bug fix and upgrades to base functionalities
This commit is contained in:
Steve Nyemba
parent
a2988a5972
commit
8e722d5bf1
15
data/gan.py
15
data/gan.py
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@ -550,6 +550,7 @@ class Predict(GNet):
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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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@ -577,11 +578,13 @@ class Predict(GNet):
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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).astype(np.int32))
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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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if np.divide( np.sum(x), x.size) > .9 or p:
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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 i == CANDIDATE_COUNT:
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@ -597,11 +600,13 @@ class Predict(GNet):
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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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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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# r = np.zeros(self.ROW_COUNT)
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df.columns = self.values
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if len(found):
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# print (len(found),NTH_VALID_CANDIDATE)
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@ -618,6 +623,10 @@ class Predict(GNet):
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missing = np.repeat(0, np.where(ii==1)[0].size)
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else:
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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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@ -15,6 +15,7 @@ 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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class ContinuousToDiscrete :
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ROUND_UP = 2
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@staticmethod
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def binary(X,n=4) :
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"""
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@ -22,7 +23,7 @@ class ContinuousToDiscrete :
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"""
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# BOUNDS = np.repeat(np.divide(X.max(),n),n).cumsum().tolist()
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BOUNDS = ContinuousToDiscrete.bounds(X,n)
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BOUNDS = ContinuousToDiscrete.bounds(np.round(X,ContinuousToDiscrete.ROUND_UP),n)
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# _map = [{"index":BOUNDS.index(i),"ubound":i} for i in BOUNDS]
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_matrix = []
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@ -41,7 +42,7 @@ class ContinuousToDiscrete :
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@staticmethod
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def bounds(x,n):
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return list(pd.cut(np.array(x),n).categories)
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return list(pd.cut(np.array( np.round(x,ContinuousToDiscrete.ROUND_UP) ),n).categories)
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@ -66,7 +67,7 @@ class ContinuousToDiscrete :
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ubound = BOUNDS[ index ].right
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lbound = BOUNDS[ index ].left
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x_ = np.round(np.random.uniform(lbound,ubound),3).astype(float)
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x_ = np.round(np.random.uniform(lbound,ubound),ContinuousToDiscrete.ROUND_UP).astype(float)
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values.append(x_)
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lbound = ubound
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@ -104,10 +105,10 @@ def train (**args) :
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# if 'float' not in df[col].dtypes.name :
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# args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
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if 'float' in df[col].dtypes.name and col in CONTINUOUS:
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BIN_SIZE = 10 if 'bin_size' not in args else int(args['bin_size'])
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BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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args['real'] = ContinuousToDiscrete.binary(df[col],BIN_SIZE).astype(np.float32)
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else:
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args['real'] = pd.get_dummies(df[col].fillna('')).astype(np.float32).values
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args['real'] = pd.get_dummies(df[col].dropna()).astype(np.float32).values
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args['column'] = col
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@ -157,12 +158,13 @@ def generate(**args):
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args['context'] = col
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args['column'] = col
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if 'float' in df[col].dtypes.name or col in CONTINUOUS :
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#
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# We should create the bins for the values we are observing here
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BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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values = ContinuousToDiscrete.continuous(df[col].values,BIN_SIZE)
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else:
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# if 'float' in df[col].dtypes.name or col in CONTINUOUS :
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# #
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# # We should create the bins for the values we are observing here
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# BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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# values = ContinuousToDiscrete.continuous(df[col].values,BIN_SIZE)
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# # values = np.unique(values).tolist()
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# else:
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values = df[col].unique().tolist()
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args['values'] = values
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@ -172,10 +174,11 @@ def generate(**args):
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handler = gan.Predict (**args)
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handler.load_meta(col)
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r = handler.apply()
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_df[col] = r[col]
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BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
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_df[col] = ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if 'float' in df[col].dtypes.name or col in CONTINUOUS else r[col]
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#
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# @TODO: log basic stats about the synthetic attribute
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#
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# print (r)s
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# break
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return _df
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310
pipeline.py
310
pipeline.py
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@ -1,5 +1,6 @@
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import json
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from transport import factory
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import numpy as np
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import os
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from multiprocessing import Process
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import pandas as pd
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@ -8,116 +9,291 @@ import data.maker
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from data.params import SYS_ARGS
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f = open ('config.json')
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PIPELINE = json.loads(f.read())
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f.close()
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#
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# The configuration array is now loaded and we will execute the pipe line as follows
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DATASET='combined20190510_deid'
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DATASET='combined20190510'
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class Components :
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@staticmethod
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def get(args):
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"""
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This function returns a data-frame provided a bigquery sql statement with conditions (and limits for testing purposes)
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The function must be wrapped around a lambda this makes testing easier and changing data stores transparent to the rest of the code. (Vital when testing)
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:sql basic sql statement
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:condition optional condition and filters
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"""
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SQL = args['sql']
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if 'condition' in args :
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condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
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SQL = " ".join([SQL,'WHERE',condition])
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SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
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return SQL #+ " LIMIT 10000 "
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@staticmethod
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def train(args):
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"""
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This function will instanciate a worker that will train given a message that is provided to it
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This is/will be a separate process that will
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"""
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print (['starting .... ',args['notify'],args['context']] )
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#SQL = args['sql']
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#if 'condition' in args :
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# condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
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# SQL = " ".join([SQL,'WHERE',condition])
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print ( args['context'])
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = os.sep.join(["logs",args['context']])
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_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":250,"num_gpus":2,"column":args['columns'],"id":"person_id","logger":logger}
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os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu']
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#SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
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SQL = Components.get(args)
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if 'limit' in args :
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SQL = ' '.join([SQL,'limit',args['limit'] ])
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_args['max_epochs'] = 250 if 'max_epochs' not in args else args['max_epochs']
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SQL = SQL + 'LIMIT ' + args['limit']
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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#_args['data'] = _args['data'].astype(object)
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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data.maker.train(**_args)
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df = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
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return df
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# return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna()
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@staticmethod
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def generate(args):
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def split(X,MAX_ROWS=3,PART_SIZE=3):
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return list(pd.cut( np.arange(X.shape[0]+1),PART_SIZE).categories)
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def train(self,**args):
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"""
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This function will perform training on the basis of a given pointer that reads data
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"""
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#
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# @TODO: we need to log something here about the parameters being passed
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pointer = args['reader'] if 'reader' in args else lambda: Components.get(**args)
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df = pointer()
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#
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# Now we can parse the arguments and submit the entire thing to training
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#
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = args['logs'] if 'logs' in args else 'logs'
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_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
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PART_SIZE = args['part_size'] if 'part_size' in args else 0
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if df.shape[0] > MAX_ROWS and 'partition' not in args:
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lbound = 0
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bounds = list(pd.cut( np.arange(df.shape[0]+1),PART_SIZE).categories)
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# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
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qwriter = factory.instance(type='queue.QueueWriter',args={'queue':'aou.io'})
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for b in bounds :
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part_index = bounds.index(b)
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ubound = int(b.right)
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_data = df.iloc[lbound:ubound][args['columns']]
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lbound = ubound
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# _args['logs'] = os.sep.join([log_folder,str(part_index)])
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_args['partition'] = str(part_index)
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_args['logger'] = {'args':{'dbname':'aou','doc':args['context']},'type':'mongo.MongoWriter'}
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#
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# We should post the the partitions to a queue server (at least the instructions on ):
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# - where to get the data
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# - and athe arguments to use (partition #,columns,gpu,epochs)
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#
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info = {"rows":_data.shape[0],"cols":_data.shape[1], "paritition":part_index,"logs":_args['logs']}
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p = {"args":_args,"data":_data.to_dict(orient="records"),"info":info}
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qwriter.write(p)
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#
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# @TODO:
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# - Notify that information was just posted to the queue
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info['max_rows'] = MAX_ROWS
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info['part_size'] = PART_SIZE
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logger.write({"module":"train","action":"setup-partition","input":info})
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pass
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else:
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partition = args['partition'] if 'partition' in args else ''
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log_folder = os.sep.join([log_folder,args['context'],partition])
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_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
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_args['data'] = df
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#
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# @log :
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# Logging information about the training process for this partition (or not)
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#
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info = {"rows":df.shape[0],"cols":df.shape[1], "partition":partition,"logs":_args['logs']}
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logger.write({"module":"train","action":"train","input":info})
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data.maker.train(**_args)
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pass
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# @staticmethod
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def generate(self,args):
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"""
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This function will generate data and store it to a given,
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"""
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = os.sep.join(["logs",args['context']])
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_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":250,"num_gpus":2,"column":args['columns'],"id":"person_id","logger":logger}
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os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu']
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SQL = Components.get(args)
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if 'limit' in args :
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SQL = " ".join([SQL ,'limit', args['limit'] ])
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').fillna('')
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log_folder = args['logs'] if 'logs' in args else 'logs'
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partition = args['partition'] if 'partition' in args else ''
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log_folder = os.sep.join([log_folder,args['context'],partition])
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_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
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_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
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_args['no_value']= args['no_value']
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MAX_ROWS = args['max_rows'] if 'max_rows' in args else 0
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PART_SIZE = args['part_size'] if 'part_size' in args else 0
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# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
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reader = args['reader']
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df = reader()
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if 'partition' in args :
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bounds = Components.split(df,MAX_ROWS,PART_SIZE)
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# bounds = list(pd.cut( np.arange(df.shape[0]+1),PART_SIZE).categories)
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lbound = int(bounds[int(partition)].left)
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ubound = int(bounds[int(partition)].right)
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df = df.iloc[lbound:ubound]
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_args['data'] = df
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# _args['data'] = reader()
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#_args['data'] = _args['data'].astype(object)
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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_args['max_epochs'] = 250 if 'max_epochs' not in args else args['max_epochs']
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_args['no_value'] = args['no_value'] if 'no_value' in args else ''
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#credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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#_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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#_args['data'] = _args['data'].astype(object)
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_dc = data.maker.generate(**_args)
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#
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# We need to post the generate the data in order to :
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# 1. compare immediately
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# 2. synthetic copy
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#
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cols = _dc.columns.tolist()
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print (args['columns'])
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data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io') #-- will be used for comparison (store this in big query)
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base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
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print (_args['data'].shape)
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print (_args['data'].shape)
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for name in cols :
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_args['data'][name] = _dc[name]
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info = {"module":"generate","action":"io","input":{"rows":_dc[name].shape[0],"name":name}}
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if partition != '' :
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info['partition'] = partition
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logger.write(info)
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# filename = os.sep.join([log_folder,'output',name+'.csv'])
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# data_comp[[name]].to_csv(filename,index=False)
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#
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#-- Let us store all of this into bigquery
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prefix = args['notify']+'.'+_args['context']
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table = '_'.join([prefix,'compare','io'])
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data_comp.to_gbq(if_exists='replace',destination_table=table,credentials=credentials,chunksize=50000)
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_args['data'].to_gbq(if_exists='replace',destination_table=table.replace('compare','full'),credentials=credentials,chunksize=50000)
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data_comp.to_csv(os.sep.join([log_folder,table+'.csv']),index=False)
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table = '_'.join([prefix,partition,'io']).replace('__','_')
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folder = os.sep.join([args['logs'],args['context'],partition,'output'])
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if 'file' in args :
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_fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
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_pname = os.sep.join([folder,table])+'.csv'
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data_comp.to_csv( _pname,index=False)
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_args['data'].to_csv(_fname,index=False)
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if __name__ == '__main__' :
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index = int(SYS_ARGS['index'])
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args = (PIPELINE[index])
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#if 'limit' in SYS_ARGS :
|
||||
# args['limit'] = SYS_ARGS['limit']
|
||||
#args['dataset'] = 'combined20190510'
|
||||
SYS_ARGS['dataset'] = 'combined20190510_deid' if 'dataset' not in SYS_ARGS else SYS_ARGS['dataset']
|
||||
#if 'max_epochs' in SYS_ARGS :
|
||||
# args['max_epochs'] = SYS_ARGS['max_epochs']
|
||||
args = dict(args,**SYS_ARGS)
|
||||
if 'generate' in SYS_ARGS :
|
||||
Components.generate(args)
|
||||
|
||||
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')
|
||||
data_comp.to_gbq(if_exists='replace',destination_table=_pname,credentials='credentials',chunk_size=50000)
|
||||
data_comp.to_csv(_pname,index=False)
|
||||
INSERT_FLAG = 'replace' if 'partition' not in args else 'append'
|
||||
_args['data'].to_gbq(if_exists=INSERT_FLAG,destination_table=_fname,credentials='credentials',chunk_size=50000)
|
||||
|
||||
Components.train(args)
|
||||
info = {"full":{"path":_fname,"rows":_args['data'].shape[0]},"compare":{"name":_pname,"rows":data_comp.shape[0]} }
|
||||
if partition :
|
||||
info ['partition'] = partition
|
||||
logger.write({"module":"generate","action":"write","info":info} )
|
||||
@staticmethod
|
||||
def callback(channel,method,header,stream):
|
||||
|
||||
info = json.loads(stream)
|
||||
logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':SYS_ARGS['context']})
|
||||
|
||||
logger.write({'module':'process','action':'read-partition','input':info['info']})
|
||||
df = pd.DataFrame(info['data'])
|
||||
args = info['args']
|
||||
if int(args['num_gpu']) > 1 and args['gpu'] > 0:
|
||||
args['gpu'] = args['gpu'] + args['num_gpu']
|
||||
args['reader'] = lambda: df
|
||||
#
|
||||
# @TODO: Fix
|
||||
# There is an inconsistency in column/columns ... fix this shit!
|
||||
#
|
||||
args['columns'] = args['column']
|
||||
(Components()).train(**args)
|
||||
logger.write({"module":"process","action":"exit","info":info["info"]})
|
||||
channel.close()
|
||||
channel.connection.close()
|
||||
pass
|
||||
|
||||
if __name__ == '__main__' :
|
||||
filename = SYS_ARGS['config'] if 'config' in SYS_ARGS else 'config.json'
|
||||
f = open (filename)
|
||||
PIPELINE = json.loads(f.read())
|
||||
f.close()
|
||||
index = int(SYS_ARGS['index']) if 'index' in SYS_ARGS else 0
|
||||
|
||||
args = (PIPELINE[index])
|
||||
args['dataset'] = 'combined20190510'
|
||||
args = dict(args,**SYS_ARGS)
|
||||
args['max_rows'] = int(args['max_rows']) if 'max_rows' in args else 3
|
||||
args['part_size']= int(args['part_size']) if 'part_size' in args else 3
|
||||
|
||||
#
|
||||
# @TODO:
|
||||
# Log what was initiated so we have context of this processing ...
|
||||
#
|
||||
if 'listen' not in SYS_ARGS :
|
||||
if 'file' in args :
|
||||
reader = lambda: pd.read_csv(args['file']) ;
|
||||
else:
|
||||
reader = lambda: Components().get(args)
|
||||
args['reader'] = reader
|
||||
|
||||
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']]))
|
||||
generator = Components()
|
||||
if ''.join(content).isnumeric() :
|
||||
#
|
||||
# we have partitions we are working with
|
||||
|
||||
for id in ''.join(content) :
|
||||
args['partition'] = id
|
||||
|
||||
generator.generate(args)
|
||||
else:
|
||||
generator.generate(args)
|
||||
# Components.generate(args)
|
||||
elif 'listen' in args :
|
||||
#
|
||||
# This will start a worker just in case to listen to a queue
|
||||
if 'read' in SYS_ARGS :
|
||||
QUEUE_TYPE = 'queue.QueueReader'
|
||||
pointer = lambda qreader: qreader.read(1)
|
||||
else:
|
||||
QUEUE_TYPE = 'queue.QueueListener'
|
||||
pointer = lambda qlistener: qlistener.listen()
|
||||
N = int(SYS_ARGS['jobs']) if 'jobs' in SYS_ARGS else 1
|
||||
|
||||
qhandlers = [factory.instance(type=QUEUE_TYPE,args={'queue':'aou.io'}) for i in np.arange(N)]
|
||||
jobs = []
|
||||
for qhandler in qhandlers :
|
||||
qhandler.callback = Components.callback
|
||||
job = Process(target=pointer,args=(qhandler,))
|
||||
job.start()
|
||||
jobs.append(job)
|
||||
#
|
||||
# let us wait for the jobs
|
||||
print (["Started ",len(jobs)," trainers"])
|
||||
while len(jobs) > 0 :
|
||||
|
||||
jobs = [job for job in jobs if job.is_alive()]
|
||||
|
||||
# pointer(qhandler)
|
||||
|
||||
|
||||
# qreader.read(1)
|
||||
pass
|
||||
else:
|
||||
|
||||
trainer = Components()
|
||||
trainer.train(**args)
|
||||
# Components.train(**args)
|
||||
#for args in PIPELINE :
|
||||
#args['dataset'] = 'combined20190510'
|
||||
#process = Process(target=Components.train,args=(args,))
|
||||
|
|
|
|||
Loading…
Reference in New Issue