google lint on tdc/utils

tdc/utils/label.py

@@ -21,7 +21,7 @@ def convert_y_unit(y, from_, to_):
     if from_ == "nM":
         y = y
     elif from_ == "p":
-        y = (10 ** (-y) - 1e-10) / 1e-9
+        y = (10**(-y) - 1e-10) / 1e-9
 
     if to_ == "p":
         y = -np.log10(y * 1e-9 + 1e-10)
@@ -31,9 +31,12 @@ def convert_y_unit(y, from_, to_):
     return y
 
 
-def label_transform(
-    y, binary, threshold, convert_to_log, verbose=True, order="descending"
-):
+def label_transform(y,
+                    binary,
+                    threshold,
+                    convert_to_log,
+                    verbose=True,
+                    order="descending"):
     """label transformation helper function
 
     Args:
@@ -62,7 +65,8 @@ def label_transform(
         elif order == "ascending":
             y = np.array([1 if i else 0 for i in np.array(y) > threshold])
         else:
-            raise ValueError("Please select order from 'descending or ascending!")
+            raise ValueError(
+                "Please select order from 'descending or ascending!")
     else:
         if (len(np.unique(y)) > 2) and convert_to_log:
             if verbose:
@@ -144,16 +148,16 @@ def label_dist(y, name=None):
     median = np.median(y)
     mean = np.mean(y)
 
-    f, (ax_box, ax_hist) = plt.subplots(
-        2, sharex=True, gridspec_kw={"height_ratios": (0.15, 1)}
-    )
+    f, (ax_box,
+        ax_hist) = plt.subplots(2,
+                                sharex=True,
+                                gridspec_kw={"height_ratios": (0.15, 1)})
 
     if name is None:
         sns.boxplot(y, ax=ax_box).set_title("Label Distribution")
     else:
-        sns.boxplot(y, ax=ax_box).set_title(
-            "Label Distribution of " + str(name) + " Dataset"
-        )
+        sns.boxplot(y, ax=ax_box).set_title("Label Distribution of " +
+                                            str(name) + " Dataset")
     ax_box.axvline(median, color="b", linestyle="--")
     ax_box.axvline(mean, color="g", linestyle="--")
 
@@ -191,7 +195,8 @@ def NegSample(df, column_names, frac, two_types):
         pos_set = set([tuple([i[0], i[1]]) for i in pos])
         np.random.seed(1234)
         samples = np.random.choice(df_unique, size=(x, 2), replace=True)
-        neg_set = set([tuple([i[0], i[1]]) for i in samples if i[0] != i[1]]) - pos_set
+        neg_set = set([tuple([i[0], i[1]]) for i in samples if i[0] != i[1]
+                      ]) - pos_set
 
         while len(neg_set) < x:
             sample = np.random.choice(df_unique, 2, replace=False)
@@ -208,10 +213,13 @@ def NegSample(df, column_names, frac, two_types):
             neg_list_val.append([i[0], id2seq[i[0]], i[1], id2seq[i[1]], 0])
 
         df = df.append(
-            pd.DataFrame(neg_list_val).rename(
-                columns={0: id1, 1: x1, 2: id2, 3: x2, 4: "Y"}
-            )
-        ).reset_index(drop=True)
+            pd.DataFrame(neg_list_val).rename(columns={
+                0: id1,
+                1: x1,
+                2: id2,
+                3: x2,
+                4: "Y"
+            })).reset_index(drop=True)
         return df
     else:
         df_unique_id1 = np.unique(df[id1].values.reshape(-1))
@@ -224,16 +232,11 @@ def NegSample(df, column_names, frac, two_types):
         sample_id1 = np.random.choice(df_unique_id1, size=len(df), replace=True)
         sample_id2 = np.random.choice(df_unique_id2, size=len(df), replace=True)
 
-        neg_set = (
-            set(
-                [
-                    tuple([sample_id1[i], sample_id2[i]])
-                    for i in range(len(df))
-                    if sample_id1[i] != sample_id2[i]
-                ]
-            )
-            - pos_set
-        )
+        neg_set = (set([
+            tuple([sample_id1[i], sample_id2[i]])
+            for i in range(len(df))
+            if sample_id1[i] != sample_id2[i]
+        ]) - pos_set)
 
         while len(neg_set) < len(df):
             sample_id1 = np.random.choice(df_unique_id1, size=1, replace=True)
@@ -252,8 +255,11 @@ def NegSample(df, column_names, frac, two_types):
             neg_list_val.append([i[0], id2seq1[i[0]], i[1], id2seq2[i[1]], 0])
 
         df = df.append(
-            pd.DataFrame(neg_list_val).rename(
-                columns={0: id1, 1: x1, 2: id2, 3: x2, 4: "Y"}
-            )
-        ).reset_index(drop=True)
+            pd.DataFrame(neg_list_val).rename(columns={
+                0: id1,
+                1: x1,
+                2: id2,
+                3: x2,
+                4: "Y"
+            })).reset_index(drop=True)
         return df

tdc/utils/label_name_list.py

@@ -636,11 +636,9 @@
     "Tanguay_ZF_120hpf_YSE_up",
 ]
 
-
 QM7_targets = ["Y"]
 # QM7_targets = ["E_PBE0", "E_max_EINDO", "I_max_ZINDO", "HOMO_ZINDO", "LUMO_ZINDO", "E_1st_ZINDO", "IP_ZINDO", "EA_ZINDO", "HOMO_PBE0", "LUMO_PBE0", "HOMO_GW", "LUMO_GW", "alpha_PBE0", "alpha_SCS"]
 
-
 #### qm7b: 14 labels
 QM7b_targets = [
     "AE_PBE0",
@@ -683,7 +681,6 @@
     "f1-CAM",
 ]
 
-
 # QM9_targets = [
 #       "mu", "alpha", "homo", "lumo", "gap", "r2", "zpve", "cv", "u0", "u298",
 #       "h298", "g298"