added some utility codes

deeprank/learn/NeuralNet.py

@@ -975,7 +975,7 @@ def _plot_boxplot_class(self,figname):
                 for pts,t in zip(out,tar):
                     r = F.softmax(torch.FloatTensor(pts), dim=0).data.numpy()
                     data[t].append(r[1])
-                    confusion[t][r[1]>0.5] += 1
+                    confusion[t][bool(r[1]>0.5)] += 1
 
                 #print("  {:5s}: {:s}".format(l,str(confusion)))
 

deeprank/learn/rankingMetrics.py

@@ -16,8 +16,8 @@ def hitrate(rs):
 
     Example:
 
-    >>> r = [0,1,1]
-    >>> hit_rate(r,nr)
+    >>> rs = [0,1,1]
+    >>> hitrate(r)
 
 
     Attributes:
@@ -27,14 +27,34 @@ def hitrate(rs):
     Returns:
         hirate (array): [recall@1,recall@2,...]
     """
-    nr = np.max((1,np.sum(rs)))
+    nr = np.max((1, np.sum(rs)))
     return np.cumsum(rs) / nr
 
 
+def success(rs):
+    """Success for positions ≤ k.
+
+    Example:
+    >>> rs = [0, 0, 1, 0, 1, 0]
+    >>> success(rs)
+    [0, 0, 1, 1, 1, 1]
+
+    Args:
+        rs (array): binary relevance array
+
+    Returns:
+        success (array): [success@≤1, success@≤2,...]
+    """
+    success = np.cumsum(rs) > 0
+
+    return success.astype(np.int)
+
+
 def avprec(rs):
-    return [average_precision(rs[:i]) for i in range(1,len(rs))]
+    return [average_precision(rs[:i]) for i in range(1, len(rs))]
+
 
-def recall(rs,nr):
+def recall(rs, nr):
     """recall rate
     First element is rank 1, Relevance is binray
 
@@ -56,6 +76,7 @@ def recall(rs,nr):
 
     return np.sum(rs)/nr
 
+
 def mean_reciprocal_rank(rs):
     """Score is reciprocal of the rank of the first relevant item
 
@@ -272,4 +293,4 @@ def ndcg_at_k(r, k, method=0):
     dcg_max = dcg_at_k(sorted(r, reverse=True), k, method)
     if not dcg_max:
         return 0.
-    return dcg_at_k(r, k, method) / dcg_max
+    return dcg_at_k(r, k, method) / dcg_max

deeprank/utils/cal_hitrate_successrate.py

@@ -0,0 +1,169 @@
+import numpy as np
+import pandas as pd
+from deeprank.learn import rankingMetrics
+
+
+def evaluate(data):
+    '''
+    Calculate success rate and hit rate.
+
+    <INPUT>
+    data: a data frame.
+
+           label  caseID             modelID target        DR         HS
+           Test   1AVX  1AVX_ranair-it0_5286      0  0.503823   6.980802
+           Test   1AVX     1AVX_ti5-itw_354w      1  0.502845 -95.158100
+           Test   1AVX  1AVX_ranair-it0_6223      0  0.511688 -11.961460
+
+    <OUTPUT>
+    out_df: a data frame.
+    success: binary variable, indicating whether this case is a success when evaluating its top N models.
+
+        out_df :
+             label  caseID   success_DR   hitRate_DR   success_HS   hitRate_HS
+             train  1ZHI     1            0.1          0            0.01
+             train  1ZHI     1            0.2          1            0.3
+
+        where success =[0, 0, 1, 1, 1,...]: starting from rank 3 this case is a success
+
+    '''
+
+    out_df = pd.DataFrame()
+    labels = data.label.unique()  # ['train', 'test', 'valid']
+
+    for l in labels:
+        # l = 'train', 'test' or 'valid'
+
+        out_df_tmp = pd.DataFrame()
+
+        df = data.loc[data.label == l].copy()
+        methods = df.columns
+        methods = methods[4:]
+        df_grped = df.groupby('caseID')
+
+        for M in methods:
+            #            df_sorted = df_one_case.apply(pd.DataFrame.sort_values, by= M, ascending=True)
+
+            success = []
+            hitrate = []
+            caseIDs = []
+            for caseID, df_one_case in df_grped:
+                df_sorted = df_one_case.sort_values(by=M, ascending=True)
+                hitrate.extend(rankingMetrics.hitrate(
+                    df_sorted['target'].astype(np.int)))
+                success.extend(rankingMetrics.success(
+                    df_sorted['target'].astype(np.int)))
+                caseIDs.extend([caseID] * len(df_one_case))
+
+            # hitrate = df_sorted['target'].apply(rankingMetrics.hitrate) # df_sorted['target']: class IDs for each model
+            # success = hitrate.apply(rankingMetrics.success) # success =[0, 0, 1, 1, 1,...]: starting from rank 3 this case is a success
+
+            out_df_tmp['label'] = [l] * len(df)  # train, valid or test
+            out_df_tmp['caseID'] = caseIDs
+            out_df_tmp[f'success_{M}'] = success
+            out_df_tmp[f'hitRate_{M}'] = hitrate
+
+        out_df = pd.concat([out_df, out_df_tmp])
+
+    return out_df
+
+
+def ave_evaluate(data):
+    '''
+    Calculate the average of each column over all cases.
+
+    INPUT:
+    data =
+        label      caseID success_HS hitRate_HS success_DR hitRate_DR
+
+        train      1AVX   0.0      0.0      0.0      0.0
+        train      1AVX   1.0      1.0      1.0      1.0
+
+        train      2ACB   0.0      0.0      0.0      0.0
+        train      2ACB   1.0      1.0      1.0      1.0
+
+        test       7CEI   0.0      0.0      0.0      0.0
+        test       7CEI   1.0      1.0      1.0      1.0
+
+        test       5ACD   0.0      0.0      0.0      0.0
+        test       5ACD   1.0      1.0      1.0      1.0
+
+    OUTPUT:
+    new_data =
+        label      caseID success_HS hitRate_HS success_DR hitRate_DR
+
+        train      1AVX   0.0      0.0      0.0      0.0
+        train      1AVX   1.0      1.0      1.0      1.0
+
+        train      2ACB   0.0      0.0      0.0      0.0
+        train      2ACB   1.0      1.0      1.0      1.0
+
+        test       7CEI   0.0      0.0      0.0      0.0
+        test       7CEI   1.0      1.0      1.0      1.0
+
+        test       5ACD   0.0      0.0      0.0      0.0
+        test       5ACD   1.0      1.0      1.0      1.0
+
+    '''
+
+    new_data = pd.DataFrame()
+    for l, perf_per_case in data.groupby('label'):
+        # l = 'train', 'test' or 'valid'
+
+        # count the model number for each case
+        grouped = perf_per_case.groupby('caseID')
+        num_models = grouped.apply(len)
+        num_cases = len(grouped)
+
+        # --
+        top_N = min(num_models)
+        perf_ave = pd.DataFrame()
+        perf_ave['label'] = [l] * top_N
+
+        for col in perf_per_case.columns[2:]:
+            # perf_per_case.columns = ['label', 'caseID', 'success_HS', 'hitRate_HS', 'success_DR', 'hitRate_DR']
+            perf_ave[col] = np.zeros(top_N)
+
+            for _, perf_case in grouped:
+                perf_ave[col] = perf_ave[col][0:top_N] + \
+                    np.array(perf_case[col][0:top_N])
+
+            perf_ave[col] = perf_ave[col]/num_cases
+
+        new_data = pd.concat([new_data, perf_ave])
+
+    return new_data
+
+
+def add_rank(df):
+    '''
+    INPUT (a data frame):
+         label   success_DR  hitRate_DR  success_HS  hitRate_HS
+         Test          0.0    0.000000         0.0    0.000000
+         Test          0.0    0.000000         1.0    0.012821
+
+         Train         0.0    0.000000         1.0    0.012821
+         Train         0.0    0.000000         1.0    0.025641
+
+    OUTPUT:
+         label   success_DR  hitRate_DR  success_HS  hitRate_HS      rank
+         Test          0.0    0.000000         0.0    0.000000  0.000949
+         Test          0.0    0.000000         1.0    0.012821  0.001898
+
+         Train         0.0    0.000000         1.0    0.012821  0.002846
+         Train         0.0    0.000000         1.0    0.025641  0.003795
+
+    '''
+
+    # -- add the 'rank' column to df
+    rank = []
+    for _, df_per_label in df.groupby('label'):
+        num_mol = len(df_per_label)
+        rank_raw = np.array(range(num_mol)) + 1
+        rank.extend(rank_raw/num_mol)
+    df['rank'] = rank
+
+    df['label'] = pd.Categorical(df['label'], categories=[
+                                 'Train', 'Valid', 'Test'])
+
+    return df

deeprank/utils/get_h5subset.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+"""
+Extract first N groups of a hdf5 to a new hdf5 file.
+
+Usage: python {0} <hdf5 input file> <hdf5 output file> <number of groups to write>
+Example: python {0} ./001_1GPW.hdf5  ./001_1GPW_sub10.hdf5 10
+"""
+import sys
+import h5py
+
+USAGE = __doc__.format(__file__)
+
+
+def check_input(args):
+    if len(args) != 3:
+        sys.stderr.write(USAGE)
+        sys.exit(1)
+
+
+def get_h5subset(fin, fout, n):
+    """Extract first number of groups and write to a new hdf5 file.
+
+    Args:
+        fin (hdf5): input hdf5 file.
+        fout (hdf5): output hdf5 file.
+        n (int): first n groups to write.
+    """
+    n = int(n)
+    h5 = h5py.File(fin, "r")
+    h5out = h5py.File(fout, "w")
+    print(f"First {n} groups in {fin}:")
+    for i in list(h5)[0:n]:
+        print(i)
+        h5.copy(h5[i], h5out)
+
+    print()
+    print(f"Groups in {fout}:")
+    print(list(h5out))
+    h5.close()
+    h5out.close()
+    print()
+    print(f"{fout} generated.")
+
+
+if __name__ == "__main__":
+    check_input(sys.argv[1:])
+    fin, fout, n = sys.argv[1:]
+    get_h5subset(fin, fout, n)