def score(self, table):
prepared_table, __ = prepare_data_table(
table, score_columns=self.score_columns)
texp = Experiment(prepared_table)
score = self.classifier.score(texp, True)
texp["d_score"] = (score - self.mu) / self.nu
s_values, q_values = lookup_s_and_q_values_from_error_table(
texp["d_score"].values, self.error_stat.df)
texp["m_score"] = q_values
texp["s_value"] = s_values
logging.info("mean m_score = %e, std_dev m_score = %e" %
(np.mean(q_values), np.std(q_values, ddof=1)))
logging.info("mean s_value = %e, std_dev s_value = %e" %
(np.mean(s_values), np.std(s_values, ddof=1)))
texp.add_peak_group_rank()
df = table.join(texp[["d_score", "m_score", "peak_group_rank"]])
if CONFIG.get("compute.probabilities"):
df = self.add_probabilities(df, texp)
if CONFIG.get("target.compress_results"):
to_drop = [
n for n in df.columns
if n.startswith("var_") or n.startswith("main_")
]
df.drop(to_drop, axis=1, inplace=True)
return df
def score(self, table):
prepared_table, __ = prepare_data_table(table, score_columns=self.score_columns)
texp = Experiment(prepared_table)
score = self.classifier.score(texp, True)
texp["d_score"] = (score - self.mu) / self.nu
s_values, q_values = lookup_s_and_q_values_from_error_table(texp["d_score"].values,
self.error_stat.df)
texp["m_score"] = q_values
texp["s_value"] = s_values
logging.info("mean m_score = %e, std_dev m_score = %e" % (np.mean(q_values),
np.std(q_values, ddof=1)))
logging.info("mean s_value = %e, std_dev s_value = %e" % (np.mean(s_values),
np.std(s_values, ddof=1)))
texp.add_peak_group_rank()
df = table.join(texp[["d_score", "m_score", "peak_group_rank"]])
if CONFIG.get("compute.probabilities"):
df = self.add_probabilities(df, texp)
if CONFIG.get("target.compress_results"):
to_drop = [n for n in df.columns if n.startswith("var_") or n.startswith("main_")]
df.drop(to_drop, axis=1, inplace=True)
return df
def enrich(self, input_table, experiment):
s_values, q_values = lookup_s_and_q_values_from_error_table(experiment["d_score"],
self.df)
experiment["m_score"] = q_values
experiment["s_value"] = s_values
logging.info("mean m_score = %e, std_dev m_score = %e" % (np.mean(q_values),
np.std(q_values, ddof=1)))
logging.info("mean s_value = %e, std_dev s_value = %e" % (np.mean(s_values),
np.std(s_values, ddof=1)))
experiment.add_peak_group_rank()
scored_table = input_table.join(experiment[["d_score", "m_score", "peak_group_rank"]])
return scored_table
def enrich_table_with_results(self, table, experiment, df_raw_stat):
s_values, q_values = lookup_s_and_q_values_from_error_table(
experiment["d_score"], df_raw_stat)
experiment["m_score"] = q_values
experiment["s_value"] = s_values
logging.info("mean m_score = %e, std_dev m_score = %e" %
(np.mean(q_values), np.std(q_values, ddof=1)))
logging.info("mean s_value = %e, std_dev s_value = %e" %
(np.mean(s_values), np.std(s_values, ddof=1)))
experiment.add_peak_group_rank()
scored_table = table.join(
experiment[["d_score", "m_score", "peak_group_rank"]])
return scored_table
