import pandas as pd
import tensorflow as tf
import sys
import random
import pandas as pd
from functools import reduce
from sklearn.utils import shuffle
import pickle


data = pd.read_csv('tra.csv')
name = sys.argv[1]
client = int(sys.argv[2].strip())


l=list(range(int(client)+1))
data = reduce(lambda df1, df2: df1.merge(df2, "outer"), [data.loc[data.digit==i] for i in l])
data = shuffle(data)
data.reset_index(inplace=True, drop=True)
n=random.choice(list(range(100,201)))
data = data.sample(n=n, random_state=1)
data.drop(data.filter(regex="Unname"),axis=1, inplace=True)

# data = pd.read_csv('{}.csv'.format(client))
# data = data.loc[data.digit==client].sample(n=100, random_state=1)
# data = data.loc[data.digit==client].iloc[:100,:]
# data = data.sample(n=1000, random_state=1)
# data = data.iloc[:1000,:]
# l=random.sample(list(range(10)),random.choice(list(range(1,10))))
# data = reduce(lambda df1, df2: df1.merge(df2, "outer"), [data.loc[data.digit==i] for i in l])
# data = shuffle(data)
# data.reset_index(inplace=True, drop=True)
# data = data.sample(n=100, random_state=1)
data.drop(data.filter(regex="Unname"),axis=1, inplace=True)

div=0
for i in l:
    div=div+(pow(abs((n/10)-len(data.loc[data.digit==i])),2)/10)
pickle.dump(div,open('{}.pkl'.format(client),'wb'))


load = tf.keras.models.load_model('TEST2')
# load = tf.keras.models.load_model('TEST-{}'.format(sys.argv[3]))

load.fit(data.iloc[:,:-2], data.digit, epochs=25, batch_size = 32)

load.save('Local-{}-{}'.format(name,client))
26601eec09744c3f9fcd15feb366cc0fdc159087
<mask token>

<mask token>
data.reset_index(inplace=True, drop=True)
<mask token>
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
<mask token>
for i in l:
    div = div + pow(abs(n / 10 - len(data.loc[data.digit == i])), 2) / 10
pickle.dump(div, open('{}.pkl'.format(client), 'wb'))
<mask token>
load.fit(data.iloc[:, :-2], data.digit, epochs=25, batch_size=32)
load.save('Local-{}-{}'.format(name, client))

<mask token>
data = pd.read_csv('tra.csv')
name = sys.argv[1]
client = int(sys.argv[2].strip())
l = list(range(int(client) + 1))
data = reduce(lambda df1, df2: df1.merge(df2, 'outer'), [data.loc[data.
    digit == i] for i in l])
data = shuffle(data)
data.reset_index(inplace=True, drop=True)
n = random.choice(list(range(100, 201)))
data = data.sample(n=n, random_state=1)
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
div = 0
for i in l:
    div = div + pow(abs(n / 10 - len(data.loc[data.digit == i])), 2) / 10
pickle.dump(div, open('{}.pkl'.format(client), 'wb'))
load = tf.keras.models.load_model('TEST2')
load.fit(data.iloc[:, :-2], data.digit, epochs=25, batch_size=32)
load.save('Local-{}-{}'.format(name, client))

import pandas as pd
import tensorflow as tf
import sys
import random
import pandas as pd
from functools import reduce
from sklearn.utils import shuffle
import pickle
data = pd.read_csv('tra.csv')
name = sys.argv[1]
client = int(sys.argv[2].strip())
l = list(range(int(client) + 1))
data = reduce(lambda df1, df2: df1.merge(df2, 'outer'), [data.loc[data.
    digit == i] for i in l])
data = shuffle(data)
data.reset_index(inplace=True, drop=True)
n = random.choice(list(range(100, 201)))
data = data.sample(n=n, random_state=1)
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
data.drop(data.filter(regex='Unname'), axis=1, inplace=True)
div = 0
for i in l:
    div = div + pow(abs(n / 10 - len(data.loc[data.digit == i])), 2) / 10
pickle.dump(div, open('{}.pkl'.format(client), 'wb'))
load = tf.keras.models.load_model('TEST2')
load.fit(data.iloc[:, :-2], data.digit, epochs=25, batch_size=32)
load.save('Local-{}-{}'.format(name, client))

import pandas as pd
import tensorflow as tf
import sys
import random
import pandas as pd
from functools import reduce
from sklearn.utils import shuffle
import pickle


data = pd.read_csv('tra.csv')
name = sys.argv[1]
client = int(sys.argv[2].strip())


l=list(range(int(client)+1))
data = reduce(lambda df1, df2: df1.merge(df2, "outer"), [data.loc[data.digit==i] for i in l])
data = shuffle(data)
data.reset_index(inplace=True, drop=True)
n=random.choice(list(range(100,201)))
data = data.sample(n=n, random_state=1)
data.drop(data.filter(regex="Unname"),axis=1, inplace=True)

# data = pd.read_csv('{}.csv'.format(client))
# data = data.loc[data.digit==client].sample(n=100, random_state=1)
# data = data.loc[data.digit==client].iloc[:100,:]
# data = data.sample(n=1000, random_state=1)
# data = data.iloc[:1000,:]
# l=random.sample(list(range(10)),random.choice(list(range(1,10))))
# data = reduce(lambda df1, df2: df1.merge(df2, "outer"), [data.loc[data.digit==i] for i in l])
# data = shuffle(data)
# data.reset_index(inplace=True, drop=True)
# data = data.sample(n=100, random_state=1)
data.drop(data.filter(regex="Unname"),axis=1, inplace=True)

div=0
for i in l:
    div=div+(pow(abs((n/10)-len(data.loc[data.digit==i])),2)/10)
pickle.dump(div,open('{}.pkl'.format(client),'wb'))


load = tf.keras.models.load_model('TEST2')
# load = tf.keras.models.load_model('TEST-{}'.format(sys.argv[3]))

load.fit(data.iloc[:,:-2], data.digit, epochs=25, batch_size = 32)

load.save('Local-{}-{}'.format(name,client))