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Merge branch 'master' of ssh://gogs@the-cake-is-a-lie.net:20022/jonas/NLP-LAB.git

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Carsten 2018-06-26 21:23:15 +02:00
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130
Project/naive_approach/naiveApproachTest.ipynb Normal file
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@ -0,0 +1,130 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import naive_approach"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"top_emojis = ['😂',\n",
" '😭',\n",
" '😍',\n",
" '😩',\n",
" '😊',\n",
" '😘',\n",
" '🙏',\n",
" '🙌',\n",
" '😉',\n",
" '😁',\n",
" '😅',\n",
" '😎',\n",
" '😢',\n",
" '😒',\n",
" '😏',\n",
" '😌',\n",
" '😔',\n",
" '😋',\n",
" '😀',\n",
" '😤']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"lookup = naive_approach.prepareData(emojis_to_consider=top_emojis)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"sentence=\"I am very happy today\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "357",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-a7b8b0832a7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnaive_approach\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtop_emojis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(sentence, lookup, emojis_to_consider, criteria, description_key, lang, n, t)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 357"
]
}
],
"source": [
"pred = naive_approach.predict(sentence, lookup, emojis_to_consider=top_emojis, n=3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['🎁', '🙋', '\\U0001f91f']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

70
Project/naive_approach/naive_approach.py
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@ -37,7 +37,7 @@ def stemming(messages):
# * compare words to emoji descriptions # * compare words to emoji descriptions
def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng'): def evaluate_sentence(sentence, table, description_key = 'description', lang = 'eng', emojis_to_consider="all"):
tokenized_sentence = word_tokenize(sentence) tokenized_sentence = word_tokenize(sentence)
n = len(tokenized_sentence) n = len(tokenized_sentence)
@ -77,10 +77,7 @@ def evaluate_sentence(sentence, table, description_key = 'description', lang = '
# load and preprocess data # load and preprocess data
# emojis_to_consider can be either a list or "all" # emojis_to_consider can be either a list or "all"
def prepareData(stemming=False, emojis_to_consider="all"): def prepareData(stemming=False):
table.head()
if(stemming): if(stemming):
table['description'] = stemming(table['description']) table['description'] = stemming(table['description'])
@ -88,9 +85,8 @@ def prepareData(stemming=False, emojis_to_consider="all"):
lookup = {} lookup = {}
emoji_set = [] emoji_set = []
for index, row in table.iterrows(): for index, row in table.iterrows():
if(emojis_to_consider=="all" or (type(emojis_to_consider)==list and row['character'] in emojis_to_consider)): lookup[index] = row['character']
lookup[index] = row['character'] emoji_set.append(row['character'])
emoji_set.append(row['character'])
emoji_set = set(emoji_set) emoji_set = set(emoji_set)
@ -99,30 +95,44 @@ def prepareData(stemming=False, emojis_to_consider="all"):
# make a prediction for an input sentence # make a prediction for an input sentence
def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", description_key='description', lang = 'eng', n=10, t=0.9): def predict(sentence, lookup, emojis_to_consider="all", criteria="threshold", description_key='description', lang = 'eng', n=10, t=0.9):
result = evaluate_sentence(sentence, table, description_key, lang) result = evaluate_sentence(sentence, table, description_key, lang, emojis_to_consider=emojis_to_consider)
if(criteria=="summed"): try:
indexes = np.argsort([-np.sum(x) for x in result])[0:n] if(criteria=="summed"):
elif (criteria=="max_val"): resultValues = [-np.sum(x) for x in result]
indexes = np.argsort([-np.max(x) for x in result])[0:n] elif (criteria=="max_val"):
elif(criteria=="avg"): resultValues = [-np.max(x) for x in result]
indexes = np.argsort([-np.mean(x) for x in result])[0:n] elif(criteria=="avg"):
else: resultValues = [-np.mean(x) for x in result]
indexes= np.argsort([-len(np.where(x>t)[0]) / (x.shape[0] * x.shape[1]) for x in result])[0:n] else:
resultValues = [-len(np.where(x>t)[0]) / (x.shape[0] * x.shape[1]) for x in result]
indexes = np.argsort(resultValues)
results = np.sort(resultValues)
if (emojis_to_consider != "all" and type(emojis_to_consider) == list):
indexes2 = []
results2 = []
for i in range(len(indexes)):
if lookup[indexes[i]] in emojis_to_consider:
indexes2.append(indexes[i])
results2.append(results[i])
indexes = indexes2
results = results2
indexes = indexes[0:n]
results = results[0:n]
# build a result table
table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ]
table_frame = pd.DataFrame(table_array, columns=[criteria, 'description'])
#display(table_frame)
return list(table_frame[criteria]), results
if(emojis_to_consider!="all"): except ZeroDivisionError as err:
for i in indexes: print("There seems to be a problem with the input format. Please enter a nonempty string")
if (i not in lookup):
indexes = np.delete(indexes, [i])
# build a result table
table_array = [[lookup[indexes[i]], str(table.iloc[indexes[i]][description_key])] for i in range(n) ]
table_frame = pd.DataFrame(table_array, columns=[criteria, 'description'])
#display(table_frame)
return list(table_frame[criteria])
#predict("I like to travel by train", description_key='description' , lang='eng') #predict("I like to travel by train", description_key='description' , lang='eng')

73
Project/simple_approach/Continous_Learner.ipynb
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@ -144,7 +144,7 @@
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "5ac970d7d7cf4849b4f5adfb80a820c0", "model_id": "4fd5552e6a024dcaa0f35a594c77ae99",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
@ -172,7 +172,7 @@
" ],\n", " ],\n",
" [\n", " [\n",
" (widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\"),\n", " (widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\"),\n",
" (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=10), \"n_top_emojis\")\n", " (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\")\n",
" ],\n", " ],\n",
" [\n", " [\n",
" (widgets.Button(disabled=True),\"load_data\")\n", " (widgets.Button(disabled=True),\"load_data\")\n",
@ -235,6 +235,18 @@
" ]\n", " ]\n",
" ], \n", " ], \n",
" \"train\" )\n", " \"train\" )\n",
"create_area(\"playground 😎\",\n",
" [\n",
" [\n",
" (widgets.Text(),\"test_input\"),\n",
" (widgets.HTML(),\"prediction\")\n",
" ],\n",
" [\n",
" (widgets.Checkbox(),\"show_sorted_list\")\n",
" ]\n",
" ],\n",
" \"playground\")\n",
"\n",
"tab_manager" "tab_manager"
] ]
}, },
@ -360,11 +372,11 @@
"source": [ "source": [
"class progress_indicator(object):\n", "class progress_indicator(object):\n",
" \n", " \n",
" def __init__(self, n, description=\"progress\"):\n", " def __init__(self, description=\"progress\"):\n",
" self.w = widgets.IntProgress(value=0, min=0,max=n, description = description)\n", " self.w = widgets.FloatProgress(value=0, min=0,max=1, description = description)\n",
" display(self.w)\n", " display(self.w)\n",
" def update(self, dn=1):\n", " def update(self, val):\n",
" self.w.value += dn\n", " self.w.value = val\n",
" " " "
] ]
}, },
@ -416,13 +428,15 @@
" r = shown_widgets[\"file_range\"].value\n", " r = shown_widgets[\"file_range\"].value\n",
" r = (r[0], r[1] + 1) # range has to be exclusive according to the last element!\n", " r = (r[0], r[1] + 1) # range has to be exclusive according to the last element!\n",
" \n", " \n",
" p = progress_indicator(r[1] - r[0], \"reading progress\")\n", " p_r = progress_indicator(\"reading progress\")\n",
" p_s = progress_indicator(\"stemming progress\")\n",
" \n", " \n",
" sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n", " sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n",
" n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n", " n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n",
" file_range=range(r[0], r[1]),\n", " file_range=range(r[0], r[1]),\n",
" n_kmeans_cluster=shown_widgets[\"k_means_cluster\"].value,\n", " n_kmeans_cluster=shown_widgets[\"k_means_cluster\"].value,\n",
" progress_callback=p.update)\n", " read_progress_callback=p_r.update,\n",
" stem_progress_callback=p_s.update)\n",
" shown_widgets[\"batch_size\"].max = len(sdm.labels)\n", " shown_widgets[\"batch_size\"].max = len(sdm.labels)\n",
" \n", " \n",
" \n", " \n",
@ -461,11 +475,10 @@
" \n", " \n",
" print(\"update train test split:\")\n", " print(\"update train test split:\")\n",
" sdm.create_train_test_split(split=val_split)\n", " sdm.create_train_test_split(split=val_split)\n",
" batch_n = len(sdm.X) // batch_size\n",
" \n", " \n",
" print(\"fit\")\n", " print(\"fit\")\n",
" \n", " \n",
" p = progress_indicator(batch_n)\n", " p = progress_indicator()\n",
" \n", " \n",
" tr = stl.trainer(sdm=sdm, pm=pm)\n", " tr = stl.trainer(sdm=sdm, pm=pm)\n",
" tr.fit(progress_callback=p.update, batch_size=batch_size, n_epochs=n_epochs)\n", " tr.fit(progress_callback=p.update, batch_size=batch_size, n_epochs=n_epochs)\n",
@ -601,6 +614,46 @@
"\n", "\n",
"\n" "\n"
] ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## testing area"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def test_input(b):\n",
" global sdm\n",
" global pm\n",
" global tr\n",
" with out_areas[\"playground\"]:\n",
" clear_output()\n",
" mp(\"----\")\n",
" if pm is None:\n",
" sys.stderr.write(\"ERROR: load or create classifier first\")\n",
" return\n",
" X = shown_widgets[\"test_input\"].value\n",
" pred = pm.predict([X])\n",
" shown_widgets[\"prediction\"].value = \"<h1> \" + str(stl.sent2emoji(pred)[0]) + \"</h1>\"\n",
" if shown_widgets[\"show_sorted_list\"].value:\n",
" mp(\"## \" + \"\".join(stl.edist.sentiment_vector_to_emoji(pred, only_emoticons=True, n_results=100)))\n",
"\n",
"#link\n",
"shown_widgets[\"test_input\"].observe(test_input)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {

134
Project/simple_approach/simple_twitter_learning.py
View File

@ -28,6 +28,12 @@ nltk.download('punkt')
nltk.download('averaged_perceptron_tagger') nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet') nltk.download('wordnet')
# check whether the display function exists:
try:
display
except NameError:
print("no fancy display function found... using print instead")
display = print
# In[2]: # In[2]:
@ -108,6 +114,38 @@ def get_wordnet_pos(treebank_tag):
return wordnet.NOUN return wordnet.NOUN
# global stemmer and lemmatizer function
stemmer = SnowballStemmer("english")
def stem(s):
stemmed_sent = []
for word in s.split(" "):
word_stemmed = stemmer.stem(word)
stemmed_sent.append(word_stemmed)
stemmed_sent = (" ").join(stemmed_sent)
return stemmed_sent
lemmatizer = WordNetLemmatizer()
def lemm(s):
lemmatized_sent = []
sent_pos = pos_tag(word_tokenize(s))
for word in sent_pos:
wordnet_pos = get_wordnet_pos(word[1].lower())
word_lemmatized = lemmatizer.lemmatize(word[0], pos=wordnet_pos)
lemmatized_sent.append(word_lemmatized)
lemmatized_sent = (" ").join(lemmatized_sent)
return lemmatized_sent
def batch_stem(sentences):
return [stem(s) for s in sentences]
def batch_lemm(sentences):
return [lemm(s) for s in sentences]
# ### sample data manager # ### sample data manager
# the sample data manager loads and preprocesses data # the sample data manager loads and preprocesses data
# most common way to use: # most common way to use:
@ -123,7 +161,7 @@ def get_wordnet_pos(treebank_tag):
class sample_data_manager(object): class sample_data_manager(object):
@staticmethod @staticmethod
def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, progress_callback=None): 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):
""" """
generate, read and process train data in one step. generate, read and process train data in one step.
@ -137,9 +175,9 @@ class sample_data_manager(object):
@return: sample_data_manager object @return: sample_data_manager object
""" """
sdm = sample_data_manager(path) sdm = sample_data_manager(path)
sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=progress_callback) 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)
if apply_stemming: if apply_stemming:
sdm.apply_stemming_and_lemmatization() sdm.apply_stemming_and_lemmatization(progress_callback=stem_progress_callback)
sdm.generate_emoji_count_and_weights() sdm.generate_emoji_count_and_weights()
@ -161,7 +199,6 @@ class sample_data_manager(object):
self.data_root_folder = data_root_folder self.data_root_folder = data_root_folder
self.json_files = sorted(glob.glob(self.data_root_folder + "/*.json")) self.json_files = sorted(glob.glob(self.data_root_folder + "/*.json"))
self.n_files = len(self.json_files) self.n_files = len(self.json_files)
self.raw_data = None
self.emojis = None self.emojis = None
self.plain_text = None self.plain_text = None
self.labels = None self.labels = None
@ -176,46 +213,70 @@ class sample_data_manager(object):
self.use_binary_labels = False self.use_binary_labels = False
self.kmeans_cluster = None self.kmeans_cluster = None
self.label_binarizer = None self.label_binarizer = None
self.use_stemming = False
self.use_lemmatization = False
def read_files(self, file_index_range:list, only_emoticons=True, progress_callback=None): def read_files(self, file_index_range:list, only_emoticons=True, emoji_mean=False ,progress_callback=None):
""" """
reading (multiple) files to one panda table. reading (multiple) files to one panda table.
@param file_index_range: range of file's indices to read (eg `range(3)` to read the first three files) @param file_index_range: range of file's indices to read (eg `range(3)` to read the first three files)
@param only_emoticons: if True, only messages containing emoticons (aka smileys) are used. This classification is derived from Tools.Emoji_Distance @param only_emoticons: if True, only messages containing emoticons (aka smileys) are used. This classification is derived from Tools.Emoji_Distance
@param emoji_mean: if True, using mean of all emojis instead of the last one
""" """
assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files assert np.min(file_index_range) >= 0 and np.max(file_index_range) < self.n_files
n = len(file_index_range)
for i in file_index_range: for i in file_index_range:
print("reading file: " + self.json_files[i] + "...") print("reading file: " + self.json_files[i] + "...")
if self.raw_data is None: raw_data_i = pd.read_json(self.json_files[i], encoding="utf-8")
self.raw_data = pd.read_json(self.json_files[i], encoding="utf-8") emojis_i = raw_data_i['EMOJI']
else: plain_text_i = raw_data_i['text']
self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding="utf-8"))
if progress_callback is not None:
progress_callback()
self.emojis = self.raw_data['EMOJI']
self.plain_text = self.raw_data['text']
# replacing keywords. TODO: maybe these information can be extracted and used
self.plain_text = self.plain_text.str.replace("(<EMOJI>|<USER>|<HASHTAG>)","").str.replace("[" + "".join(list(emoji_blacklist)) + "]","")
# so far filtering for the latest emoji. TODO: maybe there are also better approaches
self.labels = emoji2sent([latest(e) for e in self.emojis], only_emoticons=only_emoticons )
# and filter out all samples we have no label for:
wrong_labels = np.isnan(np.linalg.norm(self.labels, axis=1))
self.labels = self.labels[np.invert(wrong_labels)] # replacing keywords. TODO: maybe these information can be extracted and used
self.plain_text = self.plain_text[np.invert(wrong_labels)] plain_text_i = plain_text_i.str.replace("(<EMOJI>|<USER>|<HASHTAG>)","").str.replace("[" + "".join(list(emoji_blacklist)) + "]","")
self.emojis = self.emojis[np.invert(wrong_labels)]
if not emoji_mean:
# so far filtering for the latest emoji. TODO: maybe there are also better approaches
labels_i = emoji2sent([latest(e) for e in emojis_i], only_emoticons=only_emoticons )
else:
labels_i = np.array([np.mean(emoji2sent(e, only_emoticons=only_emoticons), axis=0) for e in emojis_i])
# and filter out all samples we have no label for:
wrong_labels = np.isnan(np.linalg.norm(labels_i, axis=1))
labels_i = labels_i[np.invert(wrong_labels)]
plain_text_i = plain_text_i[np.invert(wrong_labels)]
emojis_i = emojis_i[np.invert(wrong_labels)]
print("imported " + str(len(labels_i)) + " samples")
if self.labels is None:
self.labels = labels_i
else:
self.labels = np.append(self.labels, labels_i, axis=0)
if self.emojis is None:
self.emojis = emojis_i
else:
self.emojis = pd.concat([self.emojis,emojis_i],ignore_index=True)
if self.plain_text is None:
self.plain_text = plain_text_i
else:
self.plain_text = pd.concat([self.plain_text,plain_text_i],ignore_index=True)
if progress_callback is not None:
progress_callback((i+1)/n)
print("imported " + str(len(self.labels)) + " samples")
def apply_stemming_and_lemmatization(self): def apply_stemming_and_lemmatization(self, progress_callback = None):
""" """
apply stemming and lemmatization to plain text samples apply stemming and lemmatization to plain text samples
""" """
self.use_stemming = True
self.use_lemmatization = True
print("apply stemming and lemmatization...")
stemmer = SnowballStemmer("english") stemmer = SnowballStemmer("english")
n = self.plain_text.shape[0] * 2 # 2 for loops
i = 0
for key in self.plain_text.keys(): for key in self.plain_text.keys():
stemmed_sent = [] stemmed_sent = []
for word in self.plain_text[key].split(" "): for word in self.plain_text[key].split(" "):
@ -223,6 +284,11 @@ class sample_data_manager(object):
stemmed_sent.append(word_stemmed) stemmed_sent.append(word_stemmed)
stemmed_sent = (" ").join(stemmed_sent) stemmed_sent = (" ").join(stemmed_sent)
self.plain_text[key] = stemmed_sent self.plain_text[key] = stemmed_sent
i += 1
if progress_callback is not None and i % 1024 == 0:
progress_callback(i / n)
lemmatizer = WordNetLemmatizer() lemmatizer = WordNetLemmatizer()
for key in self.plain_text.keys(): for key in self.plain_text.keys():
@ -234,6 +300,10 @@ class sample_data_manager(object):
lemmatized_sent.append(word_lemmatized) lemmatized_sent.append(word_lemmatized)
lemmatized_sent = (" ").join(lemmatized_sent) lemmatized_sent = (" ").join(lemmatized_sent)
self.plain_text[key] = lemmatized_sent self.plain_text[key] = lemmatized_sent
i += 1
if progress_callback is not None and i % 1024 == 0:
progress_callback(i / n)
print("stemming and lemmatization done")
def generate_emoji_count_and_weights(self): def generate_emoji_count_and_weights(self):
""" """
@ -503,8 +573,12 @@ class pipeline_manager(object):
"""fitting the pipeline""" """fitting the pipeline"""
self.pipeline.fit(X,y) self.pipeline.fit(X,y)
def predict(self,X): def predict(self,X, use_stemming=True, use_lemmatization=True):
"""predict""" """predict"""
if use_stemming:
X = np.array(batch_stem(X))
if use_lemmatization:
X = np.array(batch_lemm(X))
return self.pipeline.predict(X) return self.pipeline.predict(X)
@ -558,7 +632,7 @@ class trainer(object):
for j in range(n): for j in range(n):
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])) 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]))
if progress_callback is not None: if progress_callback is not None:
progress_callback() progress_callback(j / n)
pred, yt = self.test() pred, yt = self.test()
mean_squared_error = ((pred - yt)**2).mean(axis=0) mean_squared_error = ((pred - yt)**2).mean(axis=0)
print("#" + str(j) + ": loss: ", mean_squared_error) print("#" + str(j) + ": loss: ", mean_squared_error)
@ -578,6 +652,6 @@ class trainer(object):
''' '''
if self.sdm.X is None: if self.sdm.X is None:
self.sdm.create_train_test_split() self.sdm.create_train_test_split()
return self.pm.predict(self.sdm.Xt), self.sdm.yt return self.pm.predict(self.sdm.Xt, use_lemmatization=False, use_stemming=False), self.sdm.yt