minor changes to output file

testing/save_test_results.py

@@ -0,0 +1,66 @@
+#import os
+#os.chdir('/Users/iriley/code/citation-analysis')
+import sys
+sys.path.append('/Users/iriley/code/citation-analysis')
+from classifier.linear_model import MultiClassPerceptron
+from sklearn.metrics import confusion_matrix as cm
+from utils.csv import read_csv_file
+from eval.metrics import f1_score
+import utils.constants as const
+import pandas as pd
+import numpy as np
+
+
+
+train_file_path = '/Users/iriley/code/citation-analysis/data/tsv/train.tsv'
+dev_file_path   = '/Users/iriley/code/citation-analysis/data/tsv/test.tsv'
+
+
+# Read the training dataset
+X_train_inst = read_csv_file(train_file_path, '\t')
+
+# set of labels from Training data
+labels = set([inst.true_label for inst in X_train_inst])
+
+# Read test data set
+X_dev_inst = read_csv_file(dev_file_path, '\t')
+
+# number of training iterations
+epochs = 50
+
+# create MultiClassPerceptron classifier object
+clf = MultiClassPerceptron(epochs=epochs, learning_rate=0.5, random_state=101)
+
+# train the model
+clf.fit(X_train=X_train_inst, labels=list(labels))
+
+# predict
+y_pred = clf.predict(X_dev_inst)
+y_scores = np.array(clf.get_class_scores(X_dev_inst))
+
+y_true = [inst.true_label for inst in X_dev_inst]
+
+labeldict = {'background': 0, 'method': 1, 'result': 2}
+y_pred = np.array([labeldict[x] for x in y_pred])
+y_true = np.array([labeldict[x] for x in y_true])
+
+conmat = cm(y_true, y_pred)
+
+df = pd.DataFrame()
+df['pred'] = y_pred
+df['true'] = y_true
+df['correct'] = y_pred==y_true
+df['score0'] = np.round(y_scores[:,0],3)
+df['score1'] = np.round(y_scores[:,1],3)
+df['score2'] = np.round(y_scores[:,2],3)
+
+df.to_csv('/Users/iriley/code/machine_learning/lab2020/y_pred_model1.csv', index=False)
+
+## Model Evaluation
+#f1_score_micro = f1_score(y_true, y_pred, labels, const.AVG_MICRO)
+#f1_score_macro = f1_score(y_true, y_pred, labels, const.AVG_MACRO)
+#f1_score_none  = f1_score(y_true, y_pred, labels, None)
+
+## Print F1 Score
+#for result in f1_score_micro + f1_score_macro + f1_score_none:
+#    result.print_result()