exploration/train_nn_models.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"# Train Hate Speech Classification Neural Network Models"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"With the data cleaned and processed, this notebook implements model training on the data sets. The code in this notebook assumes that cleaned data is in the filepath `\"data/combined_deduped.csv\"`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import spacy\n",
"import matplotlib.pyplot as plt\n",
"import wandb\n",
"from wandb.keras import WandbCallback\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
"from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_fscore_support, classification_report\n",
"from tensorflow import keras\n",
"from tensorflow.keras.models import Sequential, Model\n",
"from tensorflow.keras.layers import Dense, Dropout, Flatten, Embedding, LSTM\n",
"from tensorflow.keras.layers import Input, Embedding, Conv1D, Concatenate, GlobalMaxPooling1D\n",
"from tensorflow.keras.callbacks import EarlyStopping\n",
"from tensorflow.keras import regularizers\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"\n",
"import en_core_web_md"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"# Neural Network Baselines"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"## MLP"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"The \"baseline\" implementation of a neural network is more debatable. Our choice was to set up a simple multilayer perceptron with enough neurons and layers to be functional and little customization beyond that. \n",
"\n",
"First, we instantiate a CountVectorizer to transform the words into integer counts of word appearance. Next, we scale that data and convert it from sparse matrices to arrays. Finally, we create, compile, and fit our model."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"vect = CountVectorizer(stop_words='english', max_features=3000)\n",
"x_train_vect = vect.fit_transform(x_train)\n",
"x_val_vect = vect.transform(x_val)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"scaler = StandardScaler(with_mean=False)\n",
"x_train_vect_scale = scaler.fit_transform(x_train_vect)\n",
"x_val_vect_scale = scaler.transform(x_val_vect)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"x_train_vect_scale = x_train_vect_scale.toarray()\n",
"x_val_vect_scale = x_val_vect_scale.toarray()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"model = Sequential([\n",
" Dense(128, input_dim=3000, activation='relu'),\n",
" Dense(32, activation='relu'),\n",
" Dense(1, activation='sigmoid')\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
" Project page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23</a><br/>\n",
" Run page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23/runs/1s3qjow5\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23/runs/1s3qjow5</a><br/>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 99459 samples\n",
"Epoch 1/5\n",
"99459/99459 [==============================] - 37s 376us/sample - loss: 0.6787 - accuracy: 0.8567\n",
"Epoch 2/5\n",
"99459/99459 [==============================] - 24s 239us/sample - loss: 0.2212 - accuracy: 0.9178\n",
"Epoch 3/5\n",
"99459/99459 [==============================] - 23s 231us/sample - loss: 0.1629 - accuracy: 0.9403\n",
"Epoch 4/5\n",
"99459/99459 [==============================] - 23s 227us/sample - loss: 0.1284 - accuracy: 0.9523\n",
"Epoch 5/5\n",
"99459/99459 [==============================] - 22s 216us/sample - loss: 0.1105 - accuracy: 0.9586\n"
]
}
],
"source": [
"wandb.init(project=\"allay-ds-23\")\n",
"\n",
"results = model.fit(x_train_vect_scale,\n",
" y_train,\n",
" epochs=5,\n",
" batch_size=20)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"17552/17552 [==============================] - 7s 419us/sample - loss: 0.7213 - accuracy: 0.8445\n",
"17552/17552 [==============================] - 3s 188us/sample\n"
]
}
],
"source": [
"score = model.evaluate(x_val_vect_scale, y_val)\n",
"y_pred = model.predict(x_val_vect_scale, batch_size=64, verbose=1)\n",
"y_pred = np.round(y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" False 0.8485 0.8924 0.8699 10223\n",
" True 0.8382 0.7777 0.8069 7329\n",
"\n",
" accuracy 0.8445 17552\n",
" macro avg 0.8434 0.8351 0.8384 17552\n",
"weighted avg 0.8442 0.8445 0.8436 17552\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_val, y_pred, digits=4))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"accuracy, precision, recall, f1 = .8445, .8382, .7777, .8069\n",
"\n",
"wandb.log({'accuracy':accuracy, 'recall':recall, \n",
" 'f1':f1, 'precision':precision})"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"Next we try the same thing, but with a Tf-Idf vectorizer."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"vect = TfidfVectorizer(stop_words='english', max_features=3000)\n",
"x_train_vect = vect.fit_transform(x_train)\n",
"x_val_vect = vect.transform(x_val)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"x_train_vect = x_train_vect.toarray()\n",
"x_val_vect = x_val_vect.toarray()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"model = Sequential([\n",
" Dense(128, input_dim=3000, activation='relu'),\n",
" Dense(32, activation='relu'),\n",
" Dense(1, activation='sigmoid')\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
" Project page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23</a><br/>\n",
" Run page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23/runs/rdo8hvke\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23/runs/rdo8hvke</a><br/>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 99459 samples\n",
"Epoch 1/5\n",
"99459/99459 [==============================] - 25s 253us/sample - loss: 0.3500 - accuracy: 0.8495\n",
"Epoch 2/5\n",
"99459/99459 [==============================] - 31s 307us/sample - loss: 0.3024 - accuracy: 0.8731\n",
"Epoch 3/5\n",
"99459/99459 [==============================] - 20s 206us/sample - loss: 0.2700 - accuracy: 0.8885\n",
"Epoch 4/5\n",
"99459/99459 [==============================] - 20s 204us/sample - loss: 0.2257 - accuracy: 0.9090\n",
"Epoch 5/5\n",
"99459/99459 [==============================] - 20s 203us/sample - loss: 0.1735 - accuracy: 0.9319\n"
]
}
],
"source": [
"wandb.init(project=\"allay-ds-23\")\n",
"results = model.fit(x_train_vect,\n",
" y_train,\n",
" epochs=5,\n",
" batch_size=20)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"17552/17552 [==============================] - 1s 54us/sample\n",
" precision recall f1-score support\n",
"\n",
" False 0.8541 0.9035 0.8781 10223\n",
" True 0.8535 0.7847 0.8177 7329\n",
"\n",
" accuracy 0.8539 17552\n",
" macro avg 0.8538 0.8441 0.8479 17552\n",
"weighted avg 0.8538 0.8539 0.8528 17552\n",
"\n"
]
}
],
"source": [
"y_pred = model.predict(x_val_vect, batch_size=64, verbose=1)\n",
"y_pred = np.round(y_pred)\n",
"print(classification_report(y_val, y_pred, digits=4))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"accuracy, precision, recall, f1 = .8539, .8535, .7847, .8177\n",
"\n",
"wandb.log({'accuracy':accuracy, 'recall':recall, \n",
" 'f1':f1, 'precision':precision})"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"## RNN + LSTM"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"The next model we try is a recurrent neural network with LSTM. This relies on having pickled lemmatized data in the filepath `data/lemmas_2020-05-04-16-27-18Z.pkl.xz`. See other notebook for lemmatization methods."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"lemmas = pd.read_pickle(\"data/lemmas_2020-05-04-16-27-18Z.pkl.xz\", compression='xz')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sm_lemmas</th>\n",
" <th>md_lemmas</th>\n",
" <th>lg_lemmas</th>\n",
" <th>inappropriate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[beat, Dr., Dre, urbeat, Wired, Ear, Headphone...</td>\n",
" <td>[beat, Dr., Dre, urbeat, Wired, ear, Headphone...</td>\n",
" <td>[beat, Dr., Dre, urBeats, wire, Ear, Headphone...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sm_lemmas \\\n",
"0 [beat, Dr., Dre, urbeat, Wired, Ear, Headphone... \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... \n",
"2 [time, draw, close, 128591;🏻, Father, d... \n",
"3 [notice, start, act, different, distant, peep,... \n",
"4 [forget, unfollower, believe, grow, new, follo... \n",
"\n",
" md_lemmas \\\n",
"0 [beat, Dr., Dre, urbeat, Wired, ear, Headphone... \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... \n",
"2 [time, draw, close, 128591;🏻, Father, d... \n",
"3 [notice, start, act, different, distant, peep,... \n",
"4 [forget, unfollower, believe, grow, new, follo... \n",
"\n",
" lg_lemmas inappropriate \n",
"0 [beat, Dr., Dre, urBeats, wire, Ear, Headphone... True \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... True \n",
"2 [time, draw, close, 128591;🏻, Father, d... False \n",
"3 [notice, start, act, different, distant, peep,... False \n",
"4 [forget, unfollower, believe, grow, new, follo... False "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lemmas.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"medium_lemmas = lemmas[[\"md_lemmas\", \"inappropriate\"]].copy()\n",
"del lemmas"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"def make_lowercase(word_list):\n",
" \"\"\"Define a function that makes all words in a list lowercase\"\"\"\n",
" new_words = []\n",
" for word in word_list: \n",
" word = word.lower()\n",
" new_words.append(word)\n",
" return new_words"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"medium_lemmas[\"md_lemmas\"] = medium_lemmas[\"md_lemmas\"].apply(lambda x: make_lowercase(x))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>md_lemmas</th>\n",
" <th>inappropriate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <tr>\n",
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" <td>[@papapishu, man, fucking, rule, party, perpet...</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>[time, draw, close, 128591;&#127995, father, d...</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>False</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" md_lemmas inappropriate\n",
"0 [beat, dr., dre, urbeat, wired, ear, headphone... True\n",
"1 [@papapishu, man, fucking, rule, party, perpet... True\n",
"2 [time, draw, close, 128591;🏻, father, d... False\n",
"3 [notice, start, act, different, distant, peep,... False\n",
"4 [forget, unfollower, believe, grow, new, follo... False"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medium_lemmas.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"train, test = train_test_split(medium_lemmas, test_size=.2, random_state=42)\n",
"train, val = train_test_split(train, test_size=.15, random_state=42)\n",
"target = 'inappropriate'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>md_lemmas</th>\n",
" <th>inappropriate</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>40507</th>\n",
" <td>[look, work, pottstown, check, job, https://t....</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91514</th>\n",
" <td>[@sawngbyrd28, damn, annoying, leave, smh]</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21736</th>\n",
" <td>[feed(wf, ipl_t20, 2017, asiasat, 5@100.5east,...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18155</th>\n",
" <td>[finish, speak, counselor, @fresnocity, susan,...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137374</th>\n",
" <td>[check, new, trending, funny, gif, kanye, west...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" md_lemmas inappropriate\n",
"40507 [look, work, pottstown, check, job, https://t.... True\n",
"91514 [@sawngbyrd28, damn, annoying, leave, smh] True\n",
"21736 [feed(wf, ipl_t20, 2017, asiasat, 5@100.5east,... True\n",
"18155 [finish, speak, counselor, @fresnocity, susan,... False\n",
"137374 [check, new, trending, funny, gif, kanye, west... False"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"y_train = train[target]\n",
"y_val = val[target]\n",
"\n",
"x_train = train.drop([target], axis=1)\n",
"x_val = val.drop([target], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"We look to get each lemma in our file (up to a maximum number of features) coded to an integer so we can pass these tweets into an embedding layer. Since preprocessing was done in the lemmatization step, we create a CountVectorizer -- whose attributes we will access to create our vocab -- but turn off all of its automatic text processing."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"def do_nothing(tokens):\n",
" return tokens"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"vectorizer = CountVectorizer(tokenizer=do_nothing, preprocessor=None, \n",
" lowercase=False, stop_words=\"english\", \n",
" max_features=8000, min_df=.00005)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ajenk\\.virtualenvs\\allay-ds-cRyEcJS9\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:507: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
" warnings.warn(\"The parameter 'token_pattern' will not be used\"\n",
"C:\\Users\\ajenk\\.virtualenvs\\allay-ds-cRyEcJS9\\lib\\site-packages\\sklearn\\feature_extraction\\text.py:385: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['b', 'c', 'd', 'e', 'f', 'g', 'h', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y'] not in stop_words.\n",
" 'stop_words.' % sorted(inconsistent))\n"
]
},
{
"data": {
"text/plain": [
"CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=False, max_df=1.0, max_features=8000, min_df=5e-05,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words='english',\n",
" strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=<function do_nothing at 0x00000227DC571678>,\n",
" vocabulary=None)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorizer.fit(x_train[\"md_lemmas\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"The CountVectorizer now contains 8,000 words that comprise the vocabulary we will use as features. \n",
"\n",
"We take that vocabulary and construct a dictionary translating each word in it to an integer. \n",
"Finally, we use that dictionary to encode our lemmas into integer form for model input."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"word2idx = {word: idx for idx, word in enumerate(vectorizer.get_feature_names())}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"def to_sequence(index, text):\n",
" indexes = [index[word] for word in text if word in index]\n",
" return indexes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"x_train[\"integers\"] = x_train[\"md_lemmas\"].apply(lambda x: to_sequence(word2idx, x))\n",
"x_val[\"integers\"] = x_val[\"md_lemmas\"].apply(lambda x: to_sequence(word2idx, x))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>md_lemmas</th>\n",
" <th>integers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14520</th>\n",
" <td>[.@pepsi, think, protest, hip, cute, thing, mi...</td>\n",
" <td>[7163, 5708, 3549, 2089, 7162, 4698, 5751, 3720]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68713</th>\n",
" <td>[mississauga, load, line, finally, click, spen...</td>\n",
" <td>[4389, 4344, 2934, 1692, 6705, 7686, 4908, 436...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117901</th>\n",
" <td>[kejriwal, accept, role, making, udtapunjab, c...</td>\n",
" <td>[4106, 481, 6111, 4507, 7424, 1883, 2192, 5745]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61528</th>\n",
" <td>[@aseasyasriding, @lastnotlost, @shoestringcyc...</td>\n",
" <td>[4330]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115752</th>\n",
" <td>[time, like, boxer, rapper, etc, bitch, fuckin...</td>\n",
" <td>[7215, 4330, 5829, 1131, 3114, 3478, 1569, 513...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" md_lemmas \\\n",
"14520 [.@pepsi, think, protest, hip, cute, thing, mi... \n",
"68713 [mississauga, load, line, finally, click, spen... \n",
"117901 [kejriwal, accept, role, making, udtapunjab, c... \n",
"61528 [@aseasyasriding, @lastnotlost, @shoestringcyc... \n",
"115752 [time, like, boxer, rapper, etc, bitch, fuckin... \n",
"\n",
" integers \n",
"14520 [7163, 5708, 3549, 2089, 7162, 4698, 5751, 3720] \n",
"68713 [4389, 4344, 2934, 1692, 6705, 7686, 4908, 436... \n",
"117901 [4106, 481, 6111, 4507, 7424, 1883, 2192, 5745] \n",
"61528 [4330] \n",
"115752 [7215, 4330, 5829, 1131, 3114, 3478, 1569, 513... "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"As reviews are likely to be longer than tweets, we increase our max sequence length to allow our model to process texts of longer length. We then pad the sequences up to that max length, since our model expects a consistent input dimension."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_seq_length = (max(x_train[\"integers\"].apply(lambda x: len(x))) * 2)\n",
"max_seq_length"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"8000"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(word2idx)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"n_features = len(vectorizer.get_feature_names())\n",
"x_train_sequences = pad_sequences(x_train[\"integers\"], maxlen = max_seq_length, value=n_features)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"x_val_sequences = pad_sequences(x_val[\"integers\"], maxlen=max_seq_length, value=n_features)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/plain": [
"array([8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000,\n",
" 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000,\n",
" 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000,\n",
" 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000,\n",
" 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 7163, 5708, 3549,\n",
" 2089, 7162, 4698, 5751, 3720])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train_sequences[0]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"np.save(\"data/x_train_sequences.npy\", x_train_sequences)\n",
"np.save(\"data/x_val_sequences.npy\", x_val_sequences)\n",
"y_train.to_pickle(\"data/y_train.xz\")\n",
"y_val.to_pickle(\"data/y_val.xz\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"embedding (Embedding) (None, 60, 64) 512064 \n",
"_________________________________________________________________\n",
"lstm (LSTM) (None, 60, 64) 33024 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 64) 33024 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 578,177\n",
"Trainable params: 578,177\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"model = Sequential()\n",
"model.add(Embedding(len(vectorizer.get_feature_names()) + 1,\n",
" 64,\n",
" input_length=max_seq_length))\n",
"model.add(LSTM(64, return_sequences=True))\n",
"model.add(LSTM(64))\n",
"model.add(Dense(units=1, activation='sigmoid'))\n",
" \n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"print(model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
" Project page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23</a><br/>\n",
" Run page: <a href=\"https://app.wandb.ai/alexmjn/allay-ds-23/runs/2i9t6s1m\" target=\"_blank\">https://app.wandb.ai/alexmjn/allay-ds-23/runs/2i9t6s1m</a><br/>\n",
" "
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 99459 samples, validate on 17552 samples\n",
"Epoch 1/3\n",
"99459/99459 [==============================] - 109s 1ms/sample - loss: 0.3015 - accuracy: 0.8743 - val_loss: 0.3287 - val_accuracy: 0.8642\n",
"Epoch 3/3\n",
"99459/99459 [==============================] - 117s 1ms/sample - loss: 0.2709 - accuracy: 0.8864 - val_loss: 0.3361 - val_accuracy: 0.8624\n"
]
}
],
"source": [
"WANDB_NOTEBOOK_NAME = \"train_nn_models.ipynb\"\n",
"wandb.init(project=\"allay-ds-23\", config = {\"epochs\": 3, \"optimizer\": \"adam\", \"batch_size\": 20})\n",
"results = model.fit(x_train_sequences,\n",
" y_train,\n",
" epochs=3,\n",
" batch_size=20,\n",
" validation_data=(x_val_sequences, y_val),\n",
" callbacks=[WandbCallback(validation_data=(x_val_sequences, y_val),\n",
" labels=[\"appropriate\", \"inappropriate\"])])"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"### Assessing Model Errors"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"After a good deal of hyperparameter tuning (done with the training .py files through hyperparameter sweeps), we hit a barrier of around 88% accuracy. To guide future processing, we wanted to see the model's biggest errors. The following code takes a model and validation data and returns a data frame with sequences, predictions, actual results, the index of the tweet, and the associated lemmas."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# size of validation set\n",
"N_SEQUENCES = 17552\n",
"\n",
"def get_errors_df(model, x_val, y_val, n_sequences):\n",
" \"\"\"Get model predictions and actual results\n",
" \n",
" Takes a split validation set and returns a data frame with \n",
" predictions, actual results, errors, the sequences predicted on, \n",
" and the indices of the validation items from the original data set.\n",
" These indices will be used to look up the lemmas from the original,\n",
" non-transformed validation set.\"\"\"\n",
" y_pred = model.predict(x_val, batch_size=64)\n",
" sequence_list = [x_val_sequences[i, :] for i in range(0, n_sequences)]\n",
" sequences = pd.Series(sequence_list)\n",
" y_pred_reshape = np.reshape(y_pred, -1)\n",
" predictions = pd.Series(y_pred_reshape)\n",
"\n",
" validation_results = pd.concat([sequences, predictions], axis=1)\n",
" new_df = y_val.reset_index()\n",
" combined_df = pd.concat([validation_results, new_df], axis=1)\n",
" combined_df.columns = [\"sequences\", \"predictions\", \"index\", \"inappropriate\"]\n",
" combined_df[\"error\"] = combined_df[\"inappropriate\"] - combined_df[\"predictions\"]\n",
" \n",
" return combined_df\n",
"\n",
"def index_lookup(index, df, colname):\n",
" \"\"\"This function looks up the contents of a given column/index in a given data frame.\n",
" \n",
" The intended use is, given a set of indices in a given data frame that correspond to\n",
" values in a second data frame, to look up those values in the second data frame,\n",
" then be able to place them back in the first data frame. In our project, this links the \n",
" predictions, results, and sequences in our processed data set back to the lemmas in \n",
" the original data set.\"\"\"\n",
" series = df[colname].loc[[index]]\n",
" raw_lemma_list = series.reset_index().iloc[0][colname]\n",
" return raw_lemma_list"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"combined_df = get_errors_df(model, x_val_sequences, y_val, N_SEQUENCES)\n",
"combined_df[\"lemmas\"] = combined_df[\"index\"].apply(lambda x: index_lookup(x, val, \"md_lemmas\"))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>sequences</th>\n",
" <th>predictions</th>\n",
" <th>index</th>\n",
" <th>inappropriate</th>\n",
" <th>error</th>\n",
" <th>lemmas</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[8000, 8000, 8000, 8000, 8000, 8000, 8000, 800...</td>\n",
" <td>0.652034</td>\n",
" <td>40507</td>\n",
" <td>True</td>\n",
" <td>0.347966</td>\n",
" <td>[look, work, pottstown, check, job, https://t....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[8000, 8000, 8000, 8000, 8000, 8000, 8000, 800...</td>\n",
" <td>0.369565</td>\n",
" <td>91514</td>\n",
" <td>True</td>\n",
" <td>0.630435</td>\n",
" <td>[@sawngbyrd28, damn, annoying, leave, smh]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[8000, 8000, 8000, 8000, 8000, 8000, 8000, 800...</td>\n",
" <td>0.445245</td>\n",
" <td>21736</td>\n",
" <td>True</td>\n",
" <td>0.554755</td>\n",
" <td>[feed(wf, ipl_t20, 2017, asiasat, 5@100.5east,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[8000, 8000, 8000, 8000, 8000, 8000, 8000, 800...</td>\n",
" <td>0.056739</td>\n",
" <td>18155</td>\n",
" <td>False</td>\n",
" <td>-0.056739</td>\n",
" <td>[finish, speak, counselor, @fresnocity, susan,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[8000, 8000, 8000, 8000, 8000, 8000, 8000, 800...</td>\n",
" <td>0.068813</td>\n",
" <td>137374</td>\n",
" <td>False</td>\n",
" <td>-0.068813</td>\n",
" <td>[check, new, trending, funny, gif, kanye, west...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sequences predictions index \\\n",
"0 [8000, 8000, 8000, 8000, 8000, 8000, 8000, 800... 0.652034 40507 \n",
"1 [8000, 8000, 8000, 8000, 8000, 8000, 8000, 800... 0.369565 91514 \n",
"2 [8000, 8000, 8000, 8000, 8000, 8000, 8000, 800... 0.445245 21736 \n",
"3 [8000, 8000, 8000, 8000, 8000, 8000, 8000, 800... 0.056739 18155 \n",
"4 [8000, 8000, 8000, 8000, 8000, 8000, 8000, 800... 0.068813 137374 \n",
"\n",
" inappropriate error lemmas \n",
"0 True 0.347966 [look, work, pottstown, check, job, https://t.... \n",
"1 True 0.630435 [@sawngbyrd28, damn, annoying, leave, smh] \n",
"2 True 0.554755 [feed(wf, ipl_t20, 2017, asiasat, 5@100.5east,... \n",
"3 False -0.056739 [finish, speak, counselor, @fresnocity, susan,... \n",
"4 False -0.068813 [check, new, trending, funny, gif, kanye, west... "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"combined_df.to_pickle(\"data/model_assessment.xz\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"## CNN"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"The next model we try is a convolutional neural network as described in [arXiv:1510.03820 [cs.CL]](https://arxiv.org/abs/1510.03820).\n",
"\n",
"Implementation details were derived from:\n",
"- 'Transfer learning with spaCy embeddings' section of [Getting started with Keras for NLP](https://remove-js.com/https://nlpforhackers.io/keras-intro/)\n",
"- [Introduction to 1D Convolutional Neural Networks for NLP](https://github.com/Tixierae/deep_learning_NLP/blob/master/CNN_IMDB/cnn_imdb.ipynb)\n",
"\n",
"This relies on having pickled lemmatized data in the filepath `data/lemmas_2020-05-04-16-27-18Z.pkl.xz`. See other notebook for lemmatization methods."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"lemmas = pd.read_pickle(\"data/lemmas_2020-05-04-16-27-18Z.pkl.xz\", compression = 'xz')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sm_lemmas</th>\n",
" <th>md_lemmas</th>\n",
" <th>lg_lemmas</th>\n",
" <th>inappropriate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[beat, Dr., Dre, urbeat, Wired, Ear, Headphone...</td>\n",
" <td>[beat, Dr., Dre, urbeat, Wired, ear, Headphone...</td>\n",
" <td>[beat, Dr., Dre, urBeats, wire, Ear, Headphone...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>[@Papapishu, man, fucking, rule, party, perpet...</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>[time, draw, close, 128591;&#127995, Father, d...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>[notice, start, act, different, distant, peep,...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>[forget, unfollower, believe, grow, new, follo...</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sm_lemmas \\\n",
"0 [beat, Dr., Dre, urbeat, Wired, Ear, Headphone... \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... \n",
"2 [time, draw, close, 128591;🏻, Father, d... \n",
"3 [notice, start, act, different, distant, peep,... \n",
"4 [forget, unfollower, believe, grow, new, follo... \n",
"\n",
" md_lemmas \\\n",
"0 [beat, Dr., Dre, urbeat, Wired, ear, Headphone... \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... \n",
"2 [time, draw, close, 128591;🏻, Father, d... \n",
"3 [notice, start, act, different, distant, peep,... \n",
"4 [forget, unfollower, believe, grow, new, follo... \n",
"\n",
" lg_lemmas inappropriate \n",
"0 [beat, Dr., Dre, urBeats, wire, Ear, Headphone... True \n",
"1 [@Papapishu, man, fucking, rule, party, perpet... True \n",
"2 [time, draw, close, 128591;🏻, Father, d... False \n",
"3 [notice, start, act, different, distant, peep,... False \n",
"4 [forget, unfollower, believe, grow, new, follo... False "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lemmas.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Using lemmas created with spaCy's 'en_core_web_md' pre-trained model.\n",
"medium_lemmas = lemmas[[\"md_lemmas\", \"inappropriate\"]].copy()\n",
"del lemmas"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EMBEDDINGS_LEN = 300\n"
]
}
],
"source": [
"# Initialize NLP model and related constants\n",
"nlp_md = en_core_web_md.load()\n",
"\n",
"EMBEDDINGS_LEN = len(nlp_md.vocab['apple'].vector)\n",
"print(\"EMBEDDINGS_LEN =\", EMBEDDINGS_LEN)"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [
"We look to get each lemma in our file (up to a maximum number of features) coded to an integer so we can pass these tweets into an embedding layer. Since preprocessing was done in the lemmatization step, we create a CountVectorizer -- whose attributes we will access to create our vocab -- but turn off all of its automatic text processing."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"def do_nothing(tokens):\n",
" \"\"\"Returns the variable passed as an argument.\n",
"\n",
" This function is a NO-OP to allow initializing a CountVectorizer with pre-tokenized data.\n",
"\n",
" :param tokens: collection of tokens\n",
" :returns: unaltered collection of tokens\n",
" \"\"\"\n",
" return tokens"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N_FEATURES: 5000\n"
]
}
],
"source": [
"# Instantiate and fit vectorizer instance and define related constants\n",
"\n",
"vectorizer = CountVectorizer(tokenizer=do_nothing, preprocessor=None, \n",
" lowercase=False, stop_words=\"english\", \n",
" max_features=5000, min_df=.0001).fit(medium_lemmas['md_lemmas'])\n",
"\n",
"N_FEATURES = len(vectorizer.get_feature_names())\n",
"print('N_FEATURES:', N_FEATURES)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Create sequences of integer indexes into the vocabulary\n",
"\n",
"word2idx = {word: idx for idx, word in enumerate(vectorizer.get_feature_names())}\n",
"\n",
"def to_sequence(index, text):\n",
" indexes = [index[word] for word in text if word in index]\n",
" return indexes\n",
"\n",
"medium_lemmas[\"md_sequences\"] = medium_lemmas[\"md_lemmas\"].apply(lambda x: to_sequence(word2idx, x))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MAX_SEQ_LENGTH: 60\n"
]
}
],
"source": [
"# Define MAX_SEQ_LENGTH constant to be twice the max length in out training data set.en_core_web_md\n",
"# We expect the content in production to be longer than the content we are training on.\n",
"\n",
"MAX_SEQ_LENGTH = (max(medium_lemmas['md_sequences'].apply(lambda x: len(x))) * 2)\n",
"print('MAX_SEQ_LENGTH:', MAX_SEQ_LENGTH)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Reproducible train / val / test split\n",
"def tvt_split(df, target='inappropriate'):\n",
" x_train, x_test, y_train, y_test = train_test_split(df.drop(columns=target), df[target], test_size=0.2, random_state=42)\n",
" x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.15, random_state=42)\n",
" return x_train, y_train, x_val, y_val # , x_test, y_test"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train: (99459, 2) (99459,)\n",
"Val: (17552, 2) (17552,)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>md_lemmas</th>\n",
" <th>md_sequences</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14520</th>\n",
" <td>[.@pepsi, think, protest, hip, cute, thing, mi...</td>\n",
" <td>[4582, 3816, 2815, 2036, 4581, 3333, 3835, 2912]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68713</th>\n",
" <td>[Mississauga, load, line, finally, click, Spen...</td>\n",
" <td>[3192, 3171, 2507, 1815, 3183, 4614, 1491]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117901</th>\n",
" <td>[kejriwal, accept, role, making, udtapunjab, c...</td>\n",
" <td>[1224, 4036, 3253, 4721, 1919, 3830]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61528</th>\n",
" <td>[@AsEasyAsRiding, @lastnotlost, @shoestringcyc...</td>\n",
" <td>[3165]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115752</th>\n",
" <td>[time, like, boxer, rapper, etc, bitch, fuckin...</td>\n",
" <td>[4610, 3165, 3880, 1540, 2600, 2779, 1748, 352...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" md_lemmas \\\n",
"14520 [.@pepsi, think, protest, hip, cute, thing, mi... \n",
"68713 [Mississauga, load, line, finally, click, Spen... \n",
"117901 [kejriwal, accept, role, making, udtapunjab, c... \n",
"61528 [@AsEasyAsRiding, @lastnotlost, @shoestringcyc... \n",
"115752 [time, like, boxer, rapper, etc, bitch, fuckin... \n",
"\n",
" md_sequences \n",
"14520 [4582, 3816, 2815, 2036, 4581, 3333, 3835, 2912] \n",
"68713 [3192, 3171, 2507, 1815, 3183, 4614, 1491] \n",
"117901 [1224, 4036, 3253, 4721, 1919, 3830] \n",
"61528 [3165] \n",
"115752 [4610, 3165, 3880, 1540, 2600, 2779, 1748, 352... "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train, y_train, val, y_val = tvt_split(medium_lemmas)\n",
"print('Train:', train.shape, y_train.shape)\n",
"print('Val:', val.shape, y_val.shape)\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Pad or trim sequences to MAX_SEQ_LENGTH\n",
"\n",
"x_train = pad_sequences(train['md_sequences'], maxlen = MAX_SEQ_LENGTH, value=N_FEATURES)\n",
"x_val = pad_sequences(val['md_sequences'], maxlen = MAX_SEQ_LENGTH, value=N_FEATURES)\n",
"# x_test = pad_sequences(test['md_sequences'], maxlen = MAX_SEQ_LENGTH, value=N_FEATURES)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Create embeddings index from spaCy's pretrained 'en_core_web_md' model\n",
"\n",
"embeddings_index = np.zeros((N_FEATURES + 1, EMBEDDINGS_LEN))\n",
"for word, idx in word2idx.items():\n",
" try:\n",
" embedding = nlp_md.vocab[word].vector\n",
" embeddings_index[idx] = embedding\n",
" except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [
"# Export data for use in WandB hyperparameter sweeps\n",
"\n",
"import pickle\n",
"\n",
"cnn_data = {\n",
" 'EMBEDDINGS_LEN': EMBEDDINGS_LEN,\n",
" 'MAX_SEQ_LENGTH': MAX_SEQ_LENGTH,\n",
" 'N_FEATURES': N_FEATURES,\n",
" 'x_train': x_train,\n",
" 'x_val': x_val,\n",
" 'y_train': y_train,\n",
" 'y_val': y_val,\n",
" 'embeddings_index': embeddings_index\n",
"}\n",
"\n",
"with open('data/cnn_data.pkl', 'wb') as f:\n",
" pickle.dump(cnn_data, f)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model\"\n",
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_3 (InputLayer) [(None, 60)] 0 \n",
"__________________________________________________________________________________________________\n",
"embedding_2 (Embedding) (None, 60, 300) 1500300 input_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_6 (Dropout) (None, 60, 300) 0 embedding_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_4 (Conv1D) (None, 59, 2) 1202 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_5 (Conv1D) (None, 58, 2) 1802 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_6 (Conv1D) (None, 57, 2) 2402 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"global_max_pooling1d_3 (GlobalM (None, 2) 0 conv1d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"global_max_pooling1d_4 (GlobalM (None, 2) 0 conv1d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"global_max_pooling1d_5 (GlobalM (None, 2) 0 conv1d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_7 (Dropout) (None, 2) 0 global_max_pooling1d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_8 (Dropout) (None, 2) 0 global_max_pooling1d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_9 (Dropout) (None, 2) 0 global_max_pooling1d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, 6) 0 dropout_7[0][0] \n",
" dropout_8[0][0] \n",
" dropout_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_10 (Dropout) (None, 6) 0 concatenate_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_1 (Dense) (None, 1) 7 dropout_10[0][0] \n",
"==================================================================================================\n",
"Total params: 1,505,713\n",
"Trainable params: 5,413\n",
"Non-trainable params: 1,500,300\n",
"__________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"# Build CNN model\n",
"# https://github.com/Tixierae/deep_learning_NLP/blob/master/CNN_IMDB/cnn_imdb.ipynb\n",
"\n",
"# based on pdf\n",
"num_filters = 2\n",
"# based on above repo\n",
"dropout_rate = 0.3\n",
"\n",
"# Input Layer\n",
"inputs = Input(shape=(MAX_SEQ_LENGTH,))\n",
"\n",
"# Embedding layer\n",
"embedding_layer = Embedding(input_dim=N_FEATURES + 1,\n",
" output_dim=EMBEDDINGS_LEN,\n",
" weights=[embeddings_index], # we pass our pre-trained embeddings\n",
" input_length=MAX_SEQ_LENGTH,\n",
" trainable=False,\n",
" ) (inputs)\n",
"\n",
"embedding_dropped = Dropout(dropout_rate)(embedding_layer)\n",
"\n",
"# Convolution Layer - 3 Convolutions, each connected to the input embeddings\n",
"# Branch a\n",
"conv_a = Conv1D(filters = num_filters,\n",
" kernel_size = 2,\n",
" activation = 'relu',\n",
" )(embedding_dropped)\n",
"\n",
"pooled_conv_a = GlobalMaxPooling1D()(conv_a)\n",
"\n",
"pooled_conv_dropped_a = Dropout(dropout_rate)(pooled_conv_a)\n",
"\n",
"# Branch b\n",
"conv_b = Conv1D(filters = num_filters,\n",
" kernel_size = 3,\n",
" activation = 'relu',\n",
" )(embedding_dropped)\n",
"\n",
"pooled_conv_b = GlobalMaxPooling1D()(conv_b)\n",
"\n",
"pooled_conv_dropped_b = Dropout(dropout_rate)(pooled_conv_b)\n",
"\n",
"# Branch c\n",
"conv_c = Conv1D(filters = 2,\n",
" kernel_size = 4,\n",
" activation = 'relu',\n",
" )(embedding_dropped)\n",
"\n",
"pooled_conv_c = GlobalMaxPooling1D()(conv_c)\n",
"\n",
"pooled_conv_dropped_c = Dropout(dropout_rate)(pooled_conv_c)\n",
"\n",
"# Collect branches into a single Convolution layer\n",
"concat = Concatenate()([pooled_conv_dropped_a, pooled_conv_dropped_b, pooled_conv_dropped_c])\n",
"\n",
"concat_dropped = Dropout(dropout_rate)(concat)\n",
"\n",
"# Dense output layer\n",
"prob = Dense(units = 1, # dimensionality of the output space\n",
" activation = 'sigmoid',\n",
" )(concat_dropped)\n",
"\n",
"model = Model(inputs, prob)\n",
"\n",
"model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"print(model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
" Project page: <a href=\"https://app.wandb.ai/jcs_lambda/allay-ds-23\" target=\"_blank\">https://app.wandb.ai/jcs_lambda/allay-ds-23</a><br/>\n",
" Run page: <a href=\"https://app.wandb.ai/jcs_lambda/allay-ds-23/runs/h3csdv3r\" target=\"_blank\">https://app.wandb.ai/jcs_lambda/allay-ds-23/runs/h3csdv3r</a><br/>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Wandb version 0.8.35 is available! To upgrade, please run:\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: $ pip install wandb --upgrade\n",
"Train on 99459 samples, validate on 17552 samples\n",
"Epoch 1/3\n",
"99459/99459 [==============================] - 66s 666us/sample - loss: 0.4234 - accuracy: 0.8265 - val_loss: 0.3663 - val_accuracy: 0.8491\n",
"Epoch 2/3\n",
"99459/99459 [==============================] - 74s 741us/sample - loss: 0.4216 - accuracy: 0.8272 - val_loss: 0.3677 - val_accuracy: 0.8496\n",
"Epoch 3/3\n",
"99459/99459 [==============================] - 78s 783us/sample - loss: 0.4216 - accuracy: 0.8270 - val_loss: 0.3652 - val_accuracy: 0.8516\n"
]
}
],
"source": [
"WANDB_NOTEBOOK_NAME = \"train_nn_models.ipynb\"\n",
"wandb.init(project=\"allay-ds-23\", config = {\"epochs\": 3, \"optimizer\": \"adam\", \"batch_size\": 20})\n",
"results = model.fit(x_train,\n",
" y_train,\n",
" validation_data=(x_val, y_val),\n",
" epochs=3,\n",
" batch_size=20,\n",
" callbacks=[WandbCallback(validation_data=(x_val, y_val),\n",
" labels=[\"appropriate\", \"inappropriate\"])])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"Collapsed": "false"
},
"outputs": [
{
"data": {
"image/png": 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{
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"text": [
"17552/17552 [==============================] - 2s 131us/sample\n",
" precision recall f1-score support\n",
"\n",
" False 0.8145 0.9651 0.8834 10223\n",
" True 0.9344 0.6934 0.7961 7329\n",
"\n",
" accuracy 0.8516 17552\n",
" macro avg 0.8744 0.8292 0.8397 17552\n",
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"\n"
]
}
],
"source": [
"y_pred = model.predict(x_val, batch_size=64, verbose=1)\n",
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