
|
||||
# schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
|
||||
|
||||
COLUMNS = DATA.columns
|
||||
DATA = np.array_split(DATA,PART_SIZE)
|
||||
args['schema'] = schema
|
||||
# COLUMNS = DATA.columns
|
||||
# DATA = np.array_split(DATA,PART_SIZE)
|
||||
# args['schema'] = schema
|
||||
if 'generate' in SYS_ARGS :
|
||||
#
|
||||
# Let us see if we have partitions given the log folder
|
||||
|
||||
content = os.listdir( os.sep.join([args['logs'],args['context']]))
|
||||
content = os.listdir( os.sep.join([args['logs'],'train',args['context']]))
|
||||
generator = Components()
|
||||
|
||||
if ''.join(content).isnumeric() :
|
||||
#
|
||||
# we have partitions we are working with
|
||||
# if ''.join(content).isnumeric() :
|
||||
# #
|
||||
# # we have partitions we are working with
|
||||
|
||||
jobs = []
|
||||
# jobs = []
|
||||
|
||||
# columns = DATA.columns.tolist()
|
||||
# # columns = DATA.columns.tolist()
|
||||
|
||||
# DATA = np.array_split(DATA,PART_SIZE)
|
||||
# # DATA = np.array_split(DATA,PART_SIZE)
|
||||
|
||||
for index in range(0,PART_SIZE) :
|
||||
if 'focus' in args and int(args['focus']) != index :
|
||||
#
|
||||
# This handles failures/recoveries for whatever reason
|
||||
# If we are only interested in generating data for a given partition
|
||||
continue
|
||||
# index = id.index(id)
|
||||
# for index in range(0,PART_SIZE) :
|
||||
# if 'focus' in args and int(args['focus']) != index :
|
||||
# #
|
||||
# # This handles failures/recoveries for whatever reason
|
||||
# # If we are only interested in generating data for a given partition
|
||||
# continue
|
||||
# # index = id.index(id)
|
||||
|
||||
args['partition'] = index
|
||||
args['data'] = DATA[index]
|
||||
if int(args['num_gpu']) > 1 :
|
||||
args['gpu'] = index
|
||||
else:
|
||||
args['gpu']=0
|
||||
# args['partition'] = index
|
||||
# args['data'] = DATA[index]
|
||||
# if int(args['num_gpu']) > 1 :
|
||||
# args['gpu'] = index
|
||||
# else:
|
||||
# args['gpu']=0
|
||||
|
||||
make = lambda _args: (Components()).generate(_args)
|
||||
job = Process(target=make,args=(args,))
|
||||
job.name = 'generator # '+str(index)
|
||||
job.start()
|
||||
jobs.append(job)
|
||||
# if len(jobs) == 1 :
|
||||
# job.join()
|
||||
# make = lambda _args: (Components()).generate(_args)
|
||||
# job = Process(target=make,args=(args,))
|
||||
# job.name = 'generator # '+str(index)
|
||||
# job.start()
|
||||
# jobs.append(job)
|
||||
# # if len(jobs) == 1 :
|
||||
# # job.join()
|
||||
|
||||
print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
|
||||
while len(jobs)> 0 :
|
||||
jobs = [job for job in jobs if job.is_alive()]
|
||||
time.sleep(2)
|
||||
# print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
|
||||
# while len(jobs)> 0 :
|
||||
# jobs = [job for job in jobs if job.is_alive()]
|
||||
# time.sleep(2)
|
||||
|
||||
# generator.generate(args)
|
||||
else:
|
||||
generator.generate(args)
|
||||
# # generator.generate(args)
|
||||
# else:
|
||||
# generator.generate(args)
|
||||
# Components.generate(args)
|
||||
elif 'shuffle' in SYS_ARGS:
|
||||
generator.generate(args)
|
||||
|
||||
|
||||
for data in DATA :
|
||||
args['data'] = data
|
||||
_df = (Components()).shuffle(args)
|
||||
else:
|
||||
|
||||
# DATA = np.array_split(DATA,PART_SIZE)
|
||||
agent = Components()
|
||||
agent.train(**args)
|
||||
# jobs = []
|
||||
# for index in range(0,PART_SIZE) :
|
||||
# if 'focus' in args and int(args['focus']) != index :
|
||||
# continue
|
||||
# args['part_size'] = PART_SIZE
|
||||
# args['partition'] = index
|
||||
# args['data'] = DATA[index]
|
||||
# if int(args['num_gpu']) > 1 :
|
||||
# args['gpu'] = index
|
||||
# else:
|
||||
# args['gpu']=0
|
||||
|
||||
jobs = []
|
||||
for index in range(0,PART_SIZE) :
|
||||
if 'focus' in args and int(args['focus']) != index :
|
||||
continue
|
||||
args['part_size'] = PART_SIZE
|
||||
args['partition'] = index
|
||||
args['data'] = DATA[index]
|
||||
if int(args['num_gpu']) > 1 :
|
||||
args['gpu'] = index
|
||||
else:
|
||||
args['gpu']=0
|
||||
|
||||
make = lambda _args: (Components()).train(**_args)
|
||||
job = Process(target=make,args=( dict(args),))
|
||||
job.name = 'Trainer # ' + str(index)
|
||||
job.start()
|
||||
jobs.append(job)
|
||||
# args['gpu']
|
||||
print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
|
||||
while len(jobs)> 0 :
|
||||
jobs = [job for job in jobs if job.is_alive()]
|
||||
time.sleep(2)
|
||||
# make = lambda _args: (Components()).train(**_args)
|
||||
# job = Process(target=make,args=( dict(args),))
|
||||
# job.name = 'Trainer # ' + str(index)
|
||||
# job.start()
|
||||
# jobs.append(job)
|
||||
# # args['gpu']
|
||||
# print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
|
||||
# while len(jobs)> 0 :
|
||||
# jobs = [job for job in jobs if job.is_alive()]
|
||||
# time.sleep(2)
|
||||
|
||||
# trainer = Components()
|
||||
# trainer.train(**args)
|
||||
|
|
|
|||
Loading…
Reference in New Issue