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Try to coerce string input for subsample_n #34

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merged 2 commits into from
Aug 20, 2019
Merged

Try to coerce string input for subsample_n #34

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d7e18cb
all a coercable string as input to subsample_n
gwaybio Aug 20, 2019
8d07f70
closes #28
gwaybio Aug 20, 2019
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47 changes: 22 additions & 25 deletions pycytominer/aggregate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,11 +55,6 @@ def __init__(
0 < subsample_frac and 1 >= subsample_frac
), "subsample_frac must be between 0 and 1"

# Check that the user didn't specify both subset frac and
assert (
subsample_frac == 1 or subsample_n == "all"
), "Do not set both subsample_frac and subsample_n"

self.sql_file = sql_file
self.strata = strata
self.features = features
Expand All @@ -72,10 +67,18 @@ def __init__(
self.subset_data = "none"
self.subsampling_random_state = subsampling_random_state
self.is_aggregated = False
self.is_subset_computed = False

if self.subsample_n != "all":
try:
self.subsample_n = int(self.subsample_n)
except ValueError:
print("subsample n must be an integer or coercable")

# Connect to sqlite engine
self.engine = create_engine(self.sql_file)
self.conn = self.engine.connect()
self._check_subsampling()

if load_image_data:
self.load_image()
Expand All @@ -90,16 +93,22 @@ def _check_compartments(self, compartments):
elif isinstance(compartments, str):
assert compartments in valid_compartments, error_str

def _check_subsampling(self):
# Check that the user didn't specify both subset frac and
assert (
self.subsample_frac == 1 or self.subsample_n == "all"
), "Do not set both subsample_frac and subsample_n"

def set_output_file(self, output_file):
self.output_file = output_file

def set_subsample_frac(self, subsample_frac):
self.subsample_n = "all"
self.subsample_frac = subsample_frac
self._check_subsampling()

def set_subsample_n(self, subsample_n):
self.subsample_frac = 1
self.subsample_n = subsample_n
self._check_subsampling()

def set_subsample_random_state(self, random_state):
self.subsampling_random_state = random_state
Expand All @@ -109,9 +118,7 @@ def load_image(self):
Load image table from sqlite file
"""
# Extract image metadata
image_cols = (
"TableNumber, ImageNumber, {}".format(", ".join(self.strata))
)
image_cols = "TableNumber, ImageNumber, {}".format(", ".join(self.strata))
image_query = "select {} from image".format(image_cols)
self.image_df = pd.read_sql(sql=image_query, con=self.conn)

Expand All @@ -127,6 +134,7 @@ def count_cells(self, compartment="cells", count_subset=False):

if count_subset:
assert self.is_aggregated, "Make sure to aggregate_profiles() first!"
assert self.is_subset_computed, "Make sure to get_subsample() first!"
count_df = pd.crosstab(
self.subset_data.loc[:, self.strata[1]],
self.subset_data.loc[:, self.strata[0]],
Expand Down Expand Up @@ -166,7 +174,7 @@ def subsample_profiles(self, x, random_state="none"):
x, frac=self.subsample_frac, random_state=self.subsampling_random_state
)

def get_subsample(self, compartment="cells", subsample_frac=1, subsample_n="all"):
def get_subsample(self, compartment="cells"):
"""
Extract subsample from sqlite file

Expand All @@ -175,12 +183,6 @@ def get_subsample(self, compartment="cells", subsample_frac=1, subsample_n="all"
"""
self._check_compartments(compartment)

if subsample_frac < 1:
self.set_subsample_frac(subsample_frac)

if isinstance(subsample_n, int):
self.set_subsample_n(subsample_n)

query_cols = "TableNumber, ImageNumber, ObjectNumber"
query = "select {} from {}".format(query_cols, compartment)

Expand All @@ -194,6 +196,7 @@ def get_subsample(self, compartment="cells", subsample_frac=1, subsample_n="all"
.apply(lambda x: self.subsample_profiles(x))
.reset_index(drop=True)
)
self.is_subset_computed = True

def aggregate_compartment(self, compartment, compute_subsample=False):
"""
Expand Down Expand Up @@ -301,15 +304,9 @@ def aggregate(
population_df = population_df.groupby(strata)

if operation == "median":
population_df = (
population_df.median()
.reset_index()
)
population_df = population_df.median().reset_index()
else:
population_df = (
population_df.mean()
.reset_index()
)
population_df = population_df.mean().reset_index()

# Aggregated image number and object number do not make sense
for col in ["ImageNumber", "ObjectNumber"]:
Expand Down