app.tests module

app.tests.eval_and_sum(munger, weighting_keys, k_depth, discount, write_graph=False)[source]

Evaluates all metrics and summarize using the graph output from a munger. :param munger: the munger to analyze :param write_graph: whether or not to write the network to a graph ml file :param weighting_keys: the weighting key to use for knowledge calculation :param k_depth: the maximum depth to evaluate k :type k_depth: int :param discount: the discount rate :type discount: float

app.tests.feature_test(query_json_file, limit, weighting_keys, discount, k_depth)[source]

The feature endpoint constructs descendant trees for a series of roots from a single query, but does not conduct time series analysis. It also collects additional observable features for use as controls in multiple regression.

Parameters:
  • query_json_file (str) – path to a JSON file containing the query to be queried
  • limit (int) – the maximum number of docs to munge
  • k_depth (int) – the maximum depth to evaluate k
  • discount (float) – the discount rate
  • weighting_keys (list) – the weighting key to use for knowledge calculation
app.tests.forecasting(forecast_type, relative_series=False)[source]
app.tests.get_query_munger(query_file, limit=None, cache=True)[source]

Construct a query munger for a given query, stored in a JSON file. :param query_file: the path to the query file :param limit: the maximum number of docs to query :return: a QueryMunger with this configuration

app.tests.query_test(query_json_file, limit, weighting_keys, k_depth, discount, write_graph=False)[source]

The query endpoint collects patents for a query, constructs a citation network, and conducts metric calculations breadth-wise.

Parameters:
  • query_json_file (str) – path to a JSON file containing the query to be queried
  • limit (int) – the maximum number of docs to munge
  • k_depth (int) – the maximum depth to evaluate k
  • discount (float) – the discount rate
  • write_graph (bool) – whether or not to write the network to a graph ml file
  • weighting_keys (list) – the weighting key to use for knowledge calculation
app.tests.regression()[source]
app.tests.root_test_multiple(query_json_file, limit, weighting_keys, k_depth, discount, bin_size=20, prefix='TIME-DATA')[source]

The root endpoint constructs a descendant citation tree for one or more patents and calculates metrics for the root.

Parameters:
  • query_json_file (str) – path to a JSON file containing the query to be queried
  • limit (int) – the maximum number of docs to munge
  • k_depth (int) – the maximum depth to evaluate k
  • discount (float) – the discount rate
  • bin_size (Integer) – bin size in weeks
  • weighting_keys (list) – the weighting key to use for knowledge calculation
  • prefix (str) – prefix for final storage file name
app.tests.root_test_single(patent, depth, weighting_keys, bin_size=20)[source]

The root endpoint constructs a descendant citation tree for one or more patents and calculates metrics for the root.

Parameters:
  • patent (str) – the patent number
  • depth (int) – the graph search depth
  • bin_size (int) – the bin size in weeks
  • weighting_keys (list) – the weighting key to use for knowledge calculation