code stringlengths 3 6.57k |
|---|
None (generates plots) |
df_U.drop(columns=["PU_label"]) |
df_U_copy.corr() |
corrmat.nlargest(num_feats, "synth_score") |
np.corrcoef(df_U_copy[cols].values.T) |
sns.set(style='ticks') |
plt.subplots(1, 1) |
self.save_plot(fname + ".png", fig, ax) |
get_feat_importances(self, plot_format="") |
plot_format (str) |
np.sum(feat_rank_rp, axis=1) |
df_U._get_numeric_data() |
df_U.drop(columns=["PU_label"]) |
pd.DataFrame(columns=["feature", "importance"]) |
df_feat.sort_values(by="importance", ascending=False) |
max() |
plt.subplots(figsize=(10, 4) |
sns.axes_style(style="ticks") |
sns.barplot(x="feature", y="importance", data=df_feat_sort) |
ax.get_xticklabels() |
self.save_plot(filename, fig, ax) |
_process_pu_data(data) |
data (DataFrame) |
X_P (array) |
X_U (array) |
data.query("PU_label == 1") |
data.query("PU_label == 0") |
np.asarray(df_P.drop(columns=["PU_label"]) |
_get_numeric_data() |
np.asarray(df_U.drop(columns=["PU_label"]) |
_get_numeric_data() |
save_plot(filename, fig, ax) |
filename (str) |
ax (objects) |
sns.set_style("ticks") |
fig.tight_layout() |
fig.savefig(filename) |
__init__(self, df_parent, pu_parent, df_child, pu_child, merge_on=() |
compounds (e.g. layered h-BN) |
predict (parent, child) |
synthesizable
(parent/child) |
df_parent (str) |
pu_parent (dict) |
df_child (str) |
pu_child (dict) |
merge_on (tuple) |
name(s) |
feats (tuple) |
merged_df (DataFrame) |
X (array) |
pd.read_json(df_parent) |
pd.read_json(df_child) |
df_parent.eval("PU_label == 0") |
df_child.eval("PU_label == 0") |
list(merge_on) |
df.drop(columns=["PU_label_p", "PU_label_c"], inplace=True, axis=1) |
np.array(df) |
do_kmeans(self, n_clusters=2, seed=42) |
on (parent, child) |
n_clusters (int) |
seed (int) |
kmeans_output (dict) |
each (parent, child) |
np.random.seed(seed) |
KMeans(n_clusters=n_clusters, random_state=seed) |
km.fit(self.X) |
do_gmixture(self, n_components=2, seed=42) |
n_components (int) |
seed (int) |
gmm_output (dict) |
of (parent, child) |
np.random.seed(seed) |
gmm.fit(self.X) |
gmm.predict(self.X) |
gmm.predict_proba(self.X) |
do_bgm(self, n_components=6, seed=42) |
n_components (int) |
seed (int) |
bgm_output (dict) |
np.random.seed(seed) |
bgm.fit(self.X) |
bgm.predict(self.X) |
bgm.predict_proba(self.X) |
path.join(test_constants.TEST_ROOT, 'nonexistent') |
path.join(test_constants.TEST_RESOURCES, 'BAD', 'BAD_EMPTY') |
GenerateUniverseTest(absltest.TestCase) |
testCanGenerateUniverse(self) |
generate_universe.BuildUniverse(_DEFAULT_ONTOLOGY_LOCATION) |
self.assertTrue(universe) |
testCatchInvalidModifiedOntology(self) |
self.assertRaises(Exception) |
generate_universe.BuildUniverse(_BAD_MODIFIED_ONTOLOGY) |
self.assertIn('no longer valid', str(context.exception) |
testModifiedTypesCatchesNonexistent(self) |
generate_universe.BuildUniverse(_NONEXISTENT_LOCATION) |
testModifiedTypesCatchesEmpty(self) |
self.assertRaises(Exception, generate_universe.BuildUniverse(_EMPTY_FOLDER) |
absltest.main() |
pytest.importorskip("sklearn") |
test_GaussianProcessRegressionSklearn_1() |
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