rm unused file

analytics/prophet_algorithm.py

@@ -1,46 +0,0 @@
-from fbprophet import Prophet
-import pandas as pd
-
-
-class prophet_algorithm(object):
-    def __init__(self):
-        self.model = None
-        self.dataset = None
-
-    def fit(self, data, anomalies):
-        pass
-
-    def predict(self, data):
-        data = data.reset_index()
-        data = data.rename(columns={'timestamp': 'ds', 'value': 'y'})
-        self.dataset = data
-
-        self.model = Prophet(yearly_seasonality=False, weekly_seasonality=False, daily_seasonality=True)
-        self.model.fit(self.dataset)
-
-        future = self.model.make_future_dataframe(freq='H', periods=0, include_history=True)
-        forecast = self.model.predict(future)
-        cmp_df = forecast.set_index('ds')[['yhat', 'yhat_lower', 'yhat_upper']].join(self.dataset.set_index('ds'))
-        cmp_df['e'] = [ max(row.y - row.yhat_upper, row.yhat_lower - row.y, 0) for index, row in cmp_df.iterrows() ]
-        return self.__calc_anomalies(cmp_df)
-
-    def __calc_anomalies(self, dataset):
-        anomalies = []
-        cur_anomaly = None
-        for i in range(len(dataset)):
-            if dataset['e'][i] > 17:
-                if cur_anomaly is None:
-                    cur_anomaly = {'start': dataset.index[i], 'finish': dataset.index[i], 'weight': 0}
-                cur_anomaly['finish'] = dataset.index[i]
-                cur_anomaly['weight'] += dataset['e'][i]
-            elif cur_anomaly is not None:
-                anomalies.append(cur_anomaly)
-                cur_anomaly = None
-        return anomalies
-
-
-if __name__ == "__main__":
-    dataset = pd.read_csv('art_daily_flatmiddle.csv', index_col=['timestamp'], parse_dates=['timestamp'])
-    algo = prophet_algorithm(dataset)
-    res = algo.fit()
-    print(res)