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