def evaluate(self, model, data, labels):
tt = TT("Evaluator")
entidxs = set(data[:, 0]).union(set(labels))
for n in range(data.shape[0]):
ranking = self.makerank(data[n], model, entidxs)
tt.progress(n, data.shape[0])
for metric in self.metrics:
metric([labels[n]], ranking)
results = {}
for metric in self.metrics:
results[metric.name] = metric()
tt.tock("computed")
return results
def evaluate(self, model, data, labels):
tt = TT("Evaluator")
entidxs = set(data[:, 0]).union(set(labels))
for n in range(data.shape[0]):
ranking = self.makerank(data[n], model, entidxs)
tt.progress(n, data.shape[0])
for metric in self.metrics:
metric([labels[n]], ranking)
results = {}
for metric in self.metrics:
results[metric.name] = metric()
tt.tock("computed")
return results
def evaluate(data, model, *metrics):
tt = TT("Evaluator")
datadf = pd.DataFrame(data)
datadf.columns = ["s", "r", "o"]
datadfgb = datadf.groupby(by=["s", "r"]).apply(lambda x: set(x["o"].unique())).reset_index()
datadf.describe()
entidxs = set(datadf["s"].unique()).union(set(datadf["o"].unique()))
results = {}
tt.tock("initialized").tick()
for n, row in datadfgb.iterrows():
ranking = makerank(row, model, entidxs)
tt.progress(n, datadfgb.shape[0])
for metric in metrics:
metric(row, ranking)
for metric in metrics:
results[metric.name] = metric()
tt.tock("computed")
return results
def evaluate(data, model, *metrics):
tt = TT("Evaluator")
datadf = pd.DataFrame(data)
datadf.columns = ["s", "r", "o"]
datadfgb = datadf.groupby(
by=["s", "r"]).apply(lambda x: set(x["o"].unique())).reset_index()
datadf.describe()
entidxs = set(datadf["s"].unique()).union(set(datadf["o"].unique()))
results = {}
tt.tock("initialized").tick()
for n, row in datadfgb.iterrows():
ranking = makerank(row, model, entidxs)
tt.progress(n, datadfgb.shape[0])
for metric in metrics:
metric(row, ranking)
for metric in metrics:
results[metric.name] = metric()
tt.tock("computed")
return results
