cleaned up the INTERSPEECH related codes.
yemaozi88
5 years ago
parent
a1379caced
commit
eb65543781
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import os
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import sys
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import configparser
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import pypyodbc
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import numpy as np
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from collections import Counter
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import cross_val_score
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from sklearn import preprocessing
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import accuracy_score
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repo_dir = 'C:\\Users\\Aki\\source\\repos\\accent_classification'
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curr_dir = repo_dir + '\\accent_classification'
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), curr_dir))
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import data_manipulation as mani
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import evaluation as eval
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import speaker_based_functions as sb_func
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## ======================= user define =======================
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sentence_num_max = 10
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config_file = curr_dir + '\\config.ini'
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output_dir = repo_dir + '\\output'
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# make train/test set: 1, load: 0
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make_train_test_set = 0
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# specify which experiment to be performed.
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# - 3: groninven vs oost_overijssel vs limburg
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# - 2: groningen vs limburg
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experiment_type = 2
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region_labels3 = ['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg']
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region_labels2 = ['Groningen_and_Drenthe', 'Limburg']
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## ======================= data preparation =======================
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## load variables from the ini file
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config = configparser.ConfigParser()
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config.sections()
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config.read(config_file)
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MDB_file = config['sentence_based']['fileMDB']
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## connect to the database
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pypyodbc.lowercase = False
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param = r"Driver={Microsoft Access Driver (*.mdb, *.accdb)};dbq=" + MDB_file + ";"
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conn = pypyodbc.connect(param)
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cursor = conn.cursor()
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## get data from Access database
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# data format
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# 0: filename
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# 1: pid
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# 2: region
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# 3: ID (unique word_id)
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# 4: sentence_id
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# 5: word_id
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# 6: word
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# 7: pronunciation
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SQL_string = """\
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{CALL dataset_with_cities}
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"""
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cursor.execute(SQL_string)
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rows = cursor.fetchall()
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data = np.array(rows)
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del SQL_string, rows
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## get the list of pronunciation variant (pronvarList) from Access database
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# pronvarList format
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# 0: ID (unique word_id)
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# 1: word
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# 2: pronvar
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SQL_string = """\
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{CALL pronunciation_variant}
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"""
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cursor.execute(SQL_string)
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rows = cursor.fetchall()
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pronvarList = np.array(rows)
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del SQL_string, rows
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conn.close()
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## make list of LabelBinarizer object per word for X (=pronunciation variant).
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LB_list = []
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unique_wordID_list = data[:, 3].astype(int)
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unique_wordID_max = max(unique_wordID_list)
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for unique_wordID in range(1, unique_wordID_max+1):
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pronvar = data[unique_wordID_list == unique_wordID, 7]
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LB = preprocessing.LabelBinarizer()
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LB.fit(np.unique(pronvar))
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LB_list.append(LB)
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## make LabelEncorder/LabelBinilizer objects for y (=region).
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LE_y3 = preprocessing.LabelEncoder()
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LE_y3.fit(region_labels3)
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LE_y2 = preprocessing.LabelEncoder()
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LE_y2.fit(region_labels2)
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LB_y3 = preprocessing.LabelBinarizer()
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LB_y3.fit(region_labels3)
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LB_y2 = preprocessing.LabelBinarizer()
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LB_y2.fit(region_labels2)
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del unique_wordID, unique_wordID_max, pronvar, LB
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## ======================= make train/eval/test set or load =======================
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## find the smallest group to balance the number of samples per group.
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pidlist3 = np.unique(data[:, (1, 2)], axis=0)
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pidlist3_counter = Counter(pidlist3[:, 1])
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sample_num_max = min(pidlist3_counter.values())
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del pidlist3_counter
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## make train/eval/test set or load them.
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if make_train_test_set==1:
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pidlist3_train = []
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pidlist3_eval = []
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pidlist3_test = []
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for region_num in range(0, len(region_labels3)):
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region_name = region_labels3[region_num]
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pidlist3_per_region_ = pidlist3[pidlist3[:, 1]==region_labels3[region_num], :]
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pidlist3_per_region, idx = mani.extractRandomSample(
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pidlist3_per_region_, sample_num_max)
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# split dataset into train, eval and test.
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[pidlist3_per_region_train, pidlist3_per_region_test] = train_test_split(
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pidlist3_per_region, test_size = 0.2, random_state = 0)
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[pidlist3_per_region_train, pidlist3_per_region_eval] = train_test_split(
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pidlist3_per_region_train, test_size = 0.1, random_state = 0)
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# append numpy arrays.
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if region_num == 0:
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pidlist3_train = pidlist3_per_region_train
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pidlist3_eval = pidlist3_per_region_eval
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pidlist3_test = pidlist3_per_region_test
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else:
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pidlist3_train = np.r_[pidlist3_train, pidlist3_per_region_train]
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pidlist3_eval = np.r_[pidlist3_eval, pidlist3_per_region_eval]
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pidlist3_test = np.r_[pidlist3_test, pidlist3_per_region_test]
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del region_num, region_name
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del pidlist3_per_region_, pidlist3_per_region, idx
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del pidlist3_per_region_train, pidlist3_per_region_eval, pidlist3_per_region_test
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np.save(output_dir + "\\pidlist3_train.npy", pidlist3_train)
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np.save(output_dir + "\\pidlist3_eval.npy", pidlist3_eval)
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np.save(output_dir + "\\pidlist3_test.npy", pidlist3_test)
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if experiment_type == 2:
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pidlist2_train_ = np.r_[pidlist3_train, pidlist3_eval]
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pidlist2_train = sb_func.groningen_vs_limburg(pidlist2_train_)
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pidlist2_test = sb_func.groningen_vs_limburg(pidlist3_test)
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np.save(output_dir + "\\pidlist2_train", pidlist2_train)
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np.save(output_dir + "\\pidlist2_test", pidlist2_test)
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del pidlist2_train_
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else:
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pidlist3_train = np.load(output_dir + "\\pidlist3_train.npy")
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pidlist3_eval = np.load(output_dir + "\\pidlist3_eval.npy")
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pidlist3_test = np.load(output_dir + "\\pidlist3_test.npy")
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if experiment_type == 2:
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pidlist2_train = np.load(output_dir + "\\pidlist2_train.npy")
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pidlist2_test = np.load(output_dir + "\\pidlist2_test.npy")
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## extract corresponding data using pid
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data3_train = sb_func.extractPid(pidlist3_train, data)
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data3_eval = sb_func.extractPid(pidlist3_eval, data)
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data3_test = sb_func.extractPid(pidlist3_test, data)
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if experiment_type == 2:
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data2 = np.array(data)
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data2_train = sb_func.extractPid(pidlist2_train, data2)
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data2_test = sb_func.extractPid(pidlist2_test, data2)
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## ======================= experiments =======================
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## specify the dataset
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# train vs eval
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#trainData = data3_train
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#testData = data3_eval
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#testPID = pidlist3_eval
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#LB = LB_y3
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#LE = LE_y3
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#region_labels = region_labels3
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# train+eval vs test
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if experiment_type == 3:
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trainData = np.r_[data3_train, data3_eval]
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testData = data3_test
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testPID = pidlist3_test
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LB = LB_y3
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LE = LE_y3
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region_labels = region_labels3
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elif experiment_type == 2:
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trainData = data2_train
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testData = data2_test
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testPID = pidlist2_test
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LB = LB_y2
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LE = LE_y2
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region_labels = region_labels2
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## check the number of utterance
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#data_all = np.r_[trainData, testData]
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#filenames = np.c_[data_all[:, 0], data_all[:, 2]]
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#filenames_unique = np.unique(filenames, axis=0)
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#Counter(filenames_unique[:, 1])
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## output filenames
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fileComparison = output_dir + "\\algorithm_comparison.csv"
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filePerformance = output_dir + "\\sentence-level.csv"
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fileConfusionMatrix = output_dir + "\\confusion_matrix.csv"
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## compare classification algorithms for the sentence-classifiers.
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#sb_func.compare_sentence_level_classifiers(trainData, LB_list, LE, fileComparison)
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## train sentence-level classifiers.
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model_list, score_list, confusion_matrix_list = sb_func.train_sentence_level_classifiers(
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trainData, LB_list, LE, filePerformance)
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## prediction over evaluation data per each sentence-level classifier.
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pred_per_sentence = sb_func.prediction_per_sentence(testData, model_list, LB_list, LE)
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## combine sentence-level classifiers
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pred_per_pid_majority = sb_func.prediction_per_pid_majority(testPID, pred_per_sentence)
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## confusion matrix
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confusionMatrix_majority = confusion_matrix(
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pred_per_pid_majority[:, 1], pred_per_pid_majority[:, 2], labels=region_labels)
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## output
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accuracy = accuracy_score(pred_per_pid_majority[:, 1], pred_per_pid_majority[:, 2], normalize=True, sample_weight=None)
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print('accuracy: {}%'.format(accuracy * 100))
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cm = confusionMatrix_majority
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print(cm)
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np.save(output_dir + "\\pred_per_pid2.npy", pred_per_pid_majority)
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np.save(output_dir + "\\confusion_matrix2.npy", cm)
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@ -1,326 +0,0 @@
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import os
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import sys
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import configparser
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import pypyodbc
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import numpy as np
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from collections import Counter
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import cross_val_score
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from sklearn import preprocessing
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import accuracy_score
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currDir = 'C:\\Users\\Aki\\source\\repos\\rug_VS\\dialect_identification\\dialect_identification'
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), currDir))
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import dataManipulation as mani
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import evaluation as eval
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import speaker_based_functions as sb_func
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#####################
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## USER DEFINE ##
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#####################
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sentenceNumMax = 10
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configFile = currDir + '\\config.ini'
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dirOut = currDir + '\\result'
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# make train/test set: 1, load: 0
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makeTrainTestSet = 0
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# convert 3 regions to 2 regions: 1, load: 0
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conv3to2region = 0
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# 3 regions: 0
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# saxon vs limburg: 1
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# groningen vs limburg: 2
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experiment_type = 2
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regionLabels = ['Groningen_and_Drenthe', 'Limburg', 'Oost_Overijsel-Gelderland']
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# a bit useless error handling.
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#assert (experiment_type in (0, 1, 2)), "experiment type should be 0, 1 or 2."
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if experiment_type == 1:
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regionLabels2 = ['Low_Saxon', 'Limburg']
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regionLabels2 = ['Groningen_and_Drenthe', 'Limburg']
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##########################
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## DATA PREPARATION ##
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##########################
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## load init file
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config = configparser.ConfigParser()
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config.sections()
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config.read(configFile)
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dirFeature = config['sentence_based']['dirFeature']
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fileMDB = config['sentence_based']['fileMDB']
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## database connection
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pypyodbc.lowercase = False
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param = r"Driver={Microsoft Access Driver (*.mdb, *.accdb)};dbq=" + fileMDB + ";"
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conn = pypyodbc.connect(param)
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cursor = conn.cursor()
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## get data from Access database
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# data format
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# 0: filename
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# 1: pid
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# 2: region
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# 3: ID (unique word_id)
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# 4: sentence_id
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# 5: word_id
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# 6: word
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# 7: pronunciation
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SQL_string = """\
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{CALL dataset_with_cities}
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"""
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cursor.execute(SQL_string)
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rows = cursor.fetchall()
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data = np.array(rows)
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#dataNumMax = data.shape[0]
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#uniqueWordIDmax = max(data[:, 3].astype(int))
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del SQL_string, rows
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## make list of LabelBinarizer object per word.
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# for X
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# get pronvarList from Access database
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# pronvarList format
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# 0: ID (unique word_id)
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# 1: word
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# 2: pronvar
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SQL_string = """\
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{CALL pronunciation_variant}
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"""
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cursor.execute(SQL_string)
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rows = cursor.fetchall()
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pronvarList = np.array(rows)
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del SQL_string, rows
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LBlist = []
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#uniqueWordIDlist = pronvarList[:, 0].astype(int)
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uniqueWordIDlist = data[:, 3].astype(int)
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uniqueWordIDmax = max(uniqueWordIDlist)
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for uniqueWordID in range(1, uniqueWordIDmax+1):
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pronvar = data[uniqueWordIDlist == uniqueWordID, 7]
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#pronvar = pronvarList[pronvarList[:, 0] == uniqueWordID, 2]
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LB = preprocessing.LabelBinarizer()
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LB.fit(np.unique(pronvar))
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LBlist.append(LB)
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# for y (=region)
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LE_y = preprocessing.LabelEncoder()
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LE_y.fit(regionLabels)
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LE_y2 = preprocessing.LabelEncoder()
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LE_y2.fit(regionLabels2)
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LB_y = preprocessing.LabelBinarizer()
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LB_y.fit(regionLabels)
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LB_y2 = preprocessing.LabelBinarizer()
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LB_y2.fit(regionLabels2)
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del uniqueWordID, uniqueWordIDmax, pronvar, LB
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#################
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## ITERATION ##
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#################
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#CM_majority = np.zeros((1, 9)).astype(int)
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#CM_weighted = np.zeros((1, 9)).astype(int)
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#for iter in range(0, 1):
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# print(iter)
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## make balanced dataset
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pidlist = np.unique(data[:, (1, 2)], axis=0)
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# count number of samples
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pidlistCounter = Counter(pidlist[:, 1])
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sampleNumMax = min(pidlistCounter.values())
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del pidlistCounter
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## make train/eval/test set or load
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if makeTrainTestSet==1:
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pidlist_train = []
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pidlist_eval = []
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pidlist_test = []
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for regionNum in range(0, len(regionLabels)):
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regionName = regionLabels[regionNum]
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pidlist_per_region_ = pidlist[pidlist[:, 1]==regionLabels[regionNum], :]
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pidlist_per_region, idx = mani.extractRandomSample(
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pidlist_per_region_, sampleNumMax)
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# split dataset into train, eval and test.
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[pidlist_per_region_train, pidlist_per_region_test] = train_test_split(
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pidlist_per_region, test_size = 0.2, random_state = 0)
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[pidlist_per_region_train, pidlist_per_region_eval] = train_test_split(
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pidlist_per_region_train, test_size = 0.1, random_state = 0)
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# append numpy arrays
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if regionNum == 0:
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pidlist_train = pidlist_per_region_train
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pidlist_eval = pidlist_per_region_eval
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pidlist_test = pidlist_per_region_test
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else:
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pidlist_train = np.r_[pidlist_train, pidlist_per_region_train]
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pidlist_eval = np.r_[pidlist_eval, pidlist_per_region_eval]
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pidlist_test = np.r_[pidlist_test, pidlist_per_region_test]
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del regionNum, regionName
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del pidlist_per_region_, pidlist_per_region, idx
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del pidlist_per_region_train, pidlist_per_region_eval, pidlist_per_region_test
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np.save(dirOut + "\\pidlist_train.npy", pidlist_train)
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np.save(dirOut + "\\pidlist_eval.npy", pidlist_eval)
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np.save(dirOut + "\\pidlist_test.npy", pidlist_test)
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else:
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pidlist_train = np.load(dirOut + "\\pidlist_train.npy")
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pidlist_eval = np.load(dirOut + "\\pidlist_eval.npy")
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pidlist_test = np.load(dirOut + "\\pidlist_test.npy")
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## make dataset for 2 regions or load
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if conv3to2region==1:
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pidlist2_train_ = np.r_[pidlist_train, pidlist_eval]
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if experiment_type == 1:
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pidlist2_train = sb_func.saxon_vs_limburg(pidlist2_train_)
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pidlist2_test = sb_func.saxon_vs_limburg(pidlist_test)
|
||||
np.save(dirOut + "\\pidlist2_saxon_vs_limburg_train", pidlist2_train)
|
||||
np.save(dirOut + "\\pidlist2_saxon_vs_limburg_test", pidlist2_test)
|
||||
|
||||
elif experiment_type == 2:
|
||||
pidlist2_train = sb_func.groningen_vs_limburg(pidlist2_train_)
|
||||
pidlist2_test = sb_func.groningen_vs_limburg(pidlist_test)
|
||||
np.save(dirOut + "\\pidlist2_groningen_vs_limburg_train", pidlist2_train)
|
||||
np.save(dirOut + "\\pidlist2_groningen_vs_limburg_test", pidlist2_test)
|
||||
|
||||
del pidlist2_train_
|
||||
else:
|
||||
if experiment_type == 1:
|
||||
pidlist2_train = np.load(dirOut + "\\pidlist2_saxon_vs_limburg_train.npy")
|
||||
pidlist2_test = np.load(dirOut + "\\pidlist2_saxon_vs_limburg_test.npy")
|
||||
|
||||
elif experiment_type == 2:
|
||||
pidlist2_train = np.load(dirOut + "\\pidlist2_groningen_vs_limburg_train.npy")
|
||||
pidlist2_test = np.load(dirOut + "\\pidlist2_groningen_vs_limburg_test.npy")
|
||||
|
||||
|
||||
## train/test data
|
||||
if experiment_type == 0:
|
||||
# Groningen vs Overijsel vs Limburg
|
||||
data_train = sb_func.extractPid(pidlist_train, data)
|
||||
data_eval = sb_func.extractPid(pidlist_eval, data)
|
||||
data_test = sb_func.extractPid(pidlist_test, data)
|
||||
|
||||
elif experiment_type == 1 or experiment_type == 2:
|
||||
data2 = np.array(data)
|
||||
|
||||
if experiment_type == 1:
|
||||
for row, row2 in zip(data, data2):
|
||||
if row[2] == regionLabels[0] or row[2] == regionLabels[2]:
|
||||
row2[2] = regionLabels2[0]
|
||||
|
||||
data2_train = sb_func.extractPid(pidlist2_train, data2)
|
||||
data2_test = sb_func.extractPid(pidlist2_test, data2)
|
||||
|
||||
|
||||
#####################################
|
||||
## EXPERIMENTS START FROM HERE ##
|
||||
#####################################
|
||||
|
||||
## actual training
|
||||
# train vs eval
|
||||
#trainData = data_train
|
||||
#testData = data_eval
|
||||
#testPID = pidlist_eval
|
||||
#LB = LB_y
|
||||
#LE = LE_y
|
||||
#regionLabels = regionLabels3
|
||||
|
||||
# train+eval vs test
|
||||
if experiment_type == 0:
|
||||
trainData = np.r_[data_train, data_eval]
|
||||
testData = data_test
|
||||
testPID = pidlist_test
|
||||
LB = LB_y
|
||||
LE = LE_y
|
||||
elif experiment_type == 1 or experiment_type == 2:
|
||||
# 2 region: saxon vs limburg/ groningen vs limburg
|
||||
trainData = data2_train
|
||||
testData = data2_test
|
||||
testPID = pidlist2_test
|
||||
LB = LB_y2
|
||||
LE = LE_y2
|
||||
regionLabels = regionLabels2
|
||||
|
||||
|
||||
# check the number of utterance
|
||||
allData = np.r_[trainData, testData]
|
||||
filenames = np.c_[allData[:, 0], allData[:, 2]]
|
||||
filenames_unique = np.unique(filenames, axis=0)
|
||||
Counter(filenames_unique[:, 1])
|
||||
|
||||
|
||||
fileComparison = dirOut + "\\algorithm_comparison.csv"
|
||||
filePerformance = dirOut + "\\sentence-level.csv"
|
||||
fileConfusionMatrix = dirOut + "\\confusion_matrix.csv"
|
||||
|
||||
## compare classification algorithms for the sentence-classifiers.
|
||||
#sb_func.compare_sentence_level_classifiers(trainData, LBlist, LE, fileComparison)
|
||||
|
||||
## train sentence-level classifiers.
|
||||
modelList, scoreList, confusionMatrixList = sb_func.train_sentence_level_classifiers(
|
||||
trainData, LBlist, LE, filePerformance)
|
||||
|
||||
## prediction over evaluation data per each sentence-level classifier.
|
||||
pred_per_sentence = sb_func.prediction_per_sentence(testData, modelList, LBlist, LE)
|
||||
|
||||
## combine sentence-level classifiers
|
||||
pred_per_pid_majority = sb_func.prediction_per_pid_majority(testPID, pred_per_sentence)
|
||||
|
||||
## majority vote (weighted)
|
||||
#weight = sb_func.calc_weight(confusionMatrixList)
|
||||
#pred_per_pid_weighted = sb_func.prediction_per_pid_weighted(testPID, pred_per_sentence, weight, LB, LE)
|
||||
|
||||
### confusion matrix
|
||||
if experiment_type == 0:
|
||||
confusionMatrix_majority = confusion_matrix(
|
||||
pred_per_pid_majority[:, 1], pred_per_pid_majority[:, 2], labels=['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg'])
|
||||
else:
|
||||
confusionMatrix_majority = confusion_matrix(
|
||||
pred_per_pid_majority[:, 1], pred_per_pid_majority[:, 2], labels=['Groningen_and_Drenthe', 'Limburg'])
|
||||
|
||||
#confusionMatrix_weighted = confusion_matrix(
|
||||
# pred_per_pid_weighted[:, 1], pred_per_pid_weighted[:, 2], labels=regionLabels)
|
||||
|
||||
|
||||
## output
|
||||
accuracy = accuracy_score(pred_per_pid_majority[:, 1], pred_per_pid_majority[:, 2], normalize=True, sample_weight=None)
|
||||
print('accuracy: {}%'.format(accuracy * 100))
|
||||
|
||||
cm = confusionMatrix_majority
|
||||
print(cm)
|
||||
|
||||
np.save(dirOut + "\\pred_per_pid.npy", pred_per_pid_majority)
|
||||
np.save(dirOut + "\\confusion_matrix.npy", cm)
|
||||
|
||||
#fout = open(fileConfusionMatrix, "w")
|
||||
#fout.write('< confusion matrix for majority vote in evaluation set >\n')
|
||||
#sb_func.outputConfusionMatrix33('fout', 'confusionMatrix_majority', regionLabels)
|
||||
#fout.write('< confusion matrix for weighted vote in evaluation set >\n')
|
||||
#sb_func.outputConfusionMatrix33('fout', 'confusionMatrix_weighted', regionLabels)
|
||||
#fout.write('\n')
|
||||
#fout.close()
|
||||
|
||||
|
||||
##### iteration finish #####
|
||||
conn.close()
|
||||
#np.savetxt(dirOut + '\\cm_majority.csv', CM_majority, delimiter=',')
|
||||
#np.savetxt(dirOut + '\\cm_weighted.csv', CM_weighted, delimiter=',')
|
||||
|
||||
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Reference in New Issue