# print permuted_predictors
# Iterate through all combinations of permutations and shards:
for i, monthkey in enumerate(gv.months.keys()):
for j, hourkey in enumerate(gv.hours.keys()):
tablename = "flightdelays_{month}_{hour}".format(month=monthkey, hour=hourkey)
for k, predictors in enumerate(permuted_predictors):
# Get the predictors in order:
predictors = np.array(predictors)
predictor_types = np.array(permuted_predictor_types[k])
c_preds = predictors[predictor_types == 0]
d_preds = predictors[predictor_types == 1]
# Create a pickle filename based on the predictors and month/hour:
filename = ff.make_model_pickle_filename(tablename, predictors, dir_structure="../saved_models/")
# Test if pickle file exists already:
file_exists = os.path.isfile(filename)
print filename, file_exists
# sys.exit(1)
if overwrite == "yes" or file_exists == False:
dl.train_log(
tablename,
continuous_predictors=list(c_preds),
discrete_predictors=list(d_preds),
targetname=target,
subset=subset,
timesplit=timesplit,
filename=filename,
C_vals=C_vals,
#Get the required permutations of the predictors:
permuted_predictors = [list(all_predictors)]
permuted_predictor_types = [list(all_predictor_types)]
if do_all_permutations == 'all':
permuted_predictors,permuted_predictor_types = permute_predictors(all_predictors,all_predictor_types)
#print permuted_predictors
#Iterate through all combinations of permutations and shards:
for i,monthkey in enumerate(gv.months.keys()):
for j,hourkey in enumerate(gv.hours.keys()):
tablename = 'flightdelays_{month}_{hour}'.format(month=monthkey,hour=hourkey)
for k,predictors in enumerate(permuted_predictors):
#Get the predictors in order:
predictors = np.array(predictors)
predictor_types = np.array(permuted_predictor_types[k])
c_preds = predictors[predictor_types == 0]
d_preds = predictors[predictor_types == 1]
#Create a pickle filename based on the predictors and month/hour:
filename = ff.make_model_pickle_filename(tablename,predictors,dir_structure="../saved_models/")
#Test if pickle file exists already:
file_exists = os.path.isfile(filename)
print filename,file_exists
#sys.exit(1)
if overwrite == 'yes' or file_exists == False:
dl.train_log(tablename,continuous_predictors=list(c_preds),discrete_predictors=list(d_preds),targetname=target,subset=subset,timesplit=timesplit,filename=filename,C_vals=C_vals)
#sys.exit(1)
def make_predictions(inpdict,pickle_dict,other_times=True,date_range=3,other_options=3):
'''
Predict the user's itinerary, as well as itineraries at nearby times/dates, and if similar
itineraries exist predict them too. This is the main function in the site, and it powers
the calculations behind the data shown to the user.
Arguments:
inpdict -- a dictionary containing the parameters of the user's search.
pickle_dict -- a dictionary containing the model pickles, keyed on the filenames of the pickles.
other_times -- Predict delays for other times-of-day (default=True).
date_range -- how many days before/after the selected day to predict (default = 3).
other_options -- maximum number of alternative itineraries to display (default = 3).
Returns:
return_dict -- a dictionary containing all the results of the predictions.
'''
#Some prepwork, with the location of the learned models (the pickles) hardcoded in:
tableprefix = 'flightdelays'
pkl_directory = os.path.join(projectdir,'saved_models/')
return_dict = {}
#Reading in the date and time and making a datetime object:
datestring = inpdict['date'][0]
timestring = inpdict['time'][0]
date = dt.datetime.strptime(datestring + " " + timestring,'%m/%d/%Y %I:%M %p')
#What things we're going to base our prediction on (the month and time of day are encoded in the different pickles):
predictorlist = ['origin','dest','dayofweek(flightdate)','uniquecarrier']
#Get the delay time prediction the user asked for:
user_predictorvals = [inpdict['origin'],inpdict['dest'],fix_weekday(date.weekday()),inpdict['uniquecarrier']]
user_predictor_df = make_predictor_df(predictorlist,user_predictorvals)
#Get the filename of the pickle we should be using:
user_pkl_filename = ff.get_model_filename(date,predictorlist,tableprefix=tableprefix,dir_structure=pkl_directory)
user_output_df = pdl.predict_delay(user_predictor_df,user_pkl_filename,pickle_obj=pickle_dict[user_pkl_filename])
return_dict['user_prediction'] = user_output_df
#Get predicted delay times for the other times of the day:
if other_times:
#Figure out what the times actually *are*:
all_time_keys = np.array(gv.hours.keys())
time_period_start = np.array([gv.hours[key][0] for key in all_time_keys])
sorted_idxs = np.argsort(time_period_start)
sorted_time_keys = all_time_keys[sorted_idxs]
all_time_df_list = []
#Find the pickle file for each time and compute the prediction:
for i,time_period in enumerate(sorted_time_keys):
time_table_name = tableprefix+"_"+ff.get_month_name(date)+"_"+time_period
time_pkl_filename = ff.make_model_pickle_filename(time_table_name,predictorlist,dir_structure=pkl_directory)
time_df = pdl.predict_delay(user_predictor_df,time_pkl_filename,pickle_obj=pickle_dict[time_pkl_filename])
all_time_df_list.append(time_df)
#Put the predictions all together into a dataframe, add it to the returned dictionary:
all_time_df = combo_dfs(*all_time_df_list)
all_time_df = all_time_df.set_index(sorted_time_keys)
col_index_df = pd.DataFrame(np.arange(len(all_time_df.columns.values)).reshape(1,len(all_time_df.columns.values)),index=['col_order'],columns=all_time_df.columns.values)
all_time_df = all_time_df.append(col_index_df)
all_time_df['order'] = range(all_time_df.shape[0])#Add column to order the rows
return_dict['all_time_prediction'] = all_time_df
#Get predicted delay times for the days around the selected date:
if date_range != None:
all_date_df_list = []
all_date_strings = []
#Get the right pickle files, make the prediction:
for i in range(-date_range,date_range+1):
offsetdate = date + dt.timedelta(days=i)
time_predictorvals = [inpdict['origin'],inpdict['dest'],fix_weekday(offsetdate.weekday()),inpdict['uniquecarrier']]#The +2 is to put the day of the week in the right MySQL units.
date_predictor_df = make_predictor_df(predictorlist,time_predictorvals)
date_pkl_filename = ff.get_model_filename(offsetdate,predictorlist,tableprefix=tableprefix,dir_structure=pkl_directory)
date_df = pdl.predict_delay(date_predictor_df,date_pkl_filename,pickle_obj=pickle_dict[date_pkl_filename])
all_date_df_list.append(date_df)
#all_date_strings.append(offsetdate.strftime('%B'))
all_date_strings.append(offsetdate.strftime('%m-%d'))
#Put in a dataframe for output:
all_date_df = combo_dfs(*all_date_df_list)
all_date_df = all_date_df.set_index(np.array(all_date_strings))
col_index_df = pd.DataFrame(np.arange(len(all_date_df.columns.values)).reshape(1,len(all_date_df.columns.values)),index=['col_order'],columns=all_date_df.columns.values)
all_date_df = all_date_df.append(col_index_df)
all_date_df['order'] = range(all_date_df.shape[0])
return_dict['all_date_prediction'] = all_date_df
#Check if there are other itineraries in the database that are similar to the one the user chose, then make predictions for those:
if other_options != None:
#Query the db to find market ids for the two airports. Make sure both are found.
#First, figuring out what the database is. This is going to be kludgey as balls, but I think it'll work (note 7/3/14: yep, works):
monthname = ff.get_month_name(date)
timename = ff.get_time_name(date)
dbname = 'Flightdelays{month}{time}'.format(month=monthname[:1].upper()+monthname[1:],time=timename[:1].upper()+timename[1:])
dbname_sql = 'flightdelays_{month}_{time}'.format(month=monthname,time=timename)
#print dbname
#Importing the database class:
model_module = importlib.import_module('delayr.models')
flightclass = getattr(model_module,dbname)
#Get the market ids for the origin and destination airports:
market_ids_qset = flightclass.objects.raw("""select fid,origin,origincitymarketid from {0:s} where origin = '{1:s}' or origin = '{2:s}' group by origin""".format(dbname_sql,inpdict['origin'][0],inpdict['dest'][0]))
market_ids = dict([(entry.origin,entry.origincitymarketid) for entry in market_ids_qset])
origin_market_id = market_ids[inpdict['origin'][0]]
dest_market_id = market_ids[inpdict['dest'][0]]
#Query the db to find all distinct flights with the same origin and destination market ids. if it returns zero (user could ask for hawaii air flights between newark and jfk, for instance, which is nonsensical but will output a prediction) exit.
cursor = connection.cursor()
columns = ['origin','dest','uniquecarrier','orig_airportname','dest_airportname','airlinename']
cursor.execute("""select distinct fd.origin,fd.dest,fd.uniquecarrier,aorig.airportname,adest.airportname,aline.fullname from {0:s} as fd join airports as aorig on fd.origin=aorig.origin join airports as adest on fd.dest=adest.origin join airlinenames as aline on fd.uniquecarrier=aline.uniquecarrier where origincitymarketid={1:d} and destcitymarketid={2:d} and year(fd.flightdate) > 2012""".format(dbname_sql,origin_market_id,dest_market_id))
result = list(cursor.fetchall())
if len(result) > 0:
#print len(result)
result_df = pd.DataFrame(result,columns=columns)
#Cut out the itinerary that the user selected, if necessary:
searched_itinerary = (result_df['origin'] == inpdict['origin'][0]) & (result_df['dest'] == inpdict['dest'][0]) & (result_df['uniquecarrier'] == inpdict['uniquecarrier'][0])
pd.set_option('display.max_columns',10)
pd.set_option('display.width',1000)
#print result_df
result_df = result_df[searched_itinerary == False]
result_df['delay'] = 0.
#print result_df
if result_df.shape[0] > 0:
#Predict the delay time for each other itinerary:
for i in result_df.index.values:
#Only have to change a few values from the original searched itinerary, so make a copy of that dataframe and edit what you need to:
temp_predictor_df = user_predictor_df.copy()
temp_predictor_df.xs('origin',axis=1,copy=False)[0] = result_df.xs('origin',axis=1,copy=False)[i]
temp_predictor_df.xs('dest',axis=1,copy=False)[0] = result_df.xs('dest',axis=1,copy=False)[i]
temp_predictor_df.xs('uniquecarrier',axis=1,copy=False)[0] = result_df.xs('uniquecarrier',axis=1,copy=False)[i]
temp_output_df = pdl.predict_delay(temp_predictor_df,user_pkl_filename,pickle_obj=pickle_dict[user_pkl_filename])
result_df.xs('delay',axis=1,copy=False)[i] = temp_output_df.icol(0)
#Adjust the naming of the dataframe columns:
all_result_cols = result_df.columns.values
all_result_cols[-1] = temp_output_df.columns.values[0]
result_df.columns = all_result_cols
#Sort the predictions, take the N best:
sorted_result_df = result_df.sort(all_result_cols[-1],ascending=False)
trimmed_sorted_result_df = sorted_result_df[:other_options]
#print "test1",trimmed_sorted_result_df
#print "test2",user_output_df
#Get the full names for the itinerary the user searched for, add to the output dataframe:
user_origin,user_dest,user_carrier = get_full_names(inpdict['origin'][0],inpdict['dest'][0],inpdict['uniquecarrier'][0])
full_user_output_df = pd.DataFrame([[inpdict['origin'][0],inpdict['dest'][0],inpdict['uniquecarrier'][0],user_origin,user_dest,user_carrier,user_output_df[user_output_df.columns.values[0]].values[0]]],columns=trimmed_sorted_result_df.columns)
trimmed_sorted_result_df = trimmed_sorted_result_df.append(full_user_output_df,ignore_index=True)
#Determine what color to make the user's itinerary:
other_option_colorname = 'warning'
if trimmed_sorted_result_df[trimmed_sorted_result_df.columns.values[-1]].values[-1] > trimmed_sorted_result_df[trimmed_sorted_result_df.columns.values[-1]].values[0]:
other_option_colorname = 'success'
elif trimmed_sorted_result_df[trimmed_sorted_result_df.columns.values[-1]].values[-2] > trimmed_sorted_result_df[trimmed_sorted_result_df.columns.values[-1]].values[-1]:
other_option_colorname = 'danger'
return_dict['other_option_colorname'] = other_option_colorname
stringlist = []
#fill out the list of the best alternate options, put it in the output dictionary:
for i in range(trimmed_sorted_result_df.shape[0]):
rowvals = trimmed_sorted_result_df.irow(i)
testdict = {'display_string':"{0:s} to {1:s} on {2:s} (prediction: {3:.2f}% chance of delay {4:s} minutes)".format(rowvals['orig_airportname'],rowvals['dest_airportname'],rowvals['airlinename'],rowvals[all_result_cols[-1]]*100.,all_result_cols[-1]),'orig':rowvals['origin'],'dest':rowvals['dest'],'uniquecarrier':rowvals['uniquecarrier']}
stringlist.append(testdict)
return_dict['other_option_prediction'] = stringlist
return return_dict
def make_predictions(inpdict,
pickle_dict,
other_times=True,
date_range=3,
other_options=3):
'''
Predict the user's itinerary, as well as itineraries at nearby times/dates, and if similar
itineraries exist predict them too. This is the main function in the site, and it powers
the calculations behind the data shown to the user.
Arguments:
inpdict -- a dictionary containing the parameters of the user's search.
pickle_dict -- a dictionary containing the model pickles, keyed on the filenames of the pickles.
other_times -- Predict delays for other times-of-day (default=True).
date_range -- how many days before/after the selected day to predict (default = 3).
other_options -- maximum number of alternative itineraries to display (default = 3).
Returns:
return_dict -- a dictionary containing all the results of the predictions.
'''
#Some prepwork, with the location of the learned models (the pickles) hardcoded in:
tableprefix = 'flightdelays'
pkl_directory = os.path.join(projectdir, 'saved_models/')
return_dict = {}
#Reading in the date and time and making a datetime object:
datestring = inpdict['date'][0]
timestring = inpdict['time'][0]
date = dt.datetime.strptime(datestring + " " + timestring,
'%m/%d/%Y %I:%M %p')
#What things we're going to base our prediction on (the month and time of day are encoded in the different pickles):
predictorlist = [
'origin', 'dest', 'dayofweek(flightdate)', 'uniquecarrier'
]
#Get the delay time prediction the user asked for:
user_predictorvals = [
inpdict['origin'], inpdict['dest'],
fix_weekday(date.weekday()), inpdict['uniquecarrier']
]
user_predictor_df = make_predictor_df(predictorlist, user_predictorvals)
#Get the filename of the pickle we should be using:
user_pkl_filename = ff.get_model_filename(date,
predictorlist,
tableprefix=tableprefix,
dir_structure=pkl_directory)
user_output_df = pdl.predict_delay(
user_predictor_df,
user_pkl_filename,
pickle_obj=pickle_dict[user_pkl_filename])
return_dict['user_prediction'] = user_output_df
#Get predicted delay times for the other times of the day:
if other_times:
#Figure out what the times actually *are*:
all_time_keys = np.array(gv.hours.keys())
time_period_start = np.array(
[gv.hours[key][0] for key in all_time_keys])
sorted_idxs = np.argsort(time_period_start)
sorted_time_keys = all_time_keys[sorted_idxs]
all_time_df_list = []
#Find the pickle file for each time and compute the prediction:
for i, time_period in enumerate(sorted_time_keys):
time_table_name = tableprefix + "_" + ff.get_month_name(
date) + "_" + time_period
time_pkl_filename = ff.make_model_pickle_filename(
time_table_name, predictorlist, dir_structure=pkl_directory)
time_df = pdl.predict_delay(
user_predictor_df,
time_pkl_filename,
pickle_obj=pickle_dict[time_pkl_filename])
all_time_df_list.append(time_df)
#Put the predictions all together into a dataframe, add it to the returned dictionary:
all_time_df = combo_dfs(*all_time_df_list)
all_time_df = all_time_df.set_index(sorted_time_keys)
col_index_df = pd.DataFrame(np.arange(len(
all_time_df.columns.values)).reshape(
1, len(all_time_df.columns.values)),
index=['col_order'],
columns=all_time_df.columns.values)
all_time_df = all_time_df.append(col_index_df)
all_time_df['order'] = range(
all_time_df.shape[0]) #Add column to order the rows
return_dict['all_time_prediction'] = all_time_df
#Get predicted delay times for the days around the selected date:
if date_range != None:
all_date_df_list = []
all_date_strings = []
#Get the right pickle files, make the prediction:
for i in range(-date_range, date_range + 1):
offsetdate = date + dt.timedelta(days=i)
time_predictorvals = [
inpdict['origin'], inpdict['dest'],
fix_weekday(offsetdate.weekday()), inpdict['uniquecarrier']
] #The +2 is to put the day of the week in the right MySQL units.
date_predictor_df = make_predictor_df(predictorlist,
time_predictorvals)
date_pkl_filename = ff.get_model_filename(
offsetdate,
predictorlist,
tableprefix=tableprefix,
dir_structure=pkl_directory)
date_df = pdl.predict_delay(
date_predictor_df,
date_pkl_filename,
pickle_obj=pickle_dict[date_pkl_filename])
all_date_df_list.append(date_df)
#all_date_strings.append(offsetdate.strftime('%B'))
all_date_strings.append(offsetdate.strftime('%m-%d'))
#Put in a dataframe for output:
all_date_df = combo_dfs(*all_date_df_list)
all_date_df = all_date_df.set_index(np.array(all_date_strings))
col_index_df = pd.DataFrame(np.arange(len(
all_date_df.columns.values)).reshape(
1, len(all_date_df.columns.values)),
index=['col_order'],
columns=all_date_df.columns.values)
all_date_df = all_date_df.append(col_index_df)
all_date_df['order'] = range(all_date_df.shape[0])
return_dict['all_date_prediction'] = all_date_df
#Check if there are other itineraries in the database that are similar to the one the user chose, then make predictions for those:
if other_options != None:
#Query the db to find market ids for the two airports. Make sure both are found.
#First, figuring out what the database is. This is going to be kludgey as balls, but I think it'll work (note 7/3/14: yep, works):
monthname = ff.get_month_name(date)
timename = ff.get_time_name(date)
dbname = 'Flightdelays{month}{time}'.format(
month=monthname[:1].upper() + monthname[1:],
time=timename[:1].upper() + timename[1:])
dbname_sql = 'flightdelays_{month}_{time}'.format(month=monthname,
time=timename)
#print dbname
#Importing the database class:
model_module = importlib.import_module('delayr.models')
flightclass = getattr(model_module, dbname)
#Get the market ids for the origin and destination airports:
market_ids_qset = flightclass.objects.raw(
"""select fid,origin,origincitymarketid from {0:s} where origin = '{1:s}' or origin = '{2:s}' group by origin"""
.format(dbname_sql, inpdict['origin'][0], inpdict['dest'][0]))
market_ids = dict([(entry.origin, entry.origincitymarketid)
for entry in market_ids_qset])
origin_market_id = market_ids[inpdict['origin'][0]]
dest_market_id = market_ids[inpdict['dest'][0]]
#Query the db to find all distinct flights with the same origin and destination market ids. if it returns zero (user could ask for hawaii air flights between newark and jfk, for instance, which is nonsensical but will output a prediction) exit.
cursor = connection.cursor()
columns = [
'origin', 'dest', 'uniquecarrier', 'orig_airportname',
'dest_airportname', 'airlinename'
]
cursor.execute(
"""select distinct fd.origin,fd.dest,fd.uniquecarrier,aorig.airportname,adest.airportname,aline.fullname from {0:s} as fd join airports as aorig on fd.origin=aorig.origin join airports as adest on fd.dest=adest.origin join airlinenames as aline on fd.uniquecarrier=aline.uniquecarrier where origincitymarketid={1:d} and destcitymarketid={2:d} and year(fd.flightdate) > 2012"""
.format(dbname_sql, origin_market_id, dest_market_id))
result = list(cursor.fetchall())
if len(result) > 0:
#print len(result)
result_df = pd.DataFrame(result, columns=columns)
#Cut out the itinerary that the user selected, if necessary:
searched_itinerary = (
result_df['origin'] == inpdict['origin'][0]) & (
result_df['dest']
== inpdict['dest'][0]) & (result_df['uniquecarrier']
== inpdict['uniquecarrier'][0])
pd.set_option('display.max_columns', 10)
pd.set_option('display.width', 1000)
#print result_df
result_df = result_df[searched_itinerary == False]
result_df['delay'] = 0.
#print result_df
if result_df.shape[0] > 0:
#Predict the delay time for each other itinerary:
for i in result_df.index.values:
#Only have to change a few values from the original searched itinerary, so make a copy of that dataframe and edit what you need to:
temp_predictor_df = user_predictor_df.copy()
temp_predictor_df.xs('origin', axis=1,
copy=False)[0] = result_df.xs(
'origin', axis=1, copy=False)[i]
temp_predictor_df.xs('dest', axis=1,
copy=False)[0] = result_df.xs(
'dest', axis=1, copy=False)[i]
temp_predictor_df.xs('uniquecarrier', axis=1,
copy=False)[0] = result_df.xs(
'uniquecarrier',
axis=1,
copy=False)[i]
temp_output_df = pdl.predict_delay(
temp_predictor_df,
user_pkl_filename,
pickle_obj=pickle_dict[user_pkl_filename])
result_df.xs('delay', axis=1,
copy=False)[i] = temp_output_df.icol(0)
#Adjust the naming of the dataframe columns:
all_result_cols = result_df.columns.values
all_result_cols[-1] = temp_output_df.columns.values[0]
result_df.columns = all_result_cols
#Sort the predictions, take the N best:
sorted_result_df = result_df.sort(all_result_cols[-1],
ascending=False)
trimmed_sorted_result_df = sorted_result_df[:other_options]
#print "test1",trimmed_sorted_result_df
#print "test2",user_output_df
#Get the full names for the itinerary the user searched for, add to the output dataframe:
user_origin, user_dest, user_carrier = get_full_names(
inpdict['origin'][0], inpdict['dest'][0],
inpdict['uniquecarrier'][0])
full_user_output_df = pd.DataFrame(
[[
inpdict['origin'][0], inpdict['dest'][0],
inpdict['uniquecarrier'][0], user_origin, user_dest,
user_carrier, user_output_df[
user_output_df.columns.values[0]].values[0]
]],
columns=trimmed_sorted_result_df.columns)
trimmed_sorted_result_df = trimmed_sorted_result_df.append(
full_user_output_df, ignore_index=True)
#Determine what color to make the user's itinerary:
other_option_colorname = 'warning'
if trimmed_sorted_result_df[
trimmed_sorted_result_df.columns.
values[-1]].values[-1] > trimmed_sorted_result_df[
trimmed_sorted_result_df.columns.
values[-1]].values[0]:
other_option_colorname = 'success'
elif trimmed_sorted_result_df[
trimmed_sorted_result_df.columns.
values[-1]].values[-2] > trimmed_sorted_result_df[
trimmed_sorted_result_df.columns.
values[-1]].values[-1]:
other_option_colorname = 'danger'
return_dict['other_option_colorname'] = other_option_colorname
stringlist = []
#fill out the list of the best alternate options, put it in the output dictionary:
for i in range(trimmed_sorted_result_df.shape[0]):
rowvals = trimmed_sorted_result_df.irow(i)
testdict = {
'display_string':
"{0:s} to {1:s} on {2:s} (prediction: {3:.2f}% chance of delay {4:s} minutes)"
.format(rowvals['orig_airportname'],
rowvals['dest_airportname'],
rowvals['airlinename'],
rowvals[all_result_cols[-1]] * 100.,
all_result_cols[-1]),
'orig':
rowvals['origin'],
'dest':
rowvals['dest'],
'uniquecarrier':
rowvals['uniquecarrier']
}
stringlist.append(testdict)
return_dict['other_option_prediction'] = stringlist
return return_dict
