 b/src/.ipynb_checkpoints/evals-checkpoint.py
+"""
+Author: Ritambhara Singh, Pinar Demetci, Rebecca Santorella
+February 2020
+"""
+import numpy as np
+import random, math, os, sys
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import normalize
+from sklearn.metrics import roc_auc_score, silhouette_samples
+from sklearn.decomposition import PCA
+def calc_frac_idx(x1_mat,x2_mat):
+    """
+    Returns fraction closer than true match for each sample (as an array)
+    """
+    fracs = []
+    x = []
+    nsamp = x1_mat.shape[0]
+    rank=0
+    for row_idx in range(nsamp):
+        euc_dist = np.sqrt(np.sum(np.square(np.subtract(x1_mat[row_idx,:], x2_mat)), axis=1))
+        true_nbr = euc_dist[row_idx]
+        sort_euc_dist = sorted(euc_dist)
+        rank =sort_euc_dist.index(true_nbr)
+        frac = float(rank)/(nsamp -1)
+        fracs.append(frac)
+        x.append(row_idx+1)
+    return fracs,x
+def calc_domainAveraged_FOSCTTM(x1_mat, x2_mat):
+    """
+    Outputs average FOSCTTM measure (averaged over both domains)
+    Get the fraction matched for all data points in both directions
+    Averages the fractions in both directions for each data point
+    """
+    fracs1,xs = calc_frac_idx(x1_mat, x2_mat)
+    fracs2,xs = calc_frac_idx(x2_mat, x1_mat)
+    fracs = []
+    for i in range(len(fracs1)):
+        fracs.append((fracs1[i]+fracs2[i])/2)
+    return fracs
+def calc_sil(x1_mat,x2_mat,x1_lab,x2_lab):
+    """
+    Returns silhouette score for datasets with cell clusters
+    """
+    sil = []
+    sil_d0 = []
+    sil_d3 = []
+    sil_d7 = []
+    sil_d11 = []
+    sil_npc = []
+    x = np.concatenate((x1_mat,x2_mat))
+    lab = np.concatenate((x1_lab,x2_lab))
+    sil_score = silhouette_samples(x,lab)
+    nsamp = x.shape[0]
+    for i in range(nsamp):
+        if(lab[i]==1):
+            sil_d0.append(sil_score[i])
+        elif(lab[i]==2):
+            sil_d3.append(sil_score[i])
+        elif(lab[i]==3):
+            sil_d7.append(sil_score[i])
+        elif(lab[i]==4):
+            sil_d11.append(sil_score[i])
+        elif(lab[i]==5):
+            sil_npc.append(sil_score[i])
+    avg = np.mean(sil_score)
+    d0 = sum(sil_d0)/len(sil_d0)
+    d3 = sum(sil_d3)/len(sil_d3)
+    d7 = sum(sil_d7)/len(sil_d7)
+    d11 = sum(sil_d11)/len(sil_d11)
+    npc = sum(sil_npc)/len(sil_npc)
+    return avg,d0,d3,d7,d11,npc
+def binarize_labels(label,x):
+    """
+    Helper function for calc_auc
+    """
+    bin_lab = np.array([1] * len(x))
+    idx = np.where(x == label)
+    bin_lab[idx] = 0
+    return bin_lab
+def calc_auc(x1_mat, x2_mat, x1_lab, x2_lab):
+    """
+    calculate avg. ROC AUC scores for transformed data when there are >=2 number of clusters.
+    """
+    nsamp = x1_mat.shape[0]
+    auc = []
+    auc_d0 = []
+    auc_d3 = []
+    auc_d7 = []
+    auc_d11 = []
+    auc_npc = []
+    for row_idx in range(nsamp):
+        euc_dist = np.sqrt(np.sum(np.square(np.subtract(x1_mat[row_idx,:], x2_mat)), axis=1))
+        y_scores = euc_dist
+        y_true = binarize_labels(x1_lab[row_idx],x2_lab)
+        auc_score = roc_auc_score(y_true, y_scores)
+        auc.append(auc_score)
+        if(x1_lab[row_idx]==0):
+            auc_d0.append(auc_score)
+        elif(x1_lab[row_idx]==1):
+            auc_d3.append(auc_score)
+        elif(x1_lab[row_idx]==2):
+            auc_d7.append(auc_score)
+        elif(x1_lab[row_idx]==3):
+            auc_d11.append(auc_score)
+        elif(x1_lab[row_idx]==4):
+            auc_npc.append(auc_score)
+    avg = sum(auc)/len(auc)
+    d0 = sum(auc_d0)/len(auc_d0)
+    d3 = sum(auc_d3)/len(auc_d3)
+    d7 = sum(auc_d7)/len(auc_d7)
+    d11 = sum(auc_d11)/len(auc_d11)
+    npc = sum(auc_npc)/len(auc_npc)
+    return avg,d0,d3,d7,d11,npc
+def transfer_accuracy(domain1, domain2, type1, type2, n):
+    """
+    Metric from UnionCom: "Label Transfer Accuracy"
+    """
+    knn = KNeighborsClassifier(n_neighbors=n)
+    knn.fit(domain2, type2)
+    type1_predict = knn.predict(domain1)
+    np.savetxt("type1_predict.txt", type1_predict)
+    count = 0
+    for label1, label2 in zip(type1_predict, type1):
+        if label1 == label2:
+            count += 1
+    return count / len(type1)