import numpy as np

import modin.pandas as pd

from sklearnex import patch_sklearn

patch_sklearn()

from tensorflow import keras

from tensorflow.keras import layers

from io import StringIO

from IPython.display import Image, display

import time

from sklearnx.metrics import mean_squared_error as mse

from pandas.core.dtypes.common import classes

import ray

ray.init()

def to_sequences(seq_size, obs):

return np.array(x),np.array(y)

model_path = 'model_path'

model = keras.models.load_model(model_path)

df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv')

df['date'] = pd.to_datetime(df['date'])

counties = list(df['fips'].unique())

counties.sort()

metrics = pd.DataFrame(columns = ['Fips','RMSE','Mean Cases','Relative Error','Number of Instances','Minimum','Median','10%','25%','75%','90%','Maximum'])

actual = pd.DataFrame(mobility_la['date'],columns = ['date'])

predicted = pd.DataFrame(mobility_la['date'],columns = ['date'])

for i in counties:

temp = df[df['fips']==i]

temp.reset_index(inplace = True,drop = True)

df_train = temp[0:int(0.8*len(temp))]

df_test = temp[int(0.8*len(temp)):]

#df_test['date'] = pd.to_datetime(df_test['date'])

cases_train = df_train['cases'].tolist()

cases_test = df_test['cases'].tolist()

SEQUENCE_SIZE = 7

x_train,y_train = to_sequences(SEQUENCE_SIZE,cases_train)

x_test,y_test = to_sequences(SEQUENCE_SIZE,cases_test)

if len(x_test) > 0:

# print("Shape of training set: {}".format(x_train.shape))

# print("Shape of test set: {}".format(x_test.shape))

predictions = model.predict(x_test)

diff = pd.DataFrame(predictions) - pd.DataFrame(y_test)

diff = abs(diff)

metrics.loc[len(metrics.index)] = [i,mse(predictions,y_test,squared = False),y_test.mean(),(mse(predictions,y_test,squared = False)/y_test.mean())*100,len(y_test),diff.values.min(),np.median(diff),np.quantile(diff,0.10),np.quantile(diff,0.25),np.quantile(diff,0.75),np.quantile(diff,0.90),diff.values.max()]#,np.sum(np.array(diff) < 0, axis=0)]

date = pd.DataFrame(df_test.tail(-7)['date'])

date.reset_index(inplace = True,drop = True)

predictions = pd.concat([pd.DataFrame(predictions,columns = [i]),date],axis = 1)

y_test = pd.concat([pd.DataFrame(y_test,columns = [i]),date],axis = 1)

actual = actual.merge(y_test,on = ['date'],how = 'left')

predicted = actual.merge(predictions,on = 'date',how = 'left')

else:

pass

print(i)

metrics_path = '/path'

actual_path = '/path'

predictions_path = '/path'

metrics.to_csv(metrics_path,index = False)

actual.to_csv(actual_path,index = False)

predicted.to_csv(predictions_path,index = False)