def save_metadata(self):
try:
json_ = {"_col_index": self.col_index, "_col_metadata": self.col_metadata}
log.info("RESULTS: Saving Column Metadata to {0}".format(self.log_prefix, self.metadata_file_path))
save_to_JSON(self.metadata_file_path, json_)
except Exception, e:
log.warning("{0}: Failed to save Column Metadata.".format(self.log_prefix))
log.warning(e)
log.warning("{0}: File path was: {1}".format(self.log_prefix, self.metadata_file_path))
raise e
def generate_d3_JSON_ParallelCoords(self, hndl_Res):
# Historical Line Graph
# Historical Line Graph / Get History Line
(history_dates, history_values) = hndl_Res.get_resp_word_raw_values()
json_history = [DATA_SERIES_TO_JSON(d,v) for (d,v) in zip(history_dates, history_values)]
# Historical Line Graph / Write History Line
filePath = get_json_history_path(hndl_Res.file_name)
save_to_JSON(filePath, json_history)
# Parallel Coordinates
# Parallel Coordinates / Get Parallel Coords Points
models = {}
preds = []
for dt in hndl_Res.get_prediction_dates():
(model_idxs, pred_values) = hndl_Res.get_values_by_row(dt)
models.update(dict.fromkeys(model_idxs))
for (md, vl) in zip(model_idxs, pred_values):
preds.append({
JSON_MODEL_ID: md,
JSON_DATE_KEY: dt_epoch_to_str_Y_M_D(dt),
JSON_VALUE_KEY: vl
})
# Parallel Coordinates / Write Parallel Coords Points
filePath = get_json_predictions_path(hndl_Res.file_name)
save_to_JSON(filePath, preds)
# Metadata
# Metadata / Get Model Metadata
for md in models.keys():
models[md] = hndl_Res.get_model_metadata(md)
# Metadata / Write Model Metadata
filePath = get_json_model_path(hndl_Res.file_name)
save_to_JSON(filePath, models)
def generate_d3_JSON_ParallelCoords(self):
# Generate 'History'
n = 120 # 10 years
n_fd = 12
series = util_Tst.create_test_data_correlated_returns(n=n, numDims=1, includeResponse=False)
dt = util_Tst.create_monthly_date_range(n=n)
vals = series['data']
json_history = [DATA_SERIES_TO_JSON(d,v) for (d,v) in zip(dt, vals)]
# Generate Predictions
std = np_std(transform_FOD_BackwardLooking(vals,{utl_Trns.FIRST_ORDER_DIFF_TIME:1}))
end_val = vals[-1,0]
def get_random_prediction_values(per_fd):
numPreds = 40
preds = []
for i in xrange(numPreds):
preds.append(end_val + normal()*std*sqrt(per_fd))
return (range(numPreds), preds)
def get_model_metadata(model_idx):
return {
JSON_MODEL_ID : model_idx,
JSON_MODEL_CONFIDENCE : random(),
JSON_MODEL_DESC : 'junkdesc ' + str(model_idx)
}
end_dt = dt[-1]
prd_dt = util_Tst.create_monthly_date_range(n=n_fd+1, startEpoch=end_dt+10000) #hacky, but end of next month
models = {}
preds = []
for (i, dt) in enumerate(prd_dt):
(model_idxs, pred_values) = get_random_prediction_values(i)
models.update(dict.fromkeys(model_idxs))
for (md, vl) in zip(model_idxs, pred_values):
preds.append({
JSON_MODEL_ID: md,
JSON_DATE_KEY: dt_epoch_to_str_Y_M_D(dt),
JSON_VALUE_KEY: vl
})
for md in models.keys():
models[md] = get_model_metadata(md)
# Save data
dataName = 'test1'
filePath = get_json_history_path(dataName)
save_to_JSON(filePath, json_history)
filePath = get_json_predictions_path(dataName)
save_to_JSON(filePath, preds)
filePath = get_json_model_path(dataName)
save_to_JSON(filePath, models)
# if __name__ == '__main__':
# generator = EMF_TestDataGenerator()
# generator.generate_d3_JSON_ParallelCoords()