jupyter notebook ready for labeling
This commit is contained in:
Anne Lorenz
parent
59c664fbb0
commit
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@ -26,7 +26,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -63,7 +63,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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@ -115,48 +115,39 @@
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"print('Number of samples in data set in total: {}'.format(n_rows))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The following cell is disabled to prevent later overwriting."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"# save as csv\n",
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"df.to_csv('../data/interactive_labeling_round_{}.csv'.format(m),\n",
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" sep='|',\n",
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" mode='w',\n",
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" encoding='utf-8',\n",
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" quoting=csv.QUOTE_NONNUMERIC,\n",
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" quotechar='\\'')"
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"#df.to_csv('../data/interactive_labeling.csv',\n",
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"# sep='|',\n",
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"# mode='w',\n",
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"# encoding='utf-8',\n",
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"# quoting=csv.QUOTE_NONNUMERIC,\n",
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"# quotechar='\\'')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PART II\n",
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"\n",
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"In each iteration...\n",
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" 1. We label the next 10 articles manually.\n",
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" \n",
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" 2. We apply the Multinomial Naive Bayes classification algorithm which returns a vector class_probs $(K_1, K_2, ... , K_6)$ per sample with the probabilities $K_i$ per class $i$.\n",
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" \n",
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" 3. We apply class labels automatically where possible. We define a case as distinct, if the estimated probability $K_x > 0.8$ with $x \\in {1,...,6}$. In that case, our program applies the label.\n",
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" \n",
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" 4. We check and improve the automated labeling if necessary."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First we load the previously created dictionary of all article indices (keys) with a list of mentioned organizations (values).\n",
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"We load the previously created dictionary of all article indices (keys) with a list of mentioned organizations (values).\n",
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"In the following, we limit the number of occurences of a certain company name in all labeled articles to 3 to avoid imbalance."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -168,7 +159,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -231,6 +222,22 @@
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"label_next = []"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## PART II\n",
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"\n",
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"In each iteration...\n",
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" 1. We label the next 10 articles manually.\n",
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" \n",
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" 2. We apply the Multinomial Naive Bayes classification algorithm which returns a vector class_probs $(K_1, K_2, ... , K_6)$ per sample with the probabilities $K_i$ per class $i$.\n",
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" \n",
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" 3. We apply class labels automatically where possible. We define a case as distinct, if the estimated probability $K_x > 0.8$ with $x \\in {1,...,6}$. In that case, our program applies the label.\n",
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" \n",
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" 4. We check and improve the automated labeling if necessary."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -242,13 +249,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ce204ae8b66d4557972539bda12a5e6f",
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"model_id": "6c56c792f25843799d540a887e3639f8",
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"version_major": 2,
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"version_minor": 0
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},
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@ -286,16 +293,9 @@
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"display(w)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"NOW PLEASE LET THE FOLLOWING CELLS RUN AGAIN."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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@ -315,37 +315,46 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"# read current data set from csv\n",
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"df = pd.read_csv('../data/interactive_labeling.csv',\n",
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" sep='|',\n",
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" usecols=range(1,12), # drop first column 'unnamed'\n",
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" encoding='utf-8',\n",
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" quoting=csv.QUOTE_NONNUMERIC,\n",
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" quotechar='\\'')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Last round:\n",
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"Last round (no. 0):\n",
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"Number of labeled articles: 0\n",
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"Number of unlabeled articles: 10000\n"
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]
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}
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],
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"source": [
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"# read last data set from csv\n",
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"df = pd.read_csv('../data/interactive_labeling_round_{}.csv'.format(m-1),\n",
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" sep='|',\n",
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" usecols=range(1,12), # drop first column 'unnamed'\n",
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" encoding='utf-8',\n",
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" quoting=csv.QUOTE_NONNUMERIC,\n",
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" quotechar='\\'')\n",
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"\n",
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"print('Last round:')\n",
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"print('Last round (no. {}):'.format(m-1))\n",
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"print('Number of labeled articles: {}'.format(len(df.loc[df['Label'] != -1])))\n",
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"print('Number of unlabeled articles: {}'.format(len(df.loc[df['Label'] == -1])))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"execution_count": 29,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -388,7 +397,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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@ -409,7 +418,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "da64d65008f2458f9f2d21bb875ed75a",
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"model_id": "286c21f192774952bd60d051cf197a8a",
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"version_major": 2,
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"version_minor": 0
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},
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@ -458,7 +467,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cf730482c9b34f909d64d577fe27045b",
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"model_id": "8b626884311344a8b8b33ae57a46f9ef",
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"version_major": 2,
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"version_minor": 0
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},
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@ -507,7 +516,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "03a9887730274dfe880b6e3b3f26f134",
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"model_id": "fb2f45b1947f4cd0a9fc57e33e188348",
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"version_major": 2,
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"version_minor": 0
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},
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@ -556,7 +565,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "7fdb5a8441b444f797b2c6f111791be0",
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"model_id": "1d5d56ee6f9a4f91bdeee367ddedbbc5",
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"version_major": 2,
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"version_minor": 0
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},
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@ -605,7 +614,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ccc699d5ee1f4c6aa6b23ab27bdf8cfb",
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"model_id": "3e1cbc9e465945d2a36b3a40b738f023",
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"version_major": 2,
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"version_minor": 0
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},
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@ -654,7 +663,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "04674ce34fd549d5b7fbdf0982b476bc",
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"model_id": "81ba9eef3ac049f58b57a9300e88a20c",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "dc3abc4c300f4b64aad8acaccc7f4e3a",
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"model_id": "dc76006231a741838d98f30b56208934",
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"version_major": 2,
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"version_minor": 0
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},
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@ -752,7 +761,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f56b384620fd4ec7bfbade536a0526a5",
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"model_id": "111da7a4a7244fe1849502554ffc20c7",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a9e6c9e12a754d4f87385dfa9a0bf356",
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"model_id": "62e65ecb60f04c93be7b9d493c90db9c",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "93f2631f19cf4f05b5b6605e2fb8a010",
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"model_id": "a3c29fd05f7247e0b125aa867601a89b",
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"version_major": 2,
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"version_minor": 0
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},
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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" ''' \n",
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" # show new labels\n",
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" print(df.loc[df['Index'].isin(label_next)]['Label'])\n",
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" \n",
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"\n",
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"# execute function g if button is clicked\n",
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"button_confirm.on_click(g)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "caaeaf0a747e4c88a1ea87e14c6e193a",
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"model_id": "7de4c71a9825428bae7cbe4b6874a1b6",
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"version_major": 2,
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"version_minor": 0
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},
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@ -954,6 +963,23 @@
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"475 2.0\n",
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"849 0.0\n",
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"1854 0.0\n",
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"2569 0.0\n",
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"4080 0.0\n",
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"4185 0.0\n",
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"5874 0.0\n",
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"6091 5.0\n",
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"7628 0.0\n",
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"8684 0.0\n",
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"Name: Label, dtype: float64\n"
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]
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}
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],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# save as csv\n",
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"df.to_csv('../data/interactive_labeling.csv',\n",
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" sep='|',\n",
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" mode='w',\n",
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" encoding='utf-8',\n",
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" quoting=csv.QUOTE_NONNUMERIC,\n",
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" quotechar='\\'')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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" \n",
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"# split data set into labeled and unlabeled samples\n",
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"l_data = df.loc[df['Label'] != -1]\n",
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"u_data = df.loc[df['Label'] == -1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"This round:\n",
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"Number of labeled articles: 0\n",
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"Number of unlabeled articles: 10000\n"
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"This round (no. 1):\n",
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"Number of labeled articles: 10\n",
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"Number of unlabeled articles: 9990\n"
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]
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}
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],
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"source": [
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"# split data set into labeled and unlabeled samples\n",
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"l_data = df.loc[df['Label'] != -1]\n",
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"u_data = df.loc[df['Label'] == -1]\n",
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"\n",
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"print('This round:')\n",
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"print('This round (no. {}):'.format(m))\n",
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"print('Number of labeled articles: {}'.format(len(l_data)))\n",
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"print('Number of unlabeled articles: {}'.format(len(u_data)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"# MNB: starting multinomial naives bayes...\n",
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"\n",
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"# BOW: extracting all words from articles...\n",
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"\n",
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"# BOW: making vocabulary of data set...\n",
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"\n",
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"# BOW: vocabulary consists of 947 features.\n",
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"\n",
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"# MNB: fit training data and calculate matrix...\n",
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"\n",
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"# BOW: calculating matrix...\n",
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"\n",
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"# BOW: calculating frequencies...\n",
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"\n",
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"# MNB: transform testing data to matrix...\n",
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"\n",
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"# BOW: extracting all words from articles...\n",
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"\n",
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"# BOW: calculating matrix...\n",
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"\n",
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"# BOW: calculating frequencies...\n",
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"\n",
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"# MNB: ending multinomial naive bayes\n",
|
||||
"Wall time: 21min 5s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# assign array of classes in order used and array of class probabilities\n",
|
||||
"%time classes, class_probs = MNBInteractive.make_nb(l_data, u_data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -995,26 +1085,48 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"Label classes in the order in which they are used for class_probs:\n",
|
||||
"[0, 2, 5]\n",
|
||||
"[[0.3, 0.3, 0.4]]\n"
|
||||
"[0. 2. 5.]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# assign array of classes in order used and array of class probabilities\n",
|
||||
"#%time classes, class_probs = MNBInteractive.make_nb(l_data, u_data)\n",
|
||||
"classes = [0, 2, 5]\n",
|
||||
"class_probs = [[0.3, 0.3, 0.4]]\n",
|
||||
"\n",
|
||||
"print('Label classes in the order in which they are used for class_probs:')\n",
|
||||
"print(classes)\n",
|
||||
"print(classes)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"First 10 estimations:\n",
|
||||
"\n",
|
||||
"[[0.33335808 0.33330861 0.33333331]\n",
|
||||
" [0.33335762 0.33331573 0.33332665]\n",
|
||||
" [0.33336293 0.33332051 0.33331657]\n",
|
||||
" [0.3333764 0.33330196 0.33332164]\n",
|
||||
" [0.33338693 0.33331068 0.33330239]\n",
|
||||
" [0.33336951 0.33332574 0.33330475]\n",
|
||||
" [0.33341934 0.3333032 0.33327745]\n",
|
||||
" [0.33349444 0.33324293 0.33326262]\n",
|
||||
" [0.33337205 0.3333111 0.33331685]\n",
|
||||
" [0.3333632 0.33331446 0.33332234]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('First 10 estimations:')\n",
|
||||
"print()\n",
|
||||
"print(class_probs[:10])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1044,7 +1156,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -1061,12 +1173,11 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# sort new labeled articles by their estimated probability and return list of indices\n",
|
||||
"# first check sample with lowest estimated probability\n",
|
||||
"list_auto_labeled = [t[0] for t in sorted(tuples_auto_labeled, key=lambda x: x[1])]\n",
|
||||
"\n",
|
||||
"# concatenate labeled and unlabeled data\n",
|
||||
|
|
@ -1079,7 +1190,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1097,6 +1208,7 @@
|
|||
"button_check.on_click(h)\n",
|
||||
"\n",
|
||||
"# while there is still a auto-labeled article not yet checked\n",
|
||||
"# check sample with lowest estimated probability next\n",
|
||||
"while len(list_auto_labeled) > 0:\n",
|
||||
" print('PLEASE CLICK BUTTON BELOW (\\'Check Labels\\') TO CHECK AUTO-LABELED SAMPLE')\n",
|
||||
" display(button_check)"
|
||||
|
|
@ -1104,12 +1216,33 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"End of this round (no. 1):\n",
|
||||
"Number of labeled articles: 10\n",
|
||||
"Number of unlabeled articles: 9990\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('End of this round (no. {}):'.format(m))\n",
|
||||
"print('Number of labeled articles: {}'.format(len(df.loc[df['Label'] != -1])))\n",
|
||||
"print('Number of unlabeled articles: {}'.format(len(df.loc[df['Label'] == -1])))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# save this round as csv\n",
|
||||
"df.to_csv('../data/interactive_labeling_round_{}.csv'.format(m),\n",
|
||||
"# save to csv\n",
|
||||
"df.to_csv('../data/interactive_labeling.csv',\n",
|
||||
" sep='|',\n",
|
||||
" mode='w',\n",
|
||||
" encoding='utf-8',\n",
|
||||
|
|
@ -1121,7 +1254,9 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"NOW PLEASE LET PART II RUN AGAIN. REPEAT UNTIL ALL SAMPLES ARE LABELED."
|
||||
"NOW PLEASE CONTINUE ITERATION. LET PART II RUN AGAIN, CELL BY CELL.\n",
|
||||
"\n",
|
||||
"REPEAT UNTIL ALL SAMPLES ARE LABELED."
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
Loading…
Reference in New Issue