MVFTS bugfixes
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Petrônio Cândido
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8c1fec482d
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@ -39,7 +39,8 @@ class TimeGridPartitioner(partitioner.Partitioner):
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else:
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self.ordered_sets = FS.set_ordered(self.sets)
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self.extractor = lambda x: strip_datepart(x, self.season)
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if self.type == 'seasonal':
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self.extractor = lambda x: strip_datepart(x, self.season)
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def build(self, data):
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sets = {}
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@ -17,119 +17,16 @@ from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
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from pyFTS.models.seasonal import partitioner as seasonal
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from pyFTS.models.seasonal.common import DateTime
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# Multivariate time series
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from pyFTS.data import Malaysia
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train_mv = {}
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test_mv = {}
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dataset = Malaysia.get_dataframe()
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models = {}
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dataset["time"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
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for key in ['price', 'solar', 'load']:
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models[key] = []
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data = dataset['load'].values
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train_split = 8760
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train_mv = dataset.iloc[:train_split]
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test_mv = dataset.iloc[train_split:]
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sp = {'seasonality': DateTime.month , #'type': 'common',
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'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
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vmonth = variable.Variable("Month", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=12,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.day_of_week, #'type': 'common',
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'names': ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}
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vday = variable.Variable("Weekday", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=7,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.hour_of_day} #, 'type': 'common'}
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vhour = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv, partitioner_specific=sp)
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vload = variable.Variable("load", data_label="load", partitioner=Grid.GridPartitioner, npart=10,
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data=train_mv)
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"""
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model = cmvfts.ClusteredMVFTS(order=2, knn=3, cluster_params={'optmize': True})
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model.append_variable(vmonthp)
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model.append_variable(vdayp)
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model.append_variable(vhourp)
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model.append_variable(vload)
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model.target_variable = vload
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model.fit(train_mv)
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print(len(model.cluster.sets.keys()))
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model.cluster.prune()
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print(len(model.cluster.sets.keys()))
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model.predict(test_mv)
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"""
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'''
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from pyFTS.data import Malaysia
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dataset = Malaysia.get_dataframe()
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dataset["date"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
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train_mv = dataset.iloc[:10000]
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test_mv = dataset.iloc[10000:]
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sp = {'seasonality': DateTime.month , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
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vmonth = variable.Variable("Month", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=12,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.day_of_week, 'names': ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}
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vday = variable.Variable("Weekday", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=7,
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data=train_mv, partitioner_specific=sp)
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sp = {'seasonality': DateTime.hour_of_day}
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vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv, partitioner_specific=sp)
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vload = variable.Variable("load", data_label="load", partitioner=Grid.GridPartitioner, npart=10,
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data=train_mv)
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vtemperature = variable.Variable("temperature", data_label="temperature", partitioner=Grid.GridPartitioner, npart=10,
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data=train_mv)
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"""
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variables = {
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'month': vmonth,
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'day': vday,
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'hour': vhour,
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'temperature': vtemperature,
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'load': vload
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}
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var_list = [k for k in variables.keys()]
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models = []
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import itertools
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for k in [itertools.combinations(var_list, r) for r in range(2,len(var_list))]:
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for x in k:
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model = mvfts.MVFTS()
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print(x)
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for w in x:
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model.append_variable(variables[w])
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model.shortname += ' ' + w
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model.target_variable = vload
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model.fit(mv_train)
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models.append(model)
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"""
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"""
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dataset = pd.read_csv('/home/petronio/Downloads/priceHong')
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dataset['hour'] = dataset.index.values % 24
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@ -137,71 +34,100 @@ data = dataset['price'].values.flatten()
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train_split = 24 * 800
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# Multivariate time series
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train_mv = dataset.iloc[:train_split]
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test_mv = dataset.iloc[train_split:]
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train_mv['price'] = dataset.iloc[:train_split]
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test_mv['price'] = dataset.iloc[train_split:]
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#model = Util.load_obj('/home/petronio/Downloads/ClusteredMVFTS4')
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dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
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dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
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train_mv['solar'] = dataset.iloc[:24505]
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test_mv['solar'] = dataset.iloc[24505:]
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from pyFTS.data import Malaysia
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dataset = Malaysia.get_dataframe()
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dataset["time"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
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train_mv['load'] = dataset.iloc[:train_split]
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test_mv['load'] = dataset.iloc[train_split:]
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exogenous = {}
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endogenous = {}
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for key in models.keys():
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exogenous[key] = {}
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vhour = variable.Variable("Hour", data_label="hour", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=dataset,
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data=train_mv['price'],
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partitioner_specific={'seasonality': DateTime.hour_of_day, 'type': 'common'})
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exogenous['price']['Hour'] = vhour
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vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=55,
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data=train_mv)
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model = cmvfts.ClusteredMVFTS(order=2, knn=3)
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model.append_variable(vhour)
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model.append_variable(vprice)
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model.target_variable = vprice
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model.fit(train_mv)
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data=train_mv['price'])
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endogenous['price'] = vprice
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data = [[1, 1.0], [2, 2.0]]
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df = pd.DataFrame(data, columns=['hour','price'])
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forecasts = model.predict(df, steps_ahead=24, generators={'Hour': lambda x : (x+1)%24 })
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"""
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'''
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sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k) for k in range(0,24)]}
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params = [
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{},
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{},
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{'order': 2, 'knn': 3, 'cluster_params': {'optmize': True}},
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{'order': 2, 'knn': 2, 'cluster_params': {'optmize': True}},
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{'order': 2, 'knn': 1, 'cluster_params': {'optmize': True}}
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vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv['solar'], partitioner_specific=sp)
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exogenous['solar']['Hour'] = vhour
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vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=30, alpha_cut=.3,
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data=train_mv['solar'])
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endogenous['solar'] = vavg
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sp = {'seasonality': DateTime.hour_of_day}
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vhourp = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
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data=train_mv['load'], partitioner_specific=sp)
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exogenous['load']['Hour'] = vhourp
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vload = variable.Variable("load", data_label="load", partitioner=Grid.GridPartitioner, npart=10,
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data=train_mv['load'])
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endogenous['load'] = vload
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
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fig, ax = plt.subplots(nrows=3, ncols=1, figsize=[15,15])
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parameters = [
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{},{},
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{'order':2, 'knn': 1},
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{'order':2, 'knn': 2},
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{'order':2, 'knn': 3},
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]
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from pyFTS.models.multivariate import grid
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for ct, key in enumerate(models.keys()):
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cluster = None
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for ct2, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
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cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS,cmvfts.ClusteredMVFTS]):
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print(key, method, parameters[ct2])
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model = method(**parameters[ct2])
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_key2 = ""
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for k in parameters[ct2].keys():
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_key2 += k + str(parameters[ct2][k])
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model.shortname += str(ct) + key + _key2
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for var in exogenous[key].values():
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model.append_variable(var)
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model.append_variable(endogenous[key])
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model.target_variable = endogenous[key]
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model.fit(train_mv[key])
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models[key].append(model.shortname)
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for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS]):
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Util.persist_obj(model, model.shortname)
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model = method(**params[ct])
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model.append_variable(vmonth)
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model.append_variable(vday)
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model.append_variable(vhour)
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model.append_variable(vload)
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model.target_variable = vload
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model.fit(train_mv)
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if method == cmvfts.ClusteredMVFTS:
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model.cluster.prune()
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try:
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print(model.shortname, params[ct], Measures.get_point_statistics(test_mv, model))
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except Exception as ex:
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print(model.shortname, params[ct])
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print(ex)
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print("\n\n==============================================\n\n")
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#print(model1)
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#print(model1.predict(test_mv, steps_ahead=24, generators={'Hour': lambda x : (x+1)%24 }))
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#'''
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del(model)
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