data.Malaysia added; Several improvements on pyFTS.data modules

pyFTS/data/Bitcoin.py

@@ -28,7 +28,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/72gews5w3c7oaf7by5vp7evsasluia')
+    df = common.get_dataframe("BTCUSD.csv", "https://query.data.world/s/72gews5w3c7oaf7by5vp7evsasluia",
+                              sep=",")
 
     return df
 

pyFTS/data/DowJones.py

@@ -29,7 +29,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/d4hfir3xrelkx33o3bfs5dbhyiztml')
+    df = common.get_dataframe("DowJones.csv", "https://query.data.world/s/d4hfir3xrelkx33o3bfs5dbhyiztml",
+                              sep=",")
 
     return df
 

pyFTS/data/EURGBP.py

@@ -27,7 +27,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/gvsaeruthnxjkwzl7z4ki7u5rduah3')
+    df = common.get_dataframe("EURGBP.csv", "https://query.data.world/s/gvsaeruthnxjkwzl7z4ki7u5rduah3",
+                              sep=",")
 
     return df
 

pyFTS/data/EURUSD.py

@@ -27,7 +27,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/od4eojioz4w6o5bbwxjfn6j5zoqtos')
+    df = common.get_dataframe("EURUSD.csv", "https://query.data.world/s/od4eojioz4w6o5bbwxjfn6j5zoqtos",
+                              sep=",")
 
     return df
 

pyFTS/data/Ethereum.py

@@ -29,7 +29,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/qj4ly7o4rl7oq527xzy4v76wkr3hws')
+    df = common.get_dataframe("ETHUSD.csv", "https://query.data.world/s/qj4ly7o4rl7oq527xzy4v76wkr3hws",
+                              sep=",")
 
     return df
 

pyFTS/data/GBPUSD.py

@@ -27,7 +27,8 @@ def get_dataframe():
 
     :return: Pandas DataFrame
     """
-    df = pd.read_csv('https://query.data.world/s/sw4mijpowb3mqv6bsat7cdj54hyxix')
+    df = common.get_dataframe("GBPUSD.csv", "https://query.data.world/s/sw4mijpowb3mqv6bsat7cdj54hyxix",
+                              sep=",")
 
     return df
 

pyFTS/data/Malaysia.py

@@ -0,0 +1,34 @@
+"""
+Hourly Malaysia eletric load and tempeature
+"""
+
+
+from pyFTS.data import common
+import pandas as pd
+import numpy as np
+
+
+def get_data(field='load'):
+    """
+    Get the univariate time series data.
+
+    :param field: dataset field to load
+    :return: numpy array
+    """
+    dat = get_dataframe()
+    return np.array(dat[field])
+
+
+def get_dataframe():
+    """
+    Get the complete multivariate time series data.
+
+    :return: Pandas DataFrame
+    """
+    df = common.get_dataframe("malaysia.csv","https://query.data.world/s/e5arbthdytod3m7wfcg7gmtluh3wa5",
+                               sep=";")
+
+    return df
+
+    return df
+

pyFTS/tests/multivariate.py

@@ -4,11 +4,13 @@ from pyFTS.data import TAIEX as tx
 from pyFTS.common import Transformations
 
 
-from pyFTS.data import SONDA
+from pyFTS.data import Malaysia
-df = SONDA.get_dataframe()
+
-train = df.iloc[0:578241] #three years
+dataset = Malaysia.get_dataframe()
-test = df.iloc[1572480:2096640] #one year
+
-del df
+print(dataset.head())
+
+dataset["date"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
 
 from pyFTS.partitioners import Grid, Util as pUtil
 from pyFTS.common import Transformations, Util
@@ -19,49 +21,30 @@ from pyFTS.models.seasonal.common import DateTime
 bc = Transformations.BoxCox(0)
 tdiff = Transformations.Differential(1)
 
-np = 10
+from pyFTS.models.multivariate import common, variable, mvfts
+from pyFTS.models.seasonal import partitioner as seasonal
+from pyFTS.models.seasonal.common import DateTime
 
+mv_train = dataset.iloc[:100000]
 
-model = mvfts.MVFTS("")
+sp = {'seasonality': DateTime.month , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
 
-fig, axes = plt.subplots(nrows=5, ncols=1,figsize=[15,10])
+vmonth = variable.Variable("Month", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=12, 
+                         data=mv_train, partitioner_specific=sp)
 
+sp = {'seasonality': DateTime.day_of_week, 'names': ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']}
 
-sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
+vday = variable.Variable("Weekday", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=7, 
+                        data=mv_train, partitioner_specific=sp)
 
-vmonth = variable.Variable("Month", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=12,
+sp = {'seasonality': DateTime.hour_of_day}
-                           data=train, partitioner_specific=sp)
-vmonth.partitioner.plot(axes[0])
 
-sp = {'seasonality': DateTime.minute_of_day}
+vhour = variable.Variable("Hour", data_label="date", partitioner=seasonal.TimeGridPartitioner, npart=24, 
+                        data=mv_train, partitioner_specific=sp)
 
-vhour = variable.Variable("Hour", data_label="datahora", partitioner=seasonal.TimeGridPartitioner, npart=24,
+vload = variable.Variable("load", data_label="load", partitioner=Grid.GridPartitioner, npart=10, 
-                          data=train, partitioner_specific=sp)
+                       data=mv_train)
 
-vhour.partitioner.plot(axes[1])
+vtemperature = variable.Variable("temperature", data_label="temperature", partitioner=Grid.GridPartitioner, npart=10, 
+                       data=mv_train)
 
-vavg = variable.Variable("Radiance", data_label="glo_avg", partitioner=Grid.GridPartitioner, npart=30,
-                         data=train)
-
-model1 = mvfts.MVFTS("")
-
-model1.append_variable(vmonth)
-
-model1.append_variable(vhour)
-
-model1.append_variable(vavg)
-
-model1.target_variable = vavg
-
-#model1.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
-
-
-#model.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
-
-#model1.fit(train, num_batches=200, save=True, batch_save=True, file_path='mvfts_sonda', distributed=False,
-#          nodes=['192.168.0.110'], batch_save_interval=10)
-
-
-model = Util.load_obj('mvfts_sonda')
-
-forecasts = model.predict(test)