def inject_user():
user = User()
name = "Kenza"
query = user.get_user(name)
dic = json.dumps(query, ensure_ascii=False)
loaded_dic = json.loads(dic)
return dict(mydict=loaded_dic)
class Recommender:
def __init__(self):
self.user = User()
self.ranker = Ranker()
def build_states(self, states = None):
"""
Add the states to the ranker in the proper format
"""
if states is not None:
self.ranker.states = states
else:
import os
path = os.path.abspath(os.path.dirname(__file__))
json_data = open(os.path.join(path,'crawler/results/state.json'))
data = json.load(json_data)
json_data.close()
dict = data[1]
for state in dict.iterkeys():
state_vect = []
for i in range(1, len(dict[state])):
state_vect.append(dict[state][i] / float(dict[state][0]))
dict[state] = state_vect
self.ranker.states = dict
def build_counties(self, counties = None):
"""
Add the counties to the ranker in the proper format
"""
if counties is not None:
self.ranker.counties = counties
else:
import os
path = os.path.abspath(os.path.dirname(__file__))
json_data = open(os.path.join(path,'crawler/results/county.json'))
data = json.load(json_data)
json_data.close()
dict = data[1]
for county in dict.iterkeys():
county_vect = []
for i in range(1, len(dict[county])):
county_vect.append(dict[county][i] / float(dict[county][0]))
dict[county] = county_vect
self.ranker.counties = dict
def set_user(self, user):
self.user = user
def recommend_states(self):
return self.ranker.rank_states(self.user.get_vector())
def recommend_counties(self):
return self.ranker.rank_counties(self.user.get_vector())
def best_day(name, forecast_date):
user = User()
data = user.get_user(name)
df = pd.DataFrame.from_dict(data, orient='columns')
# df = (df - df.mean()) / df.std()
# summary = df.describe()
# summary.to_csv('summary.csv', sep=',')
train = df[:int(float(5/7) * (len(df)))]
valid = df[int(float(5/7) * (len(df))):]
train.index = pd.DatetimeIndex(train.index.values, freq='1H')
# Fit model on train set
model = ARIMA(endog=train['activity'], order=(1,0,0), exog = train.iloc[:, 0:6])
model_fit = model.fit(disp=0)
print(model_fit.summary())
#Forecast on valid set
# prediction = model_fit.forecast(steps=len(valid), exog=valid.iloc[:,0:6])
# print(prediction)
# Fit model on entire set
# model = ARIMA(endog=df['activity'], order=(1,0,0), exog=df.iloc[:, 0:6])
# model_fit = model.fit(disp=0)
resort_predictions = []
time_predictions = []
# Store exogenous variables from today's weather in exog dataframe
for resort, q in resorts.items():
current_weather = user.weather_query('today',q)
index = dic_float(current_weather[0])[2]
exog = pd.DataFrame(index=index[9:18], columns=list(df)[0:6])
for j in range(0,len(list(df)[0:5])):
values = dic_float(current_weather[j])[1]
for i in range(0,9):
exog.iloc[i][j] = values[i+9]
exog['status'] = 1
# Forecast for 24 hours
prediction = model_fit.forecast(steps=9, exog=exog)
# Total activity for the day
prediction_second = 0
for p in prediction[0]:
prediction_second += p
resort_predictions.append(resort)
time_predictions.append(prediction_second)
best_prediction_s = max(time_predictions)
index = time_predictions.index(best_prediction_s)
best_resort = resort_predictions[index]
best_prediction = str(datetime.timedelta(seconds=best_prediction_s))
print('%s will be perfect for you today! (time activity estimation: %s)' % (best_resort, best_prediction))
return best_resort
def __init__(self):
self.user = User()
self.ranker = Ranker()