Fin predict script

py/predict.py

@@ -6,38 +6,143 @@ import datetime as dt
 import pickle
 from pymodels import thirtyoffset, predweather, harmonic
 from pprint import pprint
+from util import datevalid
+from signal import signal, SIGPIPE, SIG_DFL
+
+def preddemand(model, weather, harmonics):
+    harmstart = thirtyoffset(weather.index.min())
+    utv = weather.index
+    harmlen = len(utv)
+
+    yharm = harmonic(harmlen, period=365.25 * 48, start=harmstart,
+                     harmonics=harmonics[0], prefix="y")
+    yharm.index = utv
+
+    wharm = harmonic(harmlen, period=7 * 48, start=harmstart,
+                     harmonics=harmonics[1], prefix="w")
+    wharm.index = utv
+
+    dharm = harmonic(harmlen, period=48, start=harmstart,
+                     harmonics=harmonics[2], prefix="d")
+    dharm.index = utv
+
+    df = p.concat([yharm, wharm, dharm, weather], axis = 1)
+    return model.predict(df)
 
 def main():
     parser = ArgumentParser(description=
                             'Predict from harmonic model of cluster')
     parser.add_argument("-m", "--model-file", dest="model_file",
                         help="filename for models",
-                        default="../models/testmod.pkl",
+                        required = True,
+                        type=FileType('rb'))
+    parser.add_argument("-w", "--weather",  dest="weather_file",
+                        help="input weather pickle path",
+                        required=False,
                         type=FileType('rb'))
-    # parser.add_argument("-w", "--weather",  dest="weather",
-    #                     help="input weather pickle path",
-    #                     metavar="PATH", required=True,
-    #                     type=FileType('rb'))
-    parser.add_argument("--weather-harmonics", dest="weather_harmonics",
-                        help="number of harmonics for weather; default: 2",
-                        type=int, default=2, metavar="NUM")
-    parser.add_argument("--icp-harmonics", dest="icp_harmonics", nargs=3,
-                        help="harmonics for icp fitting, default 2 3 3",
-                        default=[2, 3, 3], type=int,
-                        metavar="NUM")
+    parser.add_argument("-o", "--output", dest="output_file",
+                        help="file to save result",
+                        required=True, type=FileType('w'))
+    parser.add_argument("-t", "--temperature", dest="temp",
+                        help = "min and max temperature, if not using "
+                        "weather dataset, e.g. 2.0 10.5", 
+                        required=False,
+                        type=float, nargs=2)
+    parser.add_argument("-s", "--start-date", 
+                        dest = "startdate", 
+                        help = "start date for prediction; format: YYYY-MM-DD; default: 2018-01-01", 
+                        metavar="START_DATE", 
+                        required = True,
+                        type = datevalid)
+    parser.add_argument("-e", "--end-date", 
+                        dest = "enddate", 
+                        help = "end date for prediction; format: YYYY-MM-DD; default: 2018-02-01", 
+                        metavar="END_DATE", 
+                        required = True,
+                        type = datevalid)
+    parser.add_argument("-c", "--cluster",
+                        dest = "cluster",
+                        help = "cluster to predict for",
+                        type = int,
+                        required = True)
+    parser.add_argument("--csv",
+                        help="output as csv",
+                        action="store_true")
     args = parser.parse_args()
 
-    print(args)
+    if args.temp is None and args.weather_file is None:
+        parser.error("Either the temperature range or a weather "
+                     "dataset must be specified")
 
     mods = pickle.load(args.model_file)
 
-    pprint(mods)
+    if args.cluster not in mods["clusters"]:
+        parser.error(f"cluster ('{args.cluster}') not in model")
+
+    wdat = []
     
-    print(mods["max_temp"].predict())
+    if args.weather_file is not None:
+        weather = p.read_pickle(args.weather_file)
+        if (args.startdate < weather['temp_timestamp'].min() or 
+            args.enddate > weather['temp_timestamp'].max()):
+            parser.error("Start and or end date not in supplied weather dataset")
+
+        wantedtimes = p.date_range(args.startdate,
+                                   args.enddate, freq="30 min")
+        wantedtimes = p.Series(wantedtimes.round(freq="30 min"))
+        wanteddf = p.DataFrame(index=wantedtimes)
+        weather = wanteddf.join(weather.set_index(['temp_timestamp']),
+                                how='left').drop('temp_date', axis=1)
+        weather.index.name = "read_time"
+        weather = weather.fillna(method='ffill', axis=0)
+
+        # Add rolling columns
+        weather['min_rolling'] = weather['tmin_c'].rolling("1D").min()
+        weather['max_rolling'] = weather['tmax_c'].rolling("1D").max()
+
+        wharmstart = thirtyoffset(weather.index.min())
+        wharm = harmonic(weather.shape[0], period=365.25 * 48, start=wharmstart,
+                         harmonics=mods["weather_harmonics"])
+        wharm.index = wantedtimes
+        pred_max = weather['max_rolling'] - mods["max_temp"].predict(wharm)
+        pred_min = weather['min_rolling'] - mods["min_temp"].predict(wharm)
+        wdat = p.DataFrame({
+            "max_resid": pred_max,
+            "min_resid": pred_min
+        })
+
+    else:
+        mint = min(args.temp)
+        maxt = max(args.temp)
+        pred_max = maxt - predweather(mods["max_temp"],
+                               args.startdate,
+                               args.enddate,
+                               harmonics = mods["weather_harmonics"])
+        pred_min = mint - predweather(mods["min_temp"],
+                               args.startdate,
+                               args.enddate,
+                               harmonics = mods["weather_harmonics"])
+        wdat = p.DataFrame({
+            "max_resid": pred_max,
+            "min_resid": pred_min
+        })
+
+
+    dpred = p.DataFrame(
+        {
+            "predicted": preddemand(mods["icp"][args.cluster], wdat, mods["icp_harmonics"])
+        })
+
+    if args.csv:
+        signal(SIGPIPE, SIG_DFL) # Prevent broken pipe errors when piping to less or head
+        dpred.to_csv(args.output_file)
+    else:
+        dpred.to_pickle(args.output_file)
 
-    print(predweather(mods["max_temp"], "2019-01-01", "2019-02-01", harmonics = mods["weather_harmonics"]))
 
     args.model_file.close()
+    if args.weather_file is not None:
+        args.weather_file.close()
 
 
 if __name__ == "__main__":