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Merge pull request #135 from ranking-agent/use-more-values
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Use more values
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@uhbrar
uhbrar authored Jul 19, 2024
2 parents 17a6d85 + 2fd49a7 commit f7025b7
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Showing 3 changed files with 125 additions and 25 deletions.
2 changes: 1 addition & 1 deletion ranker/server.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
from ranker.util.omnicorp_get_node_pmids import get_node_pmids

# set the app version
APP_VERSION = '3.3.3'
APP_VERSION = '3.3.4'

APP = FastAPI(title='ARAGORN Ranker', version=APP_VERSION)

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62 changes: 51 additions & 11 deletions ranker/shared/ranker_obj.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,11 +112,11 @@ def score(self, answer, jaccard_like=False):
# And organizing nodes and edges into a more manageable form scoring
# There is some repeated work accross analyses so we calculate all r_graphs
# at once
r_gaphs = self.get_rgraph(answer)
r_graphs = self.get_rgraph(answer)

# For each analysis we have a unique r_graph to score
analysis_details = []
for i_analysis, r_graph in enumerate(r_gaphs):
for i_analysis, r_graph in enumerate(r_graphs):
# First we calculate the graph laplacian
# The probes are needed to make sure we don't remove anything
# that we actually wanted to use for scoring
Expand All @@ -132,7 +132,10 @@ def score(self, answer, jaccard_like=False):
# Once we have the graph laplacian we can find the effective resistance
# Between all of the probes
# The exp(-1 * .) here converts us back to normalized space
score = np.exp(-kirchhoff(laplacian, probe_inds))
try:
score = np.exp(-kirchhoff(laplacian, probe_inds))
except:
breakpoint()

# Fail safe to get rid of NaNs.
score = score if np.isfinite(score) and score >= 0 else -1
Expand Down Expand Up @@ -484,7 +487,8 @@ def get_edge_values(self, edge_id):
"publications": [],
"num_publications": 0,
"literature_coocurrence": None,
"p_value": None
"p_value": None,
"affinity": None
}

# Look through attributes and
Expand Down Expand Up @@ -523,10 +527,17 @@ def get_edge_values(self, edge_id):

usable_edge_attr["publications"] = pubs
usable_edge_attr["num_publications"] = len(pubs)


if attr_type_id == "biolink:evidence_count":
usable_edge_attr["num_publications"] = attribute.get("value", 0)

# P-Values
# first 4 probably never happen
if "p_value" in orig_attr_name or "p-value" in orig_attr_name or \
"p_value" in attr_type_id or "p-value" in attr_type_id:
"p_value" in attr_type_id or "p-value" in attr_type_id or \
"pValue" in orig_attr_name or \
"fisher_exact_p" in orig_attr_name or \
"gwas_pvalue" in orig_attr_name:

p_value = attribute.get("value", None)

Expand Down Expand Up @@ -569,6 +580,14 @@ def get_edge_values(self, edge_id):
# # Every other edge has an assumed publication of 1
# usable_edge_attr['num_publications'] += 1

# affinities
if orig_attr_name == "affinity":
usable_edge_attr["affinity"] = attribute.get("value", 0)

# confidence score
if orig_attr_name == "biolink:tmkp_confidence_score":
usable_edge_attr["confidence_score"] = attribute.get("value", 0)

# At this point we have all of the information extracted from the edge
# We have have looked through all attributes and filled up usable_edge_attr
# Now we can construct the edge values using these attributes and the base weight
Expand All @@ -582,13 +601,13 @@ def get_edge_values(self, edge_id):
property_w = get_source_sigmoid(
usable_edge_attr["p_value"],
edge_source,
"p-value",
"p_value",
self.source_transformation,
self.unknown_source_transformation
)
source_w = get_source_weight(
edge_source,
"p-value",
"p_value",
self.source_weights,
self.unknown_source_weight
)
Expand Down Expand Up @@ -624,7 +643,6 @@ def get_edge_values(self, edge_id):
}

if usable_edge_attr['literature_coocurrence'] is not None:

property_w = get_source_sigmoid(
usable_edge_attr['literature_coocurrence'],
edge_source,
Expand All @@ -639,17 +657,39 @@ def get_edge_values(self, edge_id):
self.source_weights,
self.unknown_source_weight
)

this_edge_vals[edge_source]["literature_coocurrence"] = {
"value": usable_edge_attr["literature_coocurrence"],
"property_weight": property_w,
"source_weight": source_w,
"weight": property_w * source_w
}

if usable_edge_attr["affinity"] is not None:

property_w = get_source_sigmoid(
usable_edge_attr['affinity'],
edge_source,
"affinity",
self.source_transformation,
self.unknown_source_transformation,
)

source_w = get_source_weight(
edge_source,
"affinity",
self.source_weights,
self.unknown_source_weight
)

this_edge_vals[edge_source]["affinity"] = {
"value": usable_edge_attr["affinity"],
"property_weight": property_w,
"source_weight": source_w,
"weight": property_w * source_w
}

# Cache it
self.edge_values[edge_id] = this_edge_vals

return this_edge_vals

def kirchhoff(L, probes):
Expand Down
86 changes: 73 additions & 13 deletions ranker/shared/sources.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,19 @@
"infores:omnicorp": {
"literature_co-occurrence": 1,
},
"infores:text-mining-provider-targeted": {
"publications": 0.5,
"confidence_score": 1
},
"infores:genetics-data-provider": {
"p_value": 1
},
"infores:icees-kg": {
"p_value": 1
},
"infores:gwas-catalog": {
"p_value": 1
}
},
"source_transformation": {
"infores:omnicorp": {
Expand All @@ -20,11 +33,50 @@
"midpoint": 0,
"rate": 0.00033
}
},
"infores:text-mining-provider-targeted": {
"publications": {
"lower": -1,
"upper": 1,
"midpoint": 0,
"rate": 4e-4
},
"confidence_score": {
"lower": -1,
"upper": 1,
"midpoint": 0,
"rate": 3
},
},
"infores:genetics-data-provider": {
"p_value": {
"lower": 0,
"upper": 1,
"midpoint": 0.05,
"rate": -150
}
},
"infores:icees-kg": {
"p_value": {
"lower": 0,
"upper": 1,
"midpoint": 0.05,
"rate": -150
}
},
"infores:gwas-catalog": {
"p_value": {
"lower": 0,
"upper": 1,
"midpoint": 0.05,
"rate": -150
}
}
},
"unknown_source_weight": {
"publications": 1,
"literature_co-occurrence": 1,
"affinity": 1,
"unknown_property" : 0
},
"unknown_source_transformation": {
Expand All @@ -40,6 +92,12 @@
"midpoint": 0,
"rate": 0.001373265360835
},
"affinity": {
"lower": -1,
"upper": 1,
"midpoint": 0,
"rate": 0.4
},
"unknown_property": {
"lower": 0,
"upper": 0,
Expand All @@ -52,7 +110,9 @@
"infores:omnicorp": 0,
"infores:drugcentral": 5e-2,
"infores:hetionet": 3e-2,
"infores:text-mining-provider-targeted": 5e-3
"infores:text-mining-provider-targeted": 5e-3,
"infores:icees-kg": 3e-2,
"infores:gwas-catalog": 3e-2
},
"omnicorp_relevence": 0.0025

Expand All @@ -78,7 +138,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -92,7 +152,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -106,7 +166,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -125,7 +185,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand Down Expand Up @@ -165,7 +225,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -179,7 +239,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -193,7 +253,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -212,7 +272,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand Down Expand Up @@ -252,7 +312,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -266,7 +326,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -280,7 +340,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand All @@ -299,7 +359,7 @@
"midpoint": 0,
"rate": .574213221
},
"p-value": {
"p_value": {
"lower": 1,
"upper": 0,
"midpoint": 0.055,
Expand Down

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