717 lines
27 KiB
Python
717 lines
27 KiB
Python
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# coding: utf-8
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# In[1]:
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import pandas as pd
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from IPython.display import clear_output, Markdown, Math
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import ipywidgets as widgets
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import os
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import glob
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import json
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import numpy as np
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import itertools
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import sklearn.utils as sku
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from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MultiLabelBinarizer, LabelBinarizer
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from sklearn.cluster import KMeans
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import nltk
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from keras.models import load_model
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from sklearn.externals import joblib
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import pickle
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import operator
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from sklearn.pipeline import Pipeline
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import json
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import datetime
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nltk.download('punkt')
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nltk.download('averaged_perceptron_tagger')
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nltk.download('wordnet')
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from keras import losses
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# check whether the display function exists:
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try:
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display
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except NameError:
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print("no fancy display function found... using print instead")
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display = print
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# In[2]:
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import sys
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sys.path.append("..")
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import Tools.Emoji_Distance as edist
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import Tools.sklearn_doc2vec as skd2v
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def emoji2sent(emoji_arr, only_emoticons=True):
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return np.array([edist.emoji_to_sentiment_vector(e, only_emoticons=only_emoticons) for e in emoji_arr])
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def sent2emoji(sent_arr, custom_target_emojis=None, only_emoticons=True):
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return [edist.sentiment_vector_to_emoji(s, custom_target_emojis=custom_target_emojis, only_emoticons=only_emoticons) for s in sent_arr]
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# In[3]:
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SINGLE_LABEL = True
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# ----
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# ## classes and functions we are using later:
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# ----
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# * functions for selecting items from a set / list
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# In[4]:
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def latest(lst):
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return lst[-1] if len(lst) > 0 else 'X'
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def most_common(lst):
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# trying to find the most common used emoji in the given lst
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return max(set(lst), key=lst.count) if len(lst) > 0 else "X" # setting label to 'X' if there is an empty emoji list
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# * our emoji blacklist (skin and sex modifiers)
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# In[5]:
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# defining blacklist for modifier emojis:
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emoji_blacklist = set([
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chr(0x1F3FB),
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chr(0x1F3FC),
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chr(0x1F3FD),
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chr(0x1F3FE),
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chr(0x1F3FF),
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chr(0x2642),
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chr(0x2640)
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])
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# * lemmatization helper functions
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# In[6]:
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from nltk.stem.snowball import SnowballStemmer
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from nltk.stem import WordNetLemmatizer
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from nltk import pos_tag
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from nltk import word_tokenize
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from nltk.corpus import wordnet
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def get_wordnet_pos(treebank_tag):
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if treebank_tag.startswith('J'):
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return wordnet.ADJ
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elif treebank_tag.startswith('V'):
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return wordnet.VERB
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elif treebank_tag.startswith('N'):
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return wordnet.NOUN
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elif treebank_tag.startswith('R'):
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return wordnet.ADV
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else:
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return wordnet.NOUN
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# global stemmer and lemmatizer function
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stemmer = SnowballStemmer("english")
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def stem(s):
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stemmed_sent = []
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for word in s.split(" "):
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word_stemmed = stemmer.stem(word)
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stemmed_sent.append(word_stemmed)
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stemmed_sent = (" ").join(stemmed_sent)
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return stemmed_sent
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lemmatizer = WordNetLemmatizer()
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def lemm(s):
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lemmatized_sent = []
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sent_pos = pos_tag(word_tokenize(s))
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for word in sent_pos:
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wordnet_pos = get_wordnet_pos(word[1].lower())
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word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)
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lemmatized_sent.append(word_lemmatized)
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lemmatized_sent = (" ").join(lemmatized_sent)
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return lemmatized_sent
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def batch_stem(sentences):
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return [stem(s) for s in sentences]
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def batch_lemm(sentences):
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return [lemm(s) for s in sentences]
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# ### sample data manager
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# the sample data manager loads and preprocesses data
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# most common way to use:
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#
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#
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# * `sdm = sample_data_manager.generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None)`
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#
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# * Generates a sample_data_manager object and preprocess data in one step
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#
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# In[7]:
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class sample_data_manager(object):
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@staticmethod
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def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, read_progress_callback=None, stem_progress_callback=None, emoji_mean=False):
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"""
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generate, read and process train data in one step.
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@param path: folder containing json files to process
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@param only_emoticons: if True, only messages containing emoticons (provided by Tools.Emoji_Distance) are used
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@param apply_stemming: apply stemming and lemmatization on dataset
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@param n_top_emojis: only use messages containing one of <`n_top_emojis`>-top emojis. set to `-1` to prevent top emoji filtering
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@param file_range: range of file's indices to read (eg `range(3)` to read the first three files). If `None`: all files are read
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@param n_kmeans_cluster: generating multilabeled labels with kmeans with these number of clusters. Set to -1 to use the plain sentiment space as label
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@return: sample_data_manager object
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"""
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sdm = sample_data_manager(path)
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sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=read_progress_callback, emoji_mean=emoji_mean)
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if apply_stemming:
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sdm.apply_stemming_and_lemmatization(progress_callback=stem_progress_callback)
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sdm.generate_emoji_count_and_weights()
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if n_top_emojis > 0:
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sdm.filter_by_top_emojis(n_top=n_top_emojis)
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if n_kmeans_cluster > 0:
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sdm.generate_kmeans_binary_label(only_emoticons=only_emoticons, n_clusters=n_kmeans_cluster)
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return sdm
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def __init__(self, data_root_folder:str):
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"""
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constructor for manual initialization
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@param data_root_folder: folder containing json files to process
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"""
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self.data_root_folder = data_root_folder
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self.json_files = sorted(glob.glob(self.data_root_folder + "/*.json"))
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self.n_files = len(self.json_files)
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self.emojis = None
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self.plain_text = None
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self.labels = None
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self.emoji_count = None
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self.emoji_weights = None
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self.X = None
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self.y = None
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self.Xt = None
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self.yt = None
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self.top_emojis = None
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self.binary_labels = None
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self.use_binary_labels = False
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self.kmeans_cluster = None
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self.label_binarizer = None
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self.use_stemming = False
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self.use_lemmatization = False
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def read_files(self, file_index_range:list, only_emoticons=True, emoji_mean=False ,progress_callback=None):
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"""
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reading (multiple) files to one panda table.
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@param file_index_range: range of file's indices to read (eg `range(3)` to read the first three files)
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@param only_emoticons: if True, only messages containing emoticons (aka smileys) are used. This classification is derived from Tools.Emoji_Distance
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@param emoji_mean: if True, using mean of all emojis instead of the last one
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"""
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assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files
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n = len(file_index_range)
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for i in file_index_range:
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print("reading file: " + self.json_files[i] + "...")
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raw_data_i = pd.read_json(self.json_files[i], encoding="utf-8")
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emojis_i = raw_data_i['EMOJI']
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plain_text_i = raw_data_i['text']
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# replacing keywords. TODO: maybe these information can be extracted and used
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plain_text_i = plain_text_i.str.replace("(<EMOJI>|<USER>|<HASHTAG>)","").str.replace("[" + "".join(list(emoji_blacklist)) + "]","")
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# filter empty labels
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empty_labels = []
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for e in emojis_i:
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if len(e) < 1:
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empty_labels.append(True)
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else:
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empty_labels.append(False)
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empty_labels = np.array(empty_labels, dtype=np.bool_)
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plain_text_i = plain_text_i[np.invert(empty_labels)]
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emojis_i = emojis_i[np.invert(empty_labels)]
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print("ignored " + str(np.sum(empty_labels)) + " empty labels")
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if not emoji_mean:
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# so far filtering for the latest emoji. TODO: maybe there are also better approaches
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labels_i = emoji2sent([latest(e) for e in emojis_i], only_emoticons=only_emoticons )
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else:
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tmp = [np.nanmean(emoji2sent(e, only_emoticons=only_emoticons), axis=0, dtype=float) for e in emojis_i]
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c = 0
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for t in tmp:
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# only to find and debug wrong formatted data
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if str(type(t)) != "<class 'numpy.ndarray'>":
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print(t, type(t))
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print(emojis_i[c])
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print(emoji2sent(emojis_i[c], only_emoticons=only_emoticons))
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c += 1
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labels_i = np.array(tmp, dtype=float)
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# and filter out all samples we have no label for:
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wrong_labels = np.isnan(np.linalg.norm(labels_i, axis=1))
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labels_i = labels_i[np.invert(wrong_labels)]
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plain_text_i = plain_text_i[np.invert(wrong_labels)]
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emojis_i = emojis_i[np.invert(wrong_labels)]
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print("imported " + str(len(labels_i)) + " samples")
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if self.labels is None:
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self.labels = labels_i
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else:
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self.labels = np.append(self.labels, labels_i, axis=0)
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if self.emojis is None:
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self.emojis = emojis_i
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else:
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self.emojis = pd.concat([self.emojis,emojis_i],ignore_index=True)
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if self.plain_text is None:
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self.plain_text = plain_text_i
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else:
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self.plain_text = pd.concat([self.plain_text,plain_text_i],ignore_index=True)
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if progress_callback is not None:
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progress_callback((i+1)/n)
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def apply_stemming_and_lemmatization(self, progress_callback = None):
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"""
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apply stemming and lemmatization to plain text samples
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"""
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self.use_stemming = True
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self.use_lemmatization = True
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print("apply stemming and lemmatization...")
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stemmer = SnowballStemmer("english")
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n = self.plain_text.shape[0] * 2 # 2 for loops
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i = 0
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for key in self.plain_text.keys():
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stemmed_sent = []
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for word in self.plain_text[key].split(" "):
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word_stemmed = stemmer.stem(word)
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stemmed_sent.append(word_stemmed)
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stemmed_sent = (" ").join(stemmed_sent)
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self.plain_text[key] = stemmed_sent
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i += 1
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if progress_callback is not None and i % 1024 == 0:
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progress_callback(i / n)
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lemmatizer = WordNetLemmatizer()
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for key in self.plain_text.keys():
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lemmatized_sent = []
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sent_pos = pos_tag(word_tokenize(self.plain_text[key]))
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for word in sent_pos:
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wordnet_pos = get_wordnet_pos(word[1].lower())
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word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)
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lemmatized_sent.append(word_lemmatized)
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lemmatized_sent = (" ").join(lemmatized_sent)
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self.plain_text[key] = lemmatized_sent
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i += 1
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if progress_callback is not None and i % 1024 == 0:
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progress_callback(i / n)
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print("stemming and lemmatization done")
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def generate_emoji_count_and_weights(self):
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"""
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counting occurences of emojis
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"""
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self.emoji_count = {}
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for e_list in self.emojis:
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for e in set(e_list):
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if e not in self.emoji_count:
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self.emoji_count[e] = 0
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self.emoji_count[e] += 1
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emoji_sum = sum([self.emoji_count[e] for e in self.emoji_count])
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self.emoji_weights = {}
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for e in self.emoji_count:
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# tfidf for emojis
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self.emoji_weights[e] = np.log((emoji_sum / self.emoji_count[e]))
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weights_sum= sum([self.emoji_weights[x] for x in self.emoji_weights])
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# normalize:
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for e in self.emoji_weights:
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self.emoji_weights[e] = self.emoji_weights[e] / weights_sum
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self.emoji_weights['X'] = 0 # dummy values
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self.emoji_count['X'] = 0
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# dump count data to json:
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f = open("count_from_read_progress_" + str(datetime.datetime.now()) + ".json", 'w')
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f.write(json.dumps(self.emoji_count, ensure_ascii=False))
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f.close()
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def get_emoji_count(self):
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"""
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@return: descending list of tuples in form (<emoji as character>, <emoji count>)
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"""
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assert self.emoji_count is not None
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sorted_emoji_count = list(reversed(sorted(self.emoji_count.items(), key=operator.itemgetter(1))))
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#display(sorted_emoji_count)
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return sorted_emoji_count
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def filter_by_top_emojis(self,n_top = 20):
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"""
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filter out messages not containing one of the `n_top` emojis
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@param n_top: number of top emojis used for filtering
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"""
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assert self.labels is not None # ← messages are already read in
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self.top_emojis = [x[0] for x in self.get_emoji_count()[:n_top]]
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in_top = [edist.sentiment_vector_to_emoji(x) in self.top_emojis for x in self.labels]
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self.labels = self.labels[in_top]
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self.plain_text = self.plain_text[in_top]
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self.emojis = self.emojis[in_top]
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print("remaining samples after top emoji filtering: ", len(self.labels))
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def generate_kmeans_binary_label(self, only_emoticons=True, n_clusters=5):
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"""
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generate binary labels using kmeans.
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@param only_emoticons: set whether we're using the full emoji set or only emoticons
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@param n_clusters: number of cluster we're generating in emoji's sentiment space
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"""
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assert self.labels is not None
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array_sentiment_vectors = edist.list_sentiment_emoticon_vectors if only_emoticons else edist.list_sentiment_vectors
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array_sentiment_vectors = np.array(array_sentiment_vectors)
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list_emojis = edist.list_emoticon_emojis if only_emoticons else edist.list_emojis
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self.use_binary_labels = True
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print("clustering following emojis: " + "".join(list_emojis) + "...")
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self.kmeans_cluster = KMeans(n_clusters=n_clusters).fit(array_sentiment_vectors)
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print("clustering done")
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self.label_binarizer = LabelBinarizer()
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multiclass_labels = self.kmeans_cluster.predict(self.labels)
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# FIXME: we have to guarantee that in every dataset all classes occur.
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# otherwise batch fitting is not possible!
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# (or we have to precompute the mlb fitting process somewhere...)
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self.binary_labels = self.label_binarizer.fit_transform(multiclass_labels)
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def create_train_test_split(self, split = 0.1, random_state = 4222):
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assert self.plain_text is not None and self.labels is not None
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if self.X is not None:
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sys.stderr.write("WARNING: overwriting existing train/test split \n")
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labels = self.binary_labels if self.use_binary_labels else self.labels
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assert labels is not None
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self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state)
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# * the pipeline manager saves and stores sklearn pipelines. Keras models are handled differently, so the have to be named explicitly during save and load operations
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# In[8]:
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class pipeline_manager(object):
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@staticmethod
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def load_from_pipeline_file(pipeline_file:str):
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"""
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loading a json configuration file and using it's paramters to call 'load_pipeline_from_files'
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"""
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with open(pipeline_file, 'r') as f:
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d = json.load(f)
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keras_models = d['keras_models']
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all_models = d['all_models']
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return pipeline_manager.load_pipeline_from_files(pipeline_file.rsplit('.',1)[0], keras_models, all_models)
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@staticmethod
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def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []):
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"""
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load a pipeline from files. A pipeline should be represented by multiple model files in the form '<file_prefix>.<model_name>'
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@param file_prefix: basename of all files (without extension)
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@param keras_models: list of keras models (keras model files, only extension name). Leave this list empty if this is not a keras pipeline
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@param all_models: list of all models (including keras_models, only extension name).
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@return a pipeline manager object
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"""
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pm = pipeline_manager(keras_models=keras_models)
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pm.load(file_prefix, all_models)
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return pm
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@staticmethod
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def create_keras_pipeline_with_vectorizer(vectorizer, layers, sdm:sample_data_manager, loss=None, optimizer=None, fit_vectorizer=True):
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'''
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creates pipeline with vectorizer and keras classifier
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@param vectorizer: Vectorizer object. will be fitted with data provided by sdm
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@param layers: list of keras layers. One keras layer is a tuple in form: (<#neurons:int>, <activation_func:str>)
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@param sdm: sample data manager to get data for the vectorizer
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@param loss: set keras loss function. Depending whether sdm use multiclass labels `categorical_crossentropy` or `mean_squared_error` is used as default
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@param optimizer: set keras optimizer. Depending whether sdm use multiclass labels `sgd` or `adam` is used as default
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@return: a pipeline manager object
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'''
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from keras.models import Sequential
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from keras.layers import Dense
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if fit_vectorizer:
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if sdm.X is None:
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sdm.create_train_test_split()
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vec_train = vectorizer.fit_transform(sdm.X)
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vec_test = vectorizer.transform(sdm.Xt)
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# creating keras model:
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model=Sequential()
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keras_layers = []
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first_layer = True
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for layer in layers:
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if first_layer:
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size = None
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if "size" in dir(vectorizer):
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size = vectorizer.size
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else:
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size = vectorizer.transform([" "])[0]._shape[1]
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model.add(Dense(units=layer[0], activation=layer[1], input_dim=size))
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first_layer = False
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else:
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model.add(Dense(units=layer[0], activation=layer[1]))
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if sdm.use_binary_labels:
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loss_function = loss if loss is not None else 'categorical_crossentropy'
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optimizer_function = optimizer if optimizer is not None else 'sgd'
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model.compile(loss=loss_function,
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optimizer=optimizer_function,
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metrics=['accuracy'])
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else:
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loss_function = loss if loss is not None else 'mean_squared_error'
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optimizer_function = optimizer if optimizer is not None else 'adam'
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model.compile(loss=loss_function,
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optimizer=optimizer_function)
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pipeline = Pipeline([
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('vectorizer',vectorizer),
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('keras_model', model)
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])
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return pipeline_manager(pipeline=pipeline, keras_models=['keras_model'])
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@staticmethod
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def create_pipeline_with_classifier_and_vectorizer(vectorizer, classifier, sdm:sample_data_manager = None):
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'''
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creates pipeline with vectorizer and non-keras classifier
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@param vectorizer: Vectorizer object. will be fitted with data provided by sdm
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@param classifier: unfitted classifier object (should be compatible with all sklearn classifiers)
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@param sdm: sample data manager to get data for the vectorizer
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@return: a pipeline manager object
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'''
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if sdm is not None:
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if sdm.X is None:
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sdm.create_train_test_split()
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vec_train = vectorizer.fit_transform(sdm.X)
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vec_test = vectorizer.transform(sdm.Xt)
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pipeline = Pipeline([
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('vectorizer',vectorizer),
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('classifier', classifier)
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])
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return pipeline_manager(pipeline=pipeline, keras_models=[])
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def __init__(self, pipeline = None, keras_models = []):
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"""
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constructor
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@param pipeline: a sklearn pipeline
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@param keras_models: list of keras steps in pipeline. Neccessary because saving and loading from keras models differs from the scikit ones
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"""
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self.pipeline = pipeline
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self.additional_objects = {}
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self.keras_models = keras_models
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def save(self, prefix:str):
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"""
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saving the pipeline. It generates one file per model in the form: '<prefix>.<model_name>'
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@param prefix: file prefix for all models
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"""
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print(self.keras_models)
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# doing this like explained here: https://stackoverflow.com/a/43415459
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for step in self.pipeline.named_steps:
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if step in self.keras_models:
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self.pipeline.named_steps[step].model.save(prefix + "." + step)
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else:
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joblib.dump(self.pipeline.named_steps[step], prefix + "." + str(step))
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load_command = "pipeline_manager.load_pipeline_from_files( '"
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load_command += prefix + "', " + str(self.keras_models) + ", "
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load_command += str(list(self.pipeline.named_steps.keys())) + ")"
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with open(prefix + '.pipeline', 'w') as outfile:
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json.dump({'keras_models': self.keras_models, 'all_models': [step for step in self.pipeline.named_steps]}, outfile)
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import __main__ as main
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if not hasattr(main, '__file__'):
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display("saved pipeline. It can be loaded the following way:")
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display(Markdown("> ```\n"+load_command+"\n```")) # ← if we're in jupyter, print the fancy way :)
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else:
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print("saved pipeline. It can be loaded the following way:")
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print(load_command)
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def load(self, prefix:str, models = []):
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"""
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load a pipeline. A pipeline should be represented by multiple model files in the form '<prefix>.<model_name>'
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NOTE: keras model names (if there are some) have to be defined in self.keras_models first!
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@param prefix: the prefix for all model files
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@param models: model_names to load
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"""
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self.pipeline = None
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model_list = []
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for model in models:
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if model in self.keras_models:
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model_list.append((model, load_model(prefix + "." + model)))
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else:
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model_list.append((model, joblib.load(prefix+"." + model)))
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self.pipeline = Pipeline(model_list)
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def fit(self,X,y):
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"""fitting the pipeline"""
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self.pipeline.fit(X,y)
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def predict(self,X, use_stemming=False, use_lemmatization=False):
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"""predict"""
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if use_stemming:
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X = np.array(batch_stem(X))
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if use_lemmatization:
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X = np.array(batch_lemm(X))
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return self.pipeline.predict(X)
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# * the trainer class passes Data from the sample manager to the pipeline manager
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# In[9]:
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def to_dense_if_sparse(X):
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"""
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little hepler function to make data dense (if it is sparse).
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is used in trainer.fit function
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"""
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if "todense" in dir(X):
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return X.todense()
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return X
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class trainer(object):
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def __init__(self, sdm:sample_data_manager, pm:pipeline_manager):
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"""constructor"""
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self.sdm = sdm
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self.pm = pm
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def fit(self, max_size=1000000, disabled_fit_steps=['vectorizer'], keras_batch_fitting_layer=['keras_model'], batch_size=None, n_epochs=1, progress_callback=None):
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"""
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fitting data in the pipeline. Because we don't want to refit the vectorizer, the pipeline models containing the vectorizer have to be named explicitly
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@param max_size: don't train more examples than that number
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@param disabled_fit_steps: list of pipeline steps that we want to prevent to refit. Normally all vectorizer steps
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"""
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# TODO: make batch fitting available here (eg: continous waiting for data and fitting them)
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if self.sdm.X is None:
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self.sdm.create_train_test_split()
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disabled_fits = {}
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disabled_fit_transforms = {}
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disabled_keras_fits = {}
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named_steps = self.pm.pipeline.named_steps
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for s in disabled_fit_steps:
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# now it gets really dirty:
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# replace fit functions we don't want to call again (e.g. for vectorizers)
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disabled_fits[s] = named_steps[s].fit
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disabled_fit_transforms[s] = named_steps[s].fit_transform
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named_steps[s].fit = lambda self, X, y=None: self
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named_steps[s].fit_transform = named_steps[s].transform
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if batch_size is not None:
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for k in keras_batch_fitting_layer:
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# forcing batch fitting on keras
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disabled_keras_fits[k]=named_steps[k].fit
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named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(to_dense_if_sparse(X), y) # ← why has keras no sparse support on batch progressing!?!?!
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if batch_size is None:
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for e in range(n_epochs):
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print("epoch", e)
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self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])
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pred, yt = self.test()
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mean_squared_error = ((pred - yt)**2).mean(axis=0)
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print("#" + str(e) + ": validation loss: ", mean_squared_error, "scalar: ", np.mean(mean_squared_error))
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else:
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n = len(self.sdm.X) // batch_size
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for i in range(n_epochs):
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for j in range(n):
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self.pm.fit(X = np.array(self.sdm.X[j*batch_size:(j+1)*batch_size]), y = np.array(self.sdm.y[j*batch_size:(j+1)*batch_size]))
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if progress_callback is not None:
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progress_callback(j / n)
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pred, yt = self.test()
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mean_squared_error = ((pred - yt)**2).mean(axis=0)
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print("#" + str(j) + ": loss: ", mean_squared_error)
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# restore replaced fit functions:
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for s in disabled_fit_steps:
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named_steps[s].fit = disabled_fits[s]
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named_steps[s].fit_transform = disabled_fit_transforms[s]
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if batch_size is not None:
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for k in keras_batch_fitting_layer:
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named_steps[k].fit = disabled_keras_fits[k]
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def test(self):
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'''
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@return: prediction:list, teacher:list
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'''
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if self.sdm.X is None:
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self.sdm.create_train_test_split()
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return self.pm.predict(self.sdm.Xt, use_lemmatization=False, use_stemming=False), self.sdm.yt
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