diff --git a/README.md b/README.md
index dac397921c188b966d7e9fead294b65c03a112af..95fc854d4c54c326b5625a1f17597f14b6f1f274 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,110 @@
# Moderation classifier
-## Installation
+# Installation local
```
python -m venv pp_env
source pp_env/bin/activate
pip install -r requirements.txt
```
+# Installation Euler
-## Usage
+## Tensorflow
-### 1. Activation of environment
+```
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+python -m venv --system-site-packages pp_env_tf_python310
+source pp_env_tf_python310/bin/activate
+pip install -r requirements.txt
+```
+
+# Activation of environment
+
+## Local
```
source pp_env/bin/activate
```
-### 2. Preprocessing of dataframe (adding language field)
+## On Euler
+
+### TensorFlow
+```
+srun --pty --mem-per-cpu=3g --gpus=1 --gres=gpumem:12g bash
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source pp_env_tf_python310/bin/activate
+```
+
+# Usage
+
+## 1. Preprocessing of dataframe (adding language field)
```
moderation_classifier --prepare_data path_to_csv
```
+
+## 2. Model training
+
+For the model training several option can be chosen:
+
+```
+Usage: moderation_classifier [OPTIONS] INPUT_DATA
+
+ Run moderation classifier.
+ :param split_data: Binary flag to specify if data should be split.
+ :param prepare_data: Binary flag to specify if data should be prepared.
+ :param text_preprocessing: Binary flag to set text preprocessing.
+ :param newspaper: Name of newspaper selected for training.
+ :param topic: Topic selected for training.
+ :param pretrained_model: Name of pretrained BERT model to use for finetuning.
+ :param train_mnb: Binary flag to specify whether MNB should be trained.
+ :param train_bert: Binary flag to specify whether BERT should be trained.
+ :param eval_mnb: Binary flag to specify whether MNB should be evaluated.
+ :param eval_bert: Binary flag to specify whether BERT should be evaluated.
+ :param input_data: Path to input dataframe.
+
+Options:
+ -s, --split
+ -p, --prepare_data
+ -tp, --text_preprocessing
+ -n, --newspaper TEXT
+ -t, --topic TEXT
+ -pm, --pretrained_model TEXT
+ -tm, --train_mnb
+ -tb, --train_bert
+ -em, --eval_mnb
+ -eb, --eval_bert
+ -tbto, --train_bert_torch
+```
+
+The most important options during training are the model type (MNB or BERT) and the newspaper and topic selected for training.
+
+### MNB
+Training for all newspapers and topics is started with the following command:
+```
+moderation_classifier --train_mnb INPUT_DATA
+```
+
+Training for one newspapers (here: tagesanzeiger) and one topic (here: Wissen) is started with the following command:
+```
+moderation_classifier --newspaper tagesanzeiger --topic Wissen --train_mnb INPUT_DATA
+```
+
+After the training is finished a log-file with all relevant information (path to train data, params for filtering, ..) is stored in `saved_models/MNB_logs`. For the evaluation of the training only the path to this log-file is needed. The evaluation of the training run is started with:
+```
+moderation_classifier --eval_mnb LOG_FILE
+```
+
+### BERT
+Training for all newspapers and topics is started with the following command:
+```
+moderation_classifier --text_preprocessing --pretrained_model "bert-base-german-cased" --train_bert INPUT_DATA
+```
+
+Training for one newspapers (here: tagesanzeiger) and one topic (here: Wissen) is started with the following command:
+```
+moderation_classifier --text_preprocessing --pretrained_model "bert-base-german-cased" --newspaper tagesanzeiger --topic Wissen --train_bert INPUT_DATA
+```
+
+After the training is finished a log-file with all relevant information (path to train data, params for filtering, ..) is stored in `saved_models/BERT_logs`. For the evaluation of the training only the path to this log-file is needed. The evaluation of the training run is started with:
+```
+moderation_classifier --eval_bert LOG_FILE
+```
diff --git a/euler/prepare_data.sh b/euler/prepare_data.sh
new file mode 100755
index 0000000000000000000000000000000000000000..3fab9c0258a9e35519c872661b2455a3966cdec5
--- /dev/null
+++ b/euler/prepare_data.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=4g --time=6:00:00 --wrap "moderation_classifier --prepare_data ../data/tamedia_for_classifier_v3.csv"
diff --git a/euler/prepare_data_cluster.sh b/euler/prepare_data_cluster.sh
new file mode 100755
index 0000000000000000000000000000000000000000..3fab9c0258a9e35519c872661b2455a3966cdec5
--- /dev/null
+++ b/euler/prepare_data_cluster.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=4g --time=6:00:00 --wrap "moderation_classifier --prepare_data ../data/tamedia_for_classifier_v3.csv"
diff --git a/euler/train_model_cluster_germbert_alltopic_allhsprob.sh b/euler/train_model_cluster_germbert_alltopic_allhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..87b6f743183958f6a08abd579faa3fe089096b54
--- /dev/null
+++ b/euler/train_model_cluster_germbert_alltopic_allhsprob.sh
@@ -0,0 +1,15 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "bert-base-german-cased"
+ --text_preprocessing
+ --train_bert data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_germbert_alltopic_highhsprob.sh b/euler/train_model_cluster_germbert_alltopic_highhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..a74b17c9d1f107cdb8d25dc0c22ea37919475d5b
--- /dev/null
+++ b/euler/train_model_cluster_germbert_alltopic_highhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "bert-base-german-cased"
+ --text_preprocessing
+ --hsprob '[0.7,1]'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_germbert_alltopic_lowhsprob.sh b/euler/train_model_cluster_germbert_alltopic_lowhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..5dbc36a0d667c0ba2d349b383fec76b1d0c27cb5
--- /dev/null
+++ b/euler/train_model_cluster_germbert_alltopic_lowhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "bert-base-german-cased"
+ --text_preprocessing
+ --hsprob '[0.0,0.3]'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_germbert_wissentopic_allhsprob.sh b/euler/train_model_cluster_germbert_wissentopic_allhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..276034dafb5b740276c04d3a6c64b47fc7d678dd
--- /dev/null
+++ b/euler/train_model_cluster_germbert_wissentopic_allhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "bert-base-german-cased"
+ --text_preprocessing
+ --topic 'Wissen'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_hsbert_alltopic_allhsprob.sh b/euler/train_model_cluster_hsbert_alltopic_allhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..86aa89bc1328464bce42d4c0fcdab3fd76fb92df
--- /dev/null
+++ b/euler/train_model_cluster_hsbert_alltopic_allhsprob.sh
@@ -0,0 +1,15 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
+ --text_preprocessing
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_hsbert_alltopic_highhsprob.sh b/euler/train_model_cluster_hsbert_alltopic_highhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..165e8c4817a40970c050127c5ca43158fe24f1fc
--- /dev/null
+++ b/euler/train_model_cluster_hsbert_alltopic_highhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
+ --text_preprocessing
+ --hsprob '[0.7,1]'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_hsbert_alltopic_lowhsprob.sh b/euler/train_model_cluster_hsbert_alltopic_lowhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..aea1bc6bfe851c5f1b51c02edcadbb11e9fce542
--- /dev/null
+++ b/euler/train_model_cluster_hsbert_alltopic_lowhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
+ --text_preprocessing
+ --hsprob '[0.0,0.3]'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/euler/train_model_cluster_hsbert_wissentopic_allhsprob.sh b/euler/train_model_cluster_hsbert_wissentopic_allhsprob.sh
new file mode 100755
index 0000000000000000000000000000000000000000..9363b3106e49dacba8e03cca68703312b1d3f7e2
--- /dev/null
+++ b/euler/train_model_cluster_hsbert_wissentopic_allhsprob.sh
@@ -0,0 +1,16 @@
+!/bin/bash
+
+module load gcc/8.2.0 python_gpu/3.10.4 eth_proxy
+source ../pp_env_tf_python310/bin/activate
+
+sbatch --mem-per-cpu=12g\
+ --gpus=1\
+ --gres=gpumem:12g\
+ --time=30:00:00\
+ --wrap "moderation_classifier --newspaper tagesanzeiger
+ --pretrained_model "deepset/bert-base-german-cased-hatespeech-GermEval18Coarse"
+ --text_preprocessing
+ --topic 'Wissen'
+ --train_bert ../data/tamedia_for_classifier_v4_preproc_train.csv"
+
+
diff --git a/moderation_classifier/eval_BERT.py b/moderation_classifier/eval_BERT.py
new file mode 100644
index 0000000000000000000000000000000000000000..84ff6ec60fa9a9c2c8282f953712f9f5ffc80a98
--- /dev/null
+++ b/moderation_classifier/eval_BERT.py
@@ -0,0 +1,130 @@
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
+import tensorflow as tf
+
+import click
+import numpy as np
+import os
+import pandas as pd
+from pathlib import Path
+from typing import List, Union
+
+from sklearn.metrics import precision_recall_fscore_support, accuracy_score
+
+from src.preprocessing_text import TextLoader, TextProcessor
+from src.train_logs import load_logs
+from src.BERT_utils import predict_batches
+from src.eval_utils import gen_scores_dict
+
+
+@click.argument("train_logs")
+def main(train_logs: Union[str, os.PathLike]):
+ """
+ Prepares data and evaluates trained BERT model with TF
+ :param train_logs: path to csv-file containing train logs
+ """
+
+ # Load logs
+ (
+ path_repo,
+ path_model,
+ input_data,
+ text_preprocessing,
+ newspaper,
+ lang,
+ topic,
+ hsprob,
+ remove_duplicates,
+ min_num_words,
+ pretrained_model,
+ ) = load_logs(train_logs)
+
+
+ # Load data and extract only text from tagesanzeiger
+ print("Load and preprocess text")
+ tl = TextLoader(input_data)
+ df_de = tl.load_text_csv(
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ hsprob=hsprob,
+ load_subset=False,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ )
+
+ if text_preprocessing:
+ tp = TextProcessor()
+ text_proc = tp.fit_transform(df_de.text)
+ df_de.text = text_proc
+ comon_topics = tl.get_comments_per_topic(df_de)
+
+ # Load tokenizer and model
+ tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
+ model = TFAutoModelForSequenceClassification.from_pretrained(
+ pretrained_model_name_or_path=path_model
+ )
+
+ # Split text into batches
+ y_pred_all, y_prob_all = predict_batches(df_de.text.values, model, tokenizer)
+
+ import pdb; pdb.set_trace()
+
+ # eval all
+ precision, recall, f1, _ = precision_recall_fscore_support(
+ df_de.label, y_pred_all, average="weighted"
+ )
+ accuracy = accuracy_score(df_de.label, y_pred_all)
+
+ results_all = gen_scores_dict(precision, recall, f1, accuracy)
+
+ # eval per topic
+ topics = [t[0] for t in comon_topics]
+ results_t = dict()
+
+ for t in topics:
+ y_test_t = df_de[df_de.topic == t].label
+ y_pred_t = y_pred_all[df_de.topic == t]
+
+ precision, recall, f1, _ = precision_recall_fscore_support(
+ y_test_t, y_pred_t, average="weighted"
+ )
+ accuracy = accuracy_score(y_test_t, y_pred_t)
+
+ results_t[t] = gen_scores_dict(precision, recall, f1, accuracy)
+
+ # Compute rejection rate
+ reject_rate_all = np.round(df_de.label.mean(), 4) * 100
+ reject_rate_topic = [
+ np.round(df_de[df_de.topic == k].label.mean(), 4) * 100 for k in topics
+ ]
+
+ # Compute number comments
+ num_comm_all = df_de.shape[0]
+ num_comm_topic = [df_de[df_de.topic == k].shape[0] for k in topics]
+
+ # Save results labels
+ df_res_all = pd.DataFrame().from_dict(results_all, orient="index", columns=["all"])
+ df_res_all.loc["rejection rate"] = reject_rate_all
+ df_res_all.loc["number comments"] = num_comm_all
+
+ df_res_topic = pd.DataFrame.from_dict(results_t)
+ df_res_topic.loc["rejection rate"] = reject_rate_topic
+ df_res_topic.loc["number comments"] = num_comm_topic
+
+ df_res = df_res_all.join(df_res_topic)
+ df_res.loc["data"] = [input_data] * df_res.shape[1]
+
+ df_res.to_csv(
+ path_repo + "/results/results_eval_BERT/" + Path(path_model).stem + ".csv"
+ )
+
+ # Save results probs
+ df_prob_all = df_de.copy()
+ df_prob_all['bert_probability'] = y_prob_all
+ df_prob_all.to_csv(
+ path_repo + "/results/results_eval_BERT/" + Path(path_model).stem + "_bert_probability.csv"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/moderation_classifier/eval_MNB.py b/moderation_classifier/eval_MNB.py
new file mode 100644
index 0000000000000000000000000000000000000000..d510863549480b0c3851407da7d130a2b5b5cf06
--- /dev/null
+++ b/moderation_classifier/eval_MNB.py
@@ -0,0 +1,110 @@
+import click
+from collections import Counter
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from sklearn.metrics import precision_recall_fscore_support
+
+from typing import Union
+import os
+
+from src.MNB_utils import load_model
+from src.preprocessing_text import TextLoader
+from src.train_logs import load_logs
+from src.eval_utils import gen_scores_dict
+
+
+@click.argument("train_logs")
+def main(train_logs: Union[str, os.PathLike]):
+ """
+ Prepares data and evaluates trained MNB model
+ :param train_logs: path to csv-file containing train logs
+ """
+
+ # Load logs
+ (
+ path_repo,
+ path_model,
+ input_data,
+ _,
+ newspaper,
+ lang,
+ topic,
+ remove_duplicates,
+ min_num_words,
+ ) = load_logs(train_logs)
+
+ # Load model
+ pipe = load_model(path_model)
+
+ # Load test data
+ tl = TextLoader(input_data)
+ df_test = tl.load_text_csv(
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ load_subset=False,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ )
+
+ X_test = df_test.text
+ y_test = df_test.label
+
+ # Make prediction
+ y_pred = pipe.predict(X_test)
+
+ # Compute scores and add to dict
+ precision, recall, f1, _ = precision_recall_fscore_support(
+ y_test, y_pred, average="weighted"
+ )
+ accuracy = pipe.score(X_test, y_test)
+
+ results_all = gen_scores_dict(precision, recall, f1, accuracy)
+
+ # Get results per topic
+ count_topics = Counter(df_test["topic"]).most_common(10)
+ topics = [t[0] for t in count_topics]
+ results_t = dict()
+
+ for t in topics:
+ X_test_t = df_test[df_test.topic == t].text
+ y_test_t = df_test[df_test.topic == t].label
+
+ y_pred_t = pipe.predict(X_test_t)
+ precision, recall, f1, _ = precision_recall_fscore_support(
+ y_test_t, y_pred_t, average="weighted"
+ )
+ accuracy = pipe.score(X_test_t, y_test_t)
+
+ results_t[t] = gen_scores_dict(precision, recall, f1, accuracy)
+
+ # Compute rejection rate
+ reject_rate_all = np.round(df_test.label.mean(), 4) * 100
+ reject_rate_topic = [
+ np.round(df_test[df_test.topic == k].label.mean(), 4) * 100 for k in topics
+ ]
+
+ # Compute number comments
+ num_comm_all = df_test.shape[0]
+ num_comm_topic = [df_test[df_test.topic == k].shape[0] for k in topics]
+
+ # Save results
+ df_res_all = pd.DataFrame().from_dict(results_all, orient="index", columns=["all"])
+ df_res_all.loc["rejection rate"] = reject_rate_all
+ df_res_all.loc["number comments"] = num_comm_all
+
+ df_res_topic = pd.DataFrame.from_dict(results_t)
+ df_res_topic.loc["rejection rate"] = reject_rate_topic
+ df_res_topic.loc["number comments"] = num_comm_topic
+
+ df_res = df_res_all.join(df_res_topic)
+ df_res.loc["data"] = [input_data] * df_res.shape[1]
+
+ df_res.to_csv(
+ path_repo + "/results/results_eval_MNB/" + Path(path_model).stem + ".csv"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/moderation_classifier/main.py b/moderation_classifier/main.py
index 449af44b889a55146be5b1653bfe57b156155637..b8a7746bd0fd751f342f44a42059d34f894b3ca9 100644
--- a/moderation_classifier/main.py
+++ b/moderation_classifier/main.py
@@ -1,29 +1,93 @@
# imports
-from pathlib import Path
-
import click
-from src.preprocessing import DataProcessor
+from src.preprocessing_df import DataProcessor
+import moderation_classifier.split_data as split_data
+import moderation_classifier.train_MNB as train_MNB
+import moderation_classifier.train_BERT as train_BERT
+import moderation_classifier.eval_MNB as eval_MNB
+import moderation_classifier.eval_BERT as eval_BERT
+import moderation_classifier.train_BERT_torch as train_BERT_torch
from typing import Union
import os
+
@click.command()
-@click.option('-p', '--prepare_data', is_flag=True)
-@click.argument('input_data')
-def main(prepare_data: bool, input_data: Union[str, os.PathLike]):
+@click.option("-s", "--split", is_flag=True)
+@click.option("-p", "--prepare_data", is_flag=True)
+@click.option("-tp", "--text_preprocessing", is_flag=True)
+@click.option("-n", "--newspaper", default=None)
+@click.option("-t", "--topic", default=None)
+@click.option("-h", "--hsprob", default=None)
+@click.option("-pm", "--pretrained_model", default=None)
+@click.option("-tm", "--train_mnb", is_flag=True)
+@click.option("-tb", "--train_bert", is_flag=True)
+@click.option("-em", "--eval_mnb", is_flag=True)
+@click.option("-eb", "--eval_bert", is_flag=True)
+@click.option("-tbto", "--train_bert_torch", is_flag=True)
+@click.argument("input_data")
+def main(
+ split: bool,
+ prepare_data: bool,
+ text_preprocessing: bool,
+ newspaper: str,
+ topic: str,
+ hsprob: list,
+ pretrained_model: str,
+ train_mnb: bool,
+ train_bert: bool,
+ eval_mnb: bool,
+ eval_bert: bool,
+ train_bert_torch: bool,
+ input_data: Union[str, os.PathLike],
+):
"""
Run moderation classifier.
+ :param split_data: Binary flag to specify if data should be split.
:param prepare_data: Binary flag to specify if data should be prepared.
+ :param text_preprocessing: Binary flag to set text preprocessing.
+ :param newspaper: Name of newspaper selected for training.
+ :param topic: Topic selected for training.
+ :param hsprob: List with min max values for hate speech probability
+ :param pretrained_model: Name of pretrained BERT model to use for finetuning.
+ :param train_mnb: Binary flag to specify whether MNB should be trained.
+ :param train_bert: Binary flag to specify whether BERT should be trained.
+ :param eval_mnb: Binary flag to specify whether MNB should be evaluated.
+ :param eval_bert: Binary flag to specify whether BERT should be evaluated.
:param input_data: Path to input dataframe.
"""
-
+
+ if split:
+ split_data.main(input_data)
+
if prepare_data:
dp = DataProcessor(input_data)
dp.add_language()
print(input_data)
- print('Prepare data')
+ print("Prepare data")
+
+ if train_mnb:
+ train_MNB.main(input_data, newspaper, topic)
+
+ if train_bert:
+ if hsprob is None:
+ pass
+ else:
+ hsprob = eval(hsprob)
+ train_BERT.main(
+ input_data, text_preprocessing, newspaper, topic, hsprob, pretrained_model
+ )
+
+ if eval_mnb:
+ eval_MNB.main(input_data)
+
+ if eval_bert:
+ eval_BERT.main(input_data)
+
+ if train_bert_torch:
+ train_BERT_torch.main(input_data)
if __name__ == "__main__":
- main()
\ No newline at end of file
+ main()
diff --git a/moderation_classifier/predict_BERT.py b/moderation_classifier/predict_BERT.py
new file mode 100644
index 0000000000000000000000000000000000000000..242b874d36e5880b13a4146b339095bb662f8113
--- /dev/null
+++ b/moderation_classifier/predict_BERT.py
@@ -0,0 +1,19 @@
+from datasets import load_dataset
+from evaluate import evaluator
+from transformers import pipeline
+
+data = load_dataset("imdb", split="test").shuffle(seed=42).select(range(10))
+
+task_evaluator = evaluator("text-classification")
+
+pipe = pipeline("text-classification", model="../saved_models/20230630-103946/")
+
+eval_results = task_evaluator.compute(
+ model_or_pipeline=pipe,
+ data=data,
+ label_mapping={"NEGATIVE": 0, "POSITIVE": 1}
+)
+
+import pdb; pdb.set_trace()
+print(eval_results)
+
diff --git a/moderation_classifier/split_data.py b/moderation_classifier/split_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..5adce77edd9f6874f6b7729c2ab36b28ddcf6d7e
--- /dev/null
+++ b/moderation_classifier/split_data.py
@@ -0,0 +1,33 @@
+import os
+import pandas as pd
+from pathlib import Path
+from typing import Union
+
+from sklearn.model_selection import train_test_split
+
+
+def main(input_data: Union[str, os.PathLike]):
+ """
+ Performs train-test split with respect to newspaper count
+ """
+ df = pd.read_csv(input_data)
+
+ df_train, df_test = train_test_split(df, test_size=0.3, stratify=df.originTenantId)
+
+ path_train = (
+ Path(input_data)
+ .parent.joinpath(Path(input_data).stem + "_train")
+ .with_suffix(".csv")
+ )
+ path_test = (
+ Path(input_data)
+ .parent.joinpath(Path(input_data).stem + "_test")
+ .with_suffix(".csv")
+ )
+
+ df_train.to_csv(path_train)
+ df_test.to_csv(path_test)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/moderation_classifier/train_BERT.py b/moderation_classifier/train_BERT.py
new file mode 100644
index 0000000000000000000000000000000000000000..d69708715d93259e362e6570f06f83f544e46d61
--- /dev/null
+++ b/moderation_classifier/train_BERT.py
@@ -0,0 +1,167 @@
+from transformers import AutoTokenizer
+from transformers import DataCollatorWithPadding
+from transformers import TFAutoModelForSequenceClassification
+from transformers.keras_callbacks import KerasMetricCallback
+
+from tensorflow.keras.callbacks import ModelCheckpoint
+from tensorflow.keras.callbacks import TensorBoard
+
+import click
+import datetime
+import os
+import pandas as pd
+from pathlib import Path
+import spacy
+from typing import Union
+
+from src.preprocessing_text import TextLoader, TextProcessor
+from src.prepare_bert_tf import df2dict, compute_metrics, prepare_training
+from src.train_logs import save_logs
+
+
+@click.argument("input_data", required=True)
+@click.argument("text_preprocessing", required=False)
+@click.argument("newspaper", required=False)
+@click.argument("topic", required=False)
+@click.argument("pretrained_model", required=True)
+def main(
+ input_data: Union[str, os.PathLike],
+ text_preprocessing: bool,
+ newspaper: str,
+ topic: str,
+ hsprob: list,
+ pretrained_model: str,
+):
+ """
+ Prepares data and trains BERT model with TF
+ :param input_data: path to input data
+ :param text_preprocessing: Binary flag to set text preprocessing.
+ :param newspaper: Name of newspaper selected for training.
+ :param topic: Topic selected for training.
+ :param hsprob: List with min max values for hate speech probability
+ :param pretrained_model: Name of pretrained BERT model to use for finetuning.
+ """
+
+ def preprocess_function(examples):
+ """
+ Prepares tokenizer for mapping
+ """
+ return tokenizer(examples["text"], truncation=True)
+
+ # Extract path
+ p = Path(input_data)
+ p_repo = p.parent.parent
+
+ # Load data and extract only text from tagesanzeiger
+ print("Load and preprocess text")
+ lang = "de"
+ remove_duplicates = True
+ min_num_words = 3
+ tl = TextLoader(input_data)
+ df_de = tl.load_text_csv(
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ hsprob=hsprob,
+ load_subset=False,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ )
+
+ if text_preprocessing:
+ tp = TextProcessor(lowercase=False)
+ text_proc = tp.fit_transform(df_de.text)
+ df_de.text = text_proc
+ #df_de = df_de.sample(100)
+
+ # Prepare data for modeling
+ ds = df2dict(df_de)
+ # pretrained_model = "bert-base-german-cased"
+ tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
+ tokenized_text = ds.map(preprocess_function)
+ data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
+
+ # Training
+ print("Train model")
+ id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+ label2id = {"NEGATIVE": 0, "POSITIVE": 1}
+
+ optimizer, _ = prepare_training(tokenized_text)
+ model = TFAutoModelForSequenceClassification.from_pretrained(
+ pretrained_model, num_labels=2, id2label=id2label, label2id=label2id
+ )
+
+ tf_train_set = model.prepare_tf_dataset(
+ tokenized_text["train"],
+ shuffle=True,
+ batch_size=16,
+ collate_fn=data_collator,
+ )
+
+ tf_validation_set = model.prepare_tf_dataset(
+ tokenized_text["test"],
+ shuffle=False,
+ batch_size=16,
+ collate_fn=data_collator,
+ )
+
+ model.compile(optimizer=optimizer)
+
+ # Define checkpoint
+ time_stemp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+ path_checkpoint = (p_repo).joinpath("tmp/checkpoint/" + time_stemp)
+ checkpoint_filepath = path_checkpoint
+ metric_callback = KerasMetricCallback(
+ metric_fn=compute_metrics, eval_dataset=tf_validation_set
+ )
+ checkpoint_callback = ModelCheckpoint(
+ checkpoint_filepath,
+ monitor="val_loss",
+ save_best_only=True,
+ save_weights_only=False,
+ mode="min",
+ save_freq="epoch",
+ initial_value_threshold=None,
+ )
+ log_dir = "logs/fit/" + time_stemp
+ tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)
+
+ callbacks = [metric_callback, checkpoint_callback, tensorboard_callback]
+
+ # Fit model
+ print("Train model")
+ model.fit(
+ x=tf_train_set,
+ validation_data=tf_validation_set,
+ epochs=5,
+ verbose=2,
+ callbacks=callbacks,
+ )
+
+ # Save model
+ print("Save model")
+ path_model = (p_repo).joinpath("saved_models/" + time_stemp)
+ model.save_pretrained(path_model)
+ tokenizer.save_pretrained(path_model)
+
+ # Save model logs
+ save_logs(
+ path_repo=p_repo,
+ path_model=path_model,
+ input_data=input_data,
+ text_preprocessing=True,
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ hsprob=hsprob,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ model_name="BERT",
+ pretrained_model=pretrained_model,
+ )
+
+ print("Done")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/moderation_classifier/train_BERT_torch.py b/moderation_classifier/train_BERT_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..5be8e6997533ceb8f8ba69b187f37dddd8bd525b
--- /dev/null
+++ b/moderation_classifier/train_BERT_torch.py
@@ -0,0 +1,135 @@
+from datasets import Dataset, DatasetDict
+
+import evaluate
+from transformers import AutoTokenizer
+from transformers import DataCollatorWithPadding
+
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+import numpy as np
+
+import pandas as pd
+
+from typing import Union
+import os
+
+import click
+
+from sklearn.model_selection import train_test_split
+
+from src.preprocessing_text import TextLoader
+
+
+def load_text(
+ path: Union[str, os.PathLike], newspaper: str = "tagesanzeiger", lang: str = "de"
+) -> pd.DataFrame:
+ """
+ Loads daraframe and extracts text depending on newspaper and langugae
+ """
+ df = pd.read_csv(path)
+ df = df.loc[(df.originTenantId == newspaper) & (df.language == lang)]
+ df = df[["text", "rejected"]]
+ df = df.rename(columns={"rejected": "label"})
+
+ return df
+
+
+def df2dict(df: pd.DataFrame):
+ """
+ Converts Dataframe into Huggingface Dataset
+ """
+
+ df = df.sample(10000)
+ train, test = train_test_split(df, test_size=0.2)
+
+ ds_train = Dataset.from_pandas(train)
+ ds_test = Dataset.from_pandas(test)
+
+ ds = DatasetDict()
+ ds["train"] = ds_train
+ ds["test"] = ds_test
+
+ return ds
+
+
+def compute_metrics(eval_pred):
+ accuracy = evaluate.load("accuracy")
+ predictions, labels = eval_pred
+ predictions = np.argmax(predictions, axis=1)
+ return accuracy.compute(predictions=predictions, references=labels)
+
+
+def prepare_training(dataset, batch_size: int = 16, num_epochs: int = 5):
+ """
+ Prepares training and sets params
+ """
+
+ batches_per_epoch = len(dataset["train"]) // batch_size
+ total_train_steps = int(batches_per_epoch * num_epochs)
+ optimizer, schedule = create_optimizer(
+ init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps
+ )
+
+ return optimizer, schedule
+
+
+@click.argument("input_data")
+def main(input_data: Union[str, os.PathLike]):
+ # load data and extract only german text from tagesanzeiger
+ print("Load text")
+ tl = TextLoader(input_data)
+ df_de = tl.load_text_csv(newspaper="tagesanzeiger", load_subset=True)
+
+ # Dataframe to dict/Train-test split
+ ds = df2dict(df_de)
+
+ # Preprocessing/Tokenization
+ print("tokenize")
+ tokenizer = AutoTokenizer.from_pretrained("bert-base-german-cased")
+
+ def preprocess_function(examples):
+ return tokenizer(examples["text"], truncation=True)
+
+ # truncate sequences to be no longer than the models maximum input length
+ print("map")
+ tokenized_text = ds.map(preprocess_function)
+
+ # dynamically padding of sentences to the longest length in a batch
+ # data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
+ data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+ # Training
+ id2label = {0: "NEGATIVE", 1: "POSITIVE"}
+ label2id = {"NEGATIVE": 0, "POSITIVE": 1}
+
+ model = AutoModelForSequenceClassification.from_pretrained(
+ "bert-base-german-cased", num_labels=2, id2label=id2label, label2id=label2id
+ )
+
+ training_args = TrainingArguments(
+ output_dir="my_awesome_model",
+ learning_rate=2e-5,
+ per_device_train_batch_size=16,
+ per_device_eval_batch_size=16,
+ num_train_epochs=2,
+ weight_decay=0.01,
+ evaluation_strategy="epoch",
+ save_strategy="epoch",
+ load_best_model_at_end=True,
+ push_to_hub=False,
+ )
+
+ trainer = Trainer(
+ model=model,
+ args=training_args,
+ train_dataset=tokenized_text["train"],
+ eval_dataset=tokenized_text["test"],
+ tokenizer=tokenizer,
+ data_collator=data_collator,
+ compute_metrics=compute_metrics,
+ )
+
+ trainer.train()
+
+if __name__ == "__main__":
+ main()
diff --git a/moderation_classifier/train_MNB.py b/moderation_classifier/train_MNB.py
new file mode 100644
index 0000000000000000000000000000000000000000..44bddadbdd1aeddb3cc57d66d25e7ab18d1eddac
--- /dev/null
+++ b/moderation_classifier/train_MNB.py
@@ -0,0 +1,72 @@
+from sklearn.model_selection import train_test_split
+
+import click
+from pathlib import Path
+from typing import Union
+import os
+
+from src.MNB_utils import create_pipeline, create_path, save_model
+from src.preprocessing_text import TextLoader
+from src.train_logs import save_logs
+
+
+@click.argument("input_data")
+@click.argument("newspaper")
+@click.argument("topic")
+def main(input_data: Union[str, os.PathLike], newspaper: str, topic: str):
+ """
+ Runs training of MNB.
+ :param input_data: Path to input dataframe.
+ """
+
+ # Extract path
+ p = Path(input_data)
+ p_repo = p.parent.parent
+
+ # Load data and extract only text from tagesanzeiger
+ print("Load and preprocess text")
+ lang = "de"
+ remove_duplicates = True
+ min_num_words = 3
+ tl = TextLoader(input_data)
+ df_de = tl.load_text_csv(
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ load_subset=False,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ )
+
+ # Prepare data for modeling
+ text = df_de.text
+ label = df_de.label
+
+ X_train, X_val, y_train, y_val = train_test_split(text, label, stratify=label)
+
+ # Training
+ print("Train model")
+ pipe = create_pipeline()
+ pipe.fit(X_train, y_train)
+ val_score = pipe.score(X_val, y_val)
+
+ # Save model and training logs
+ path = create_path()
+ save_model(pipe, path)
+ save_logs(
+ path_repo=p_repo,
+ path_model=path,
+ input_data=input_data,
+ text_preprocessing=True,
+ newspaper=newspaper,
+ lang=lang,
+ topic=topic,
+ remove_duplicates=remove_duplicates,
+ min_num_words=min_num_words,
+ model_name="MNB",
+ val_score=val_score,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/notebooks/data-exploration.ipynb b/notebooks/data-exploration.ipynb
index f209771b25cb8884dc4407d569b90cfbe6fbc77c..cbf67ec62b6fe302c85b0a3eba015381e1cd3d38 100644
--- a/notebooks/data-exploration.ipynb
+++ b/notebooks/data-exploration.ipynb
@@ -38,12 +38,29 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 11,
"id": "6efcb560",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '../data/tamedia_for_classifier_v4_preproc.csv'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m../data/tamedia_for_classifier_v4_preproc.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:912\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 899\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 900\u001b[0m dialect,\n\u001b[1;32m 901\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 908\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m 909\u001b[0m )\n\u001b[1;32m 910\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 912\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:577\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 574\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 576\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 577\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 580\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1407\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1404\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1406\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1407\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1661\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1659\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m 1660\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1662\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1663\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1664\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1665\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1666\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1667\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1668\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1669\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1670\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1671\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1672\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/io/common.py:859\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 855\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 858\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 859\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 867\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 868\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../data/tamedia_for_classifier_v4_preproc.csv'"
+ ]
+ }
+ ],
"source": [
- "df = pd.read_csv('../data/tamedia_for_classifier_v2_preproc.csv')"
+ "df = pd.read_csv('../data/tamedia_for_classifier_v4_preproc.csv')"
]
},
{
@@ -73,7 +90,6 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>Unnamed: 0</th>\n",
" <th>ID</th>\n",
" <th>createdAt</th>\n",
" <th>text</th>\n",
@@ -81,13 +97,14 @@
" <th>state</th>\n",
" <th>originTenantId</th>\n",
" <th>replyTo</th>\n",
- " <th>language</th>\n",
+ " <th>asset.risk</th>\n",
+ " <th>topic</th>\n",
+ " <th>hsprob</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>0</td>\n",
" <td>5fee66486ef49d0033d97e4c</td>\n",
" <td>2021-01-01T00:01:12Z</td>\n",
" <td>Hat schon welche, möchte aber lieber nicht erw...</td>\n",
@@ -95,11 +112,12 @@
" <td>rejected</td>\n",
" <td>tagesanzeiger</td>\n",
" <td>5f537bbdd2abdd0032ec12ad</td>\n",
- " <td>de</td>\n",
+ " <td>high</td>\n",
+ " <td>Zürich</td>\n",
+ " <td>0.051257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>1</td>\n",
" <td>5fee66b7e9b26b00322cc53e</td>\n",
" <td>2021-01-01T00:03:03Z</td>\n",
" <td>Wieso nicht? Absolut kein Argument.</td>\n",
@@ -107,11 +125,12 @@
" <td>rejected</td>\n",
" <td>tagesanzeiger</td>\n",
" <td>NaN</td>\n",
- " <td>de</td>\n",
+ " <td>high</td>\n",
+ " <td>Bundeshaus</td>\n",
+ " <td>0.012496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>2</td>\n",
" <td>5fee66bfe9b26b00322cc543</td>\n",
" <td>2021-01-01T00:03:11Z</td>\n",
" <td>Eine Impfung kostet vergleichsweise wenig. Und...</td>\n",
@@ -119,11 +138,12 @@
" <td>approved</td>\n",
" <td>derbund</td>\n",
" <td>5fee4bccb3aa6d0032c3c1f0</td>\n",
- " <td>de</td>\n",
+ " <td>high</td>\n",
+ " <td>Bundeshaus</td>\n",
+ " <td>0.027282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>3</td>\n",
" <td>5fee66dca0dd250033ef02ea</td>\n",
" <td>2021-01-01T00:03:40Z</td>\n",
" <td>Sind Sie einfach nur etwas einfach oder hochgr...</td>\n",
@@ -131,11 +151,12 @@
" <td>approved</td>\n",
" <td>tagesanzeiger</td>\n",
" <td>5fee1998e9b26b00322caaad</td>\n",
- " <td>de</td>\n",
+ " <td>low</td>\n",
+ " <td>Meinungen</td>\n",
+ " <td>0.020309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>4</td>\n",
" <td>5fee66ec6ef49d0033d97e7e</td>\n",
" <td>2021-01-01T00:03:56Z</td>\n",
" <td>Hä??? Von welchem Paralleluniversum ist hier m...</td>\n",
@@ -143,19 +164,21 @@
" <td>rejected</td>\n",
" <td>tagesanzeiger</td>\n",
" <td>5fedfcbdf31d260033d38738</td>\n",
- " <td>de</td>\n",
+ " <td>low</td>\n",
+ " <td>Schweiz</td>\n",
+ " <td>0.018285</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
- " Unnamed: 0 ID createdAt \n",
- "0 0 5fee66486ef49d0033d97e4c 2021-01-01T00:01:12Z \\\n",
- "1 1 5fee66b7e9b26b00322cc53e 2021-01-01T00:03:03Z \n",
- "2 2 5fee66bfe9b26b00322cc543 2021-01-01T00:03:11Z \n",
- "3 3 5fee66dca0dd250033ef02ea 2021-01-01T00:03:40Z \n",
- "4 4 5fee66ec6ef49d0033d97e7e 2021-01-01T00:03:56Z \n",
+ " ID createdAt \n",
+ "0 5fee66486ef49d0033d97e4c 2021-01-01T00:01:12Z \\\n",
+ "1 5fee66b7e9b26b00322cc53e 2021-01-01T00:03:03Z \n",
+ "2 5fee66bfe9b26b00322cc543 2021-01-01T00:03:11Z \n",
+ "3 5fee66dca0dd250033ef02ea 2021-01-01T00:03:40Z \n",
+ "4 5fee66ec6ef49d0033d97e7e 2021-01-01T00:03:56Z \n",
"\n",
" text rejected state \n",
"0 Hat schon welche, möchte aber lieber nicht erw... 1 rejected \\\n",
@@ -164,12 +187,12 @@
"3 Sind Sie einfach nur etwas einfach oder hochgr... 0 approved \n",
"4 Hä??? Von welchem Paralleluniversum ist hier m... 1 rejected \n",
"\n",
- " originTenantId replyTo language \n",
- "0 tagesanzeiger 5f537bbdd2abdd0032ec12ad de \n",
- "1 tagesanzeiger NaN de \n",
- "2 derbund 5fee4bccb3aa6d0032c3c1f0 de \n",
- "3 tagesanzeiger 5fee1998e9b26b00322caaad de \n",
- "4 tagesanzeiger 5fedfcbdf31d260033d38738 de "
+ " originTenantId replyTo asset.risk topic hsprob \n",
+ "0 tagesanzeiger 5f537bbdd2abdd0032ec12ad high Zürich 0.051257 \n",
+ "1 tagesanzeiger NaN high Bundeshaus 0.012496 \n",
+ "2 derbund 5fee4bccb3aa6d0032c3c1f0 high Bundeshaus 0.027282 \n",
+ "3 tagesanzeiger 5fee1998e9b26b00322caaad low Meinungen 0.020309 \n",
+ "4 tagesanzeiger 5fedfcbdf31d260033d38738 low Schweiz 0.018285 "
]
},
"execution_count": 4,
@@ -204,18 +227,29 @@
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "<Axes: xlabel='language', ylabel='Count'>"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "KeyError",
+ "evalue": "'language'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3651\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'language'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[5], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m sns\u001b[38;5;241m.\u001b[39mhistplot(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlanguage\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3763\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+ "File \u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3655\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3656\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3657\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'language'"
+ ]
},
{
"data": {
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",
+ "image/png": "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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
@@ -239,24 +273,20 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "3bbbe6f6",
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Rejected comments:\n",
- "ganz im Gegensatz zu den Mit\"arbeitern\" im Steueramt\n",
- "\n",
- "\n",
- "Sie armer Mensch. Immer müssen Sie sich beklagen, weil Ihnen die Welt so furchtbar ungerecht erscheint.\n",
- "\n",
- "\n",
- "Es ist interessant wie es funktioniert: Patrizia Dänzi vom IKRK wird Chefin der Deza im EDA. Frau Spojlaric Egger vom EDA wird IKRK Chefin. MitarbeiterInnen scheinen bessere Opportunitäten ausserhalb Ihrer Karriereinstitution zu bekommen. Geht da Know-How nicht verloren?\n",
- "\n",
- "\n"
+ "ename": "AttributeError",
+ "evalue": "'DataFrame' object has no attribute 'language'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/bn/hrm9f3gs76z5zb1bxxc4g_s00000gn/T/ipykernel_92361/1030998729.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_rejected_de\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguage\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'de'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrejected\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf_accepted_de\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguage\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'de'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrejected\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Rejected comments:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5985\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5986\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5987\u001b[0m ):\n\u001b[1;32m 5988\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5989\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'language'"
]
}
],
@@ -272,7 +302,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "173770c7",
"metadata": {},
"outputs": [
@@ -280,19 +310,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Accepted comments:\n",
- "In Art. 23e Abs.2 PTM kommt der Ausdruck „Leib und Leben“ nicht vor, sondern es wird von der „Verbreitung von Furcht und Schrecken“ gesprochen, ohne näher zu definieren, durch welche Aktionen diese Art von Furcht und Schrecken verbreitet wird und nach welchen Kriterien festgestellt wird, ob jemand in Furcht und Schrecken versetzt worden ist. Damit ist der Deliktekatalog, der zu Furcht und Schrecken im Sinn des PTM führt und als terroristische Aktivität gilt, nicht abschliessend definiert. Es ist damit unklar, welche Verhaltensarten dazu führen, als terroristisch aktive Person eingestuft zu werden. Das ist nach meinem Verständnis der Vorbehalt, den Nils Melzer anbringt, der angesichts seines CV nicht als Leichtgewichtjurist eingestuft werden kann.\n",
- "Was strafbare Handlungen gegen Leib und Leben sind, ist in Art. 111 bis 136 StGB definiert und es braucht kein PTM, die Verfolgung solcher Vergehen zu ermöglichen.\n",
- "Das PTM ist meines Erachtens ein Gesetz für präventive Massnahmen, die basierend auf den geltenden Gesetzen nicht eingeleitet werden dürfen.\n",
- "Angesichts dieses Sachverhaltes ist Ihre Darstellung, freundlich ausgedrückt, laienhaft.\n",
- "\n",
- "\n",
- "Ach ja. Und wie meinen Sie, Frau Baus, ist die Menschheit die fürchterliche Krankheit Pocken los geworden? Genau, mit Impfzwang. Die obligatorische Pockenimpfung wurde z.B. in Deutschland Mitte der 70er Jahre abgeschafft, in Österreich Anfang der 80er. Auch Polio, Diphterie und viele üble Infektionskrankheiten mehr wurden dank der Impfung von fast der ganzen Bevölkerung richtiggehend ausgerottet. Nur weil die Impfungen in der Vergangenheit so verdammt erfolgreich waren, müssen wir diese schreckliche Krankheiten nicht mehr erleben. Aber eben, wir vergessen leider (zu) schnell.\n",
- "\n",
- "\n",
- "Abstand halten, Maske tragen und Hände desinfizieren hilft auch gegen Grippeviren. Deshalb nichts als logisch, gibt es aktuell keine Grippewelle.\n",
- "\n",
- "\n"
+ "Accepted comments:\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'df_accepted_de' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAccepted comments:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdf_accepted_de\u001b[49m\u001b[38;5;241m.\u001b[39mtext\u001b[38;5;241m.\u001b[39msample(\u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(c)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'df_accepted_de' is not defined"
]
}
],
@@ -313,7 +342,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "078ad528",
"metadata": {},
"outputs": [],
@@ -323,19 +352,20 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "484f790d",
"metadata": {},
"outputs": [
{
- "data": {
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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "NameError",
+ "evalue": "name 'df_accepted_de' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Create and generate a word cloud image:\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m text_de_accepted \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[43mdf_accepted_de\u001b[49m\u001b[38;5;241m.\u001b[39mtext)\n\u001b[1;32m 3\u001b[0m wordcloud \u001b[38;5;241m=\u001b[39m WordCloud(stopwords\u001b[38;5;241m=\u001b[39mgerman_stop_words)\u001b[38;5;241m.\u001b[39mgenerate(text_de_accepted)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Display the generated image:\u001b[39;00m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'df_accepted_de' is not defined"
+ ]
}
],
"source": [
@@ -351,21 +381,10 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "5cc400e0",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Create and generate a word cloud image:\n",
"text_de_rejected = ' '.join(df_rejected_de.text)\n",
@@ -387,7 +406,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "58e91383",
"metadata": {},
"outputs": [],
@@ -398,31 +417,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "00cc3793",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Text(0.5, 1.0, 'accepted'), (0.0, 400.0)]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1000x500 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(10,5))\n",
"fig.suptitle('Number of words per sentence')\n",
@@ -440,29 +438,21 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 6,
"id": "974e3104",
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "[Text(0.5, 1.0, 'German comments')]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "ename": "AttributeError",
+ "evalue": "'DataFrame' object has no attribute 'language'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/var/folders/bn/hrm9f3gs76z5zb1bxxc4g_s00000gn/T/ipykernel_92361/38630575.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguage\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'de'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrejected\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'German comments'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m~/Documents/public_policy/pp_env/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5985\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5986\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5987\u001b[0m ):\n\u001b[1;32m 5988\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5989\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'language'"
+ ]
}
],
"source": [
diff --git a/notebooks/hate-speech-score.ipynb b/notebooks/hate-speech-score.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..53b0308aa57b60992fd03f19421d57cd6f33f423
--- /dev/null
+++ b/notebooks/hate-speech-score.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2f0ca1cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "\n",
+ "from datetime import date\n",
+ "from wordcloud import WordCloud \n",
+ "\n",
+ "from nltk.corpus import stopwords\n",
+ "\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "e3567f43",
+ "metadata": {},
+ "outputs": [
+ {
+ "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>ID</th>\n",
+ " <th>createdAt</th>\n",
+ " <th>text</th>\n",
+ " <th>rejected</th>\n",
+ " <th>state</th>\n",
+ " <th>originTenantId</th>\n",
+ " <th>replyTo</th>\n",
+ " <th>asset.risk</th>\n",
+ " <th>topic</th>\n",
+ " <th>hsprob</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>5fee66486ef49d0033d97e4c</td>\n",
+ " <td>2021-01-01T00:01:12Z</td>\n",
+ " <td>Hat schon welche, möchte aber lieber nicht erw...</td>\n",
+ " <td>1</td>\n",
+ " <td>rejected</td>\n",
+ " <td>tagesanzeiger</td>\n",
+ " <td>5f537bbdd2abdd0032ec12ad</td>\n",
+ " <td>high</td>\n",
+ " <td>Zürich</td>\n",
+ " <td>0.051257</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5fee66b7e9b26b00322cc53e</td>\n",
+ " <td>2021-01-01T00:03:03Z</td>\n",
+ " <td>Wieso nicht? Absolut kein Argument.</td>\n",
+ " <td>1</td>\n",
+ " <td>rejected</td>\n",
+ " <td>tagesanzeiger</td>\n",
+ " <td>NaN</td>\n",
+ " <td>high</td>\n",
+ " <td>Bundeshaus</td>\n",
+ " <td>0.012496</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>5fee66bfe9b26b00322cc543</td>\n",
+ " <td>2021-01-01T00:03:11Z</td>\n",
+ " <td>Eine Impfung kostet vergleichsweise wenig. Und...</td>\n",
+ " <td>0</td>\n",
+ " <td>approved</td>\n",
+ " <td>derbund</td>\n",
+ " <td>5fee4bccb3aa6d0032c3c1f0</td>\n",
+ " <td>high</td>\n",
+ " <td>Bundeshaus</td>\n",
+ " <td>0.027282</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>5fee66dca0dd250033ef02ea</td>\n",
+ " <td>2021-01-01T00:03:40Z</td>\n",
+ " <td>Sind Sie einfach nur etwas einfach oder hochgr...</td>\n",
+ " <td>0</td>\n",
+ " <td>approved</td>\n",
+ " <td>tagesanzeiger</td>\n",
+ " <td>5fee1998e9b26b00322caaad</td>\n",
+ " <td>low</td>\n",
+ " <td>Meinungen</td>\n",
+ " <td>0.020309</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5fee66ec6ef49d0033d97e7e</td>\n",
+ " <td>2021-01-01T00:03:56Z</td>\n",
+ " <td>Hä??? Von welchem Paralleluniversum ist hier m...</td>\n",
+ " <td>1</td>\n",
+ " <td>rejected</td>\n",
+ " <td>tagesanzeiger</td>\n",
+ " <td>5fedfcbdf31d260033d38738</td>\n",
+ " <td>low</td>\n",
+ " <td>Schweiz</td>\n",
+ " <td>0.018285</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID createdAt \n",
+ "0 5fee66486ef49d0033d97e4c 2021-01-01T00:01:12Z \\\n",
+ "1 5fee66b7e9b26b00322cc53e 2021-01-01T00:03:03Z \n",
+ "2 5fee66bfe9b26b00322cc543 2021-01-01T00:03:11Z \n",
+ "3 5fee66dca0dd250033ef02ea 2021-01-01T00:03:40Z \n",
+ "4 5fee66ec6ef49d0033d97e7e 2021-01-01T00:03:56Z \n",
+ "\n",
+ " text rejected state \n",
+ "0 Hat schon welche, möchte aber lieber nicht erw... 1 rejected \\\n",
+ "1 Wieso nicht? Absolut kein Argument. 1 rejected \n",
+ "2 Eine Impfung kostet vergleichsweise wenig. Und... 0 approved \n",
+ "3 Sind Sie einfach nur etwas einfach oder hochgr... 0 approved \n",
+ "4 Hä??? Von welchem Paralleluniversum ist hier m... 1 rejected \n",
+ "\n",
+ " originTenantId replyTo asset.risk topic hsprob \n",
+ "0 tagesanzeiger 5f537bbdd2abdd0032ec12ad high Zürich 0.051257 \n",
+ "1 tagesanzeiger NaN high Bundeshaus 0.012496 \n",
+ "2 derbund 5fee4bccb3aa6d0032c3c1f0 high Bundeshaus 0.027282 \n",
+ "3 tagesanzeiger 5fee1998e9b26b00322caaad low Meinungen 0.020309 \n",
+ "4 tagesanzeiger 5fedfcbdf31d260033d38738 low Schweiz 0.018285 "
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load data\n",
+ "df = pd.read_csv('../data/tamedia_for_classifier_v4.csv')\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "ee505e09",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='Frequency'>"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Distribution of probabilities\n",
+ "df.hsprob.plot.hist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "b6ae7880",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Number of rejected and eccepted comments for high hsprob\n",
+ "df[(df.hsprob>0.7)&(df.originTenantId=='tagesanzeiger')].state.hist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "4b775b9b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: >"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Number of rejected and eccepted comments for low hsprob\n",
+ "df[(df.hsprob<0.3)&(df.originTenantId=='tagesanzeiger')].state.hist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d019c140",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "pp_env",
+ "language": "python",
+ "name": "pp_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/text-processing.ipynb b/notebooks/text-processing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a27fec0845255d75c0a581e11a0dfc1e6c44a0dc
--- /dev/null
+++ b/notebooks/text-processing.ipynb
@@ -0,0 +1,272 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a7eee72f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from src.preprocessing_text import TextLoader, TextProcessor\n",
+ "import spacy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1cad5dc2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_data = '/Users/franziskaoschmann/Documents/public_policy/data/tamedia_for_classifier_v2_preproc.csv'\n",
+ "\n",
+ "tl = TextLoader(input_data)\n",
+ "\n",
+ "df_de = tl.load_text_csv(newspaper = 'tagesanzeiger', lang ='de')\n",
+ "\n",
+ "nlp = spacy.load('de_core_news_sm')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "71ddb0ba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 566, 1061, 1381, 1445, 1496, 1589, 1723, 1934, 2221, 2641, 2947,\n",
+ " 3628, 3899, 4007, 4280, 4650, 4852, 5202, 5656, 5770, 5985, 6260,\n",
+ " 7141, 7204, 7804, 7972, 8005, 8261, 8504, 8846, 8857]),)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "np.where(['@' in t for t in df_de.text[:10000]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "28887dee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sample_text = df_de.iloc[566].text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "163a698c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Die Übersterblichkeit wird aufgrund der Todesfallzahlen des 7-Tage-Durchschnitts und nicht eines Jahresdurchschnitts berechnet. So sind übrigens auch saisonale Unterschiede berücksichtigt.\\nUnd nein @John Zürcher, es gab keine statistisch relevante Untersterblichkeit im Sommer, sondern nur eine, die sich am unteren Band der durchschnittlichen Sterblichkeit der letzten 5 Jahre bewegt hat. Den Grund dafür habe ich erwähnt.\\n@Martin Mader: was Sie schreiben ist Unsinn. Die durchschnittliche Sterblichkeit wird anhand der tatsächlichen Todesfälle über die letzten 5 Jahre festgehalten. Da sind alle massgebenden Parameter automatisch enthalten. Logisch, dass dieser so berechnete Durchschnitt sich jährlich aufgrund der Lebenserwartung verändert.\\n@Mark Keller: Ich habe nie etwas anderes behauptet. Man kann sogar soweit gehen, nachträglich die durchschnittliche Lebenszeit zu berechnen, die einem an dieser Pandemie verstorbenen Menschen genommen wurde. Sobald sich die Todesfallzahlen nach einer Phase der Übersterblichkeit durch eine unterdurchschnittliche Sterblichkeit oder durch eine Untersterblichkeit ausgeglichen haben, erhält man diesen Wert (ich gehe von rund 6 Monaten aus). Da es aber nur ein Durchschnittswert ist, sagt er nicht über die Lebenszeit aus, die einer an Corona verstorbenen Person wirklich weggenommen wurde. Bei den einen mögen es ein paar Tage oder Wochen sein, bei anderen aber mehrere Jahre oder gar Jahrzehnte.'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sample_text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3babd6fc",
+ "metadata": {},
+ "source": [
+ "### Remove spaces"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "d114f6a2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Die Übersterblichkeit wird aufgrund der Todesfallzahlen des 7-Tage-Durchschnitts und nicht eines Jahresdurchschnitts berechnet. So sind übrigens auch saisonale Unterschiede berücksichtigt. Und nein @John Zürcher, es gab keine statistisch relevante Untersterblichkeit im Sommer, sondern nur eine, die sich am unteren Band der durchschnittlichen Sterblichkeit der letzten 5 Jahre bewegt hat. Den Grund dafür habe ich erwähnt. @Martin Mader: was Sie schreiben ist Unsinn. Die durchschnittliche Sterblichkeit wird anhand der tatsächlichen Todesfälle über die letzten 5 Jahre festgehalten. Da sind alle massgebenden Parameter automatisch enthalten. Logisch, dass dieser so berechnete Durchschnitt sich jährlich aufgrund der Lebenserwartung verändert. @Mark Keller: Ich habe nie etwas anderes behauptet. Man kann sogar soweit gehen, nachträglich die durchschnittliche Lebenszeit zu berechnen, die einem an dieser Pandemie verstorbenen Menschen genommen wurde. Sobald sich die Todesfallzahlen nach einer Phase der Übersterblichkeit durch eine unterdurchschnittliche Sterblichkeit oder durch eine Untersterblichkeit ausgeglichen haben, erhält man diesen Wert (ich gehe von rund 6 Monaten aus). Da es aber nur ein Durchschnittswert ist, sagt er nicht über die Lebenszeit aus, die einer an Corona verstorbenen Person wirklich weggenommen wurde. Bei den einen mögen es ein paar Tage oder Wochen sein, bei anderen aber mehrere Jahre oder gar Jahrzehnte.'"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tp = TextProcessor(nlp)\n",
+ "\n",
+ "text_proc = tp.remove_spaces(sample_text)\n",
+ "\n",
+ "text_proc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bfc38df2",
+ "metadata": {},
+ "source": [
+ "### Remove punctuation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "216b53db",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Die Übersterblichkeit wird aufgrund der Todesfallzahlen des 7TageDurchschnitts und nicht eines Jahresdurchschnitts berechnet So sind übrigens auch saisonale Unterschiede berücksichtigt Und nein @John Zürcher es gab keine statistisch relevante Untersterblichkeit im Sommer sondern nur eine die sich am unteren Band der durchschnittlichen Sterblichkeit der letzten 5 Jahre bewegt hat Den Grund dafür habe ich erwähnt @Martin Mader was Sie schreiben ist Unsinn Die durchschnittliche Sterblichkeit wird anhand der tatsächlichen Todesfälle über die letzten 5 Jahre festgehalten Da sind alle massgebenden Parameter automatisch enthalten Logisch dass dieser so berechnete Durchschnitt sich jährlich aufgrund der Lebenserwartung verändert @Mark Keller Ich habe nie etwas anderes behauptet Man kann sogar soweit gehen nachträglich die durchschnittliche Lebenszeit zu berechnen die einem an dieser Pandemie verstorbenen Menschen genommen wurde Sobald sich die Todesfallzahlen nach einer Phase der Übersterblichkeit durch eine unterdurchschnittliche Sterblichkeit oder durch eine Untersterblichkeit ausgeglichen haben erhält man diesen Wert ich gehe von rund 6 Monaten aus Da es aber nur ein Durchschnittswert ist sagt er nicht über die Lebenszeit aus die einer an Corona verstorbenen Person wirklich weggenommen wurde Bei den einen mögen es ein paar Tage oder Wochen sein bei anderen aber mehrere Jahre oder gar Jahrzehnte'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_proc = tp.remove_punctuation(text_proc)\n",
+ "\n",
+ "text_proc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0df7f111",
+ "metadata": {},
+ "source": [
+ "### Remove @-mentions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "67edcb18",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Die Übersterblichkeit wird aufgrund der Todesfallzahlen des 7TageDurchschnitts und nicht eines Jahresdurchschnitts berechnet So sind übrigens auch saisonale Unterschiede berücksichtigt Und nein Zürcher es gab keine statistisch relevante Untersterblichkeit im Sommer sondern nur eine die sich am unteren Band der durchschnittlichen Sterblichkeit der letzten 5 Jahre bewegt hat Den Grund dafür habe ich erwähnt Mader was Sie schreiben ist Unsinn Die durchschnittliche Sterblichkeit wird anhand der tatsächlichen Todesfälle über die letzten 5 Jahre festgehalten Da sind alle massgebenden Parameter automatisch enthalten Logisch dass dieser so berechnete Durchschnitt sich jährlich aufgrund der Lebenserwartung verändert Keller Ich habe nie etwas anderes behauptet Man kann sogar soweit gehen nachträglich die durchschnittliche Lebenszeit zu berechnen die einem an dieser Pandemie verstorbenen Menschen genommen wurde Sobald sich die Todesfallzahlen nach einer Phase der Übersterblichkeit durch eine unterdurchschnittliche Sterblichkeit oder durch eine Untersterblichkeit ausgeglichen haben erhält man diesen Wert ich gehe von rund 6 Monaten aus Da es aber nur ein Durchschnittswert ist sagt er nicht über die Lebenszeit aus die einer an Corona verstorbenen Person wirklich weggenommen wurde Bei den einen mögen es ein paar Tage oder Wochen sein bei anderen aber mehrere Jahre oder gar Jahrzehnte'"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_proc = tp.remove_mentions(text_proc)\n",
+ "\n",
+ "text_proc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6783795b",
+ "metadata": {},
+ "source": [
+ "### Lemmatization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "2421eeb4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'der Übersterblichkeit werden aufgrund der Todesfallzahle der 7TageDurchschnitts und nicht ein Jahresdurchschnitt berechnen so sein übrigens auch saisonal Unterschied berücksichtigen und nein Zürcher es geben kein statistisch relevant Untersterblichkeit in Sommer sondern nur einer der sich an unterer Band der durchschnittlich Sterblichkeit der letzter 5 Jahr bewegen haben der Grund dafür haben ich erwähnen Mader was sie schreiben sein Unsinn der durchschnittlich Sterblichkeit werden anhand der tatsächlich Todesfall über der letzter 5 Jahr festhalten da sein aller massgebend Parameter automatisch enthalt Logisch dass dieser so berechnen Durchschnitt sich jährlich aufgrund der Lebenserwartung verändern Keller ich haben nie etwas anderer behaupten man können sogar soweit gehen nachträglich der durchschnittlich Lebenszeit zu berechnen der ein an dieser Pandemie verstorben Mensch nehmen werden Sobald sich der Todesfallzahle nach ein Phase der Übersterblichkeit durch ein unterdurchschnittlich Sterblichkeit oder durch ein Untersterblichkeit ausgleichen haben erhalten man dieser Wert ich gehen von rund 6 Monat aus da es aber nur ein Durchschnittswert sein sagen er nicht über der Lebenszeit aus der ein an Corona verstorben Person wirklich wegnommen werden bei der einer mögen es ein paar Tag oder Woche sein bei anderer aber mehrere Jahr oder gar Jahrzehnt'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_proc = tp.lemmatize_text(text_proc)\n",
+ "\n",
+ "text_proc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ebfb311",
+ "metadata": {},
+ "source": [
+ "### Lowercase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d7414b09",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'der übersterblichkeit werden aufgrund der todesfallzahle der 7tagedurchschnitts und nicht ein jahresdurchschnitt berechnen so sein übrigens auch saisonal unterschied berücksichtigen und nein zürcher es geben kein statistisch relevant untersterblichkeit in sommer sondern nur einer der sich an unterer band der durchschnittlich sterblichkeit der letzter 5 jahr bewegen haben der grund dafür haben ich erwähnen mader was sie schreiben sein unsinn der durchschnittlich sterblichkeit werden anhand der tatsächlich todesfall über der letzter 5 jahr festhalten da sein aller massgebend parameter automatisch enthalt logisch dass dieser so berechnen durchschnitt sich jährlich aufgrund der lebenserwartung verändern keller ich haben nie etwas anderer behaupten man können sogar soweit gehen nachträglich der durchschnittlich lebenszeit zu berechnen der ein an dieser pandemie verstorben mensch nehmen werden sobald sich der todesfallzahle nach ein phase der übersterblichkeit durch ein unterdurchschnittlich sterblichkeit oder durch ein untersterblichkeit ausgleichen haben erhalten man dieser wert ich gehen von rund 6 monat aus da es aber nur ein durchschnittswert sein sagen er nicht über der lebenszeit aus der ein an corona verstorben person wirklich wegnommen werden bei der einer mögen es ein paar tag oder woche sein bei anderer aber mehrere jahr oder gar jahrzehnt'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text_proc = tp.fold_case(text_proc)\n",
+ "\n",
+ "text_proc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9c3cfee",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "pp_env",
+ "language": "python",
+ "name": "pp_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/visualize-embeddings.ipynb b/notebooks/visualize-embeddings.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9592e974af63f2f07a9ef9cbdd83372516fed408
--- /dev/null
+++ b/notebooks/visualize-embeddings.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f192e924",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/franziskaoschmann/Documents/public_policy/pp_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import AutoTokenizer, TFAutoModelForSequenceClassification\n",
+ "import tensorflow as tf\n",
+ "from sklearn.manifold import TSNE\n",
+ "import pandas as pd\n",
+ "\n",
+ "import numpy as np\n",
+ "import numpy\n",
+ "\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "017a5f0c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load subset of data\n",
+ "df = pd.read_csv('../data/tamedia_for_classifier_v4_preproc_test.csv')\n",
+ "df_hsprob = df[df.hsprob > 0.7]\n",
+ "df_sub = df_hsprob.sample(100)\n",
+ "text = list(df_sub.text)\n",
+ "label = df_sub.rejected"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "24d7835a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
+ "\n",
+ "All the weights of TFBertForSequenceClassification were initialized from the PyTorch model.\n",
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n",
+ "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Load tokenizer and model\n",
+ "tokenizer = AutoTokenizer.from_pretrained('deepset/bert-base-german-cased-hatespeech-GermEval18Coarse')\n",
+ "model = TFAutoModelForSequenceClassification.from_pretrained('deepset/bert-base-german-cased-hatespeech-GermEval18Coarse')\n",
+ "inputs = tokenizer(list(text), return_tensors=\"tf\", padding=True, truncation=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b5e09595",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get hidden states of model\n",
+ "model_out = model(**inputs, output_hidden_states=True,return_dict=True)\n",
+ "hidden_states = model_out.hidden_states[1:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "14888084",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reduce embedding\n",
+ "layer_embed_reduced = tf.reduce_sum(hidden_states[10], axis = 1).numpy()\n",
+ "dim_reducer = TSNE(n_components=2)\n",
+ "two_dim_embed = dim_reducer.fit_transform(layer_embed_reduced)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "fc730de1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x35f8d78e0>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Visualize embedding\n",
+ "ix_rej = np.where(df_sub.rejected == 1)\n",
+ "ix_acc = np.where(df_sub.rejected == 0)\n",
+ "plt.scatter(two_dim_embed[ix_rej,0], two_dim_embed[ix_rej,1], label = 'rejected')\n",
+ "plt.scatter(two_dim_embed[ix_acc,0], two_dim_embed[ix_acc,1], label = 'accepted')\n",
+ "plt.legend()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "pp_env",
+ "language": "python",
+ "name": "pp_env"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/saved_models/BERT_logs/eval_pretrained_germ_bert.csv b/saved_models/BERT_logs/eval_pretrained_germ_bert.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9c87b2ec0dcb875d9cc2017111cac6218409ee68
--- /dev/null
+++ b/saved_models/BERT_logs/eval_pretrained_germ_bert.csv
@@ -0,0 +1,13 @@
+,logs
+path_repo,.
+path_model,bert-base-german-cased
+input_data,data/tamedia_for_classifier_v4_preproc_test.csv
+text_preprocessing,TRUE
+newspaper,tagesanzeiger
+lang,de
+topic,
+remove_duplicates,TRUE
+min_num_words,3
+val_score,
+hsprob,"[0.0,1.0]"
+pretrained_model,bert-base-german-cased
diff --git a/saved_models/BERT_logs/eval_pretrained_hs_bert.csv b/saved_models/BERT_logs/eval_pretrained_hs_bert.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3703b92caf0ff6c7eeff0de3d02ec3466d7f5a9e
--- /dev/null
+++ b/saved_models/BERT_logs/eval_pretrained_hs_bert.csv
@@ -0,0 +1,13 @@
+,logs
+path_repo,.
+path_model,deepset/bert-base-german-cased-hatespeech-GermEval18Coarse
+input_data,data/tamedia_for_classifier_v4_preproc_test.csv
+text_preprocessing,TRUE
+newspaper,tagesanzeiger
+lang,de
+topic,
+remove_duplicates,TRUE
+min_num_words,3
+val_score,
+hsprob,"[0.0, 1.0]"
+pretrained_model,deepset/bert-base-german-cased-hatespeech-GermEval18Coarse
diff --git a/src/BERT_utils.py b/src/BERT_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..990948da813360ca1c198c87ce836a4982a1051b
--- /dev/null
+++ b/src/BERT_utils.py
@@ -0,0 +1,37 @@
+import numpy as np
+import tensorflow as tf
+from tqdm import tqdm
+
+from typing import List
+
+def split_batches(text: np.ndarray, batch_size: int=100) -> List:
+ """
+ Splits list with comments into batches
+ :param text: Array containing comments
+ :param batch_size: Number of comments per batch
+ """
+ text_list = list(text)
+ text_batches=[text_list[idx:idx+batch_size] for idx in range(0, len(text_list), batch_size)]
+ return text_batches
+
+def predict_batches(text: np.ndarray, model, tokenizer) -> np.ndarray:
+ """
+ Makes prediction for all batches and combines all predictions
+ :param text: Array containing comments
+ :param model:
+ :param tokenizer:
+ """
+ text_batches = split_batches(text)
+ y_pred_all = []
+ y_prob_all = []
+ for batch in tqdm(text_batches):
+ inputs = tokenizer(batch, return_tensors="tf", padding=True, truncation=True)
+ logits = model(**inputs).logits
+ y_pred_batch = tf.argmax(logits,axis=1)
+ y_prob_batch = tf.math.softmax(logits, axis=-1)[:,1]
+ y_pred_all.append(y_pred_batch)
+ y_prob_all.append(y_prob_batch)
+ y_pred_all = np.concatenate(y_pred_all)
+ y_prob_all = np.concatenate(y_prob_all)
+
+ return y_pred_all, y_prob_all
\ No newline at end of file
diff --git a/src/MNB_utils.py b/src/MNB_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6db6889735594c494a1f3680a5e72d60eff09a9
--- /dev/null
+++ b/src/MNB_utils.py
@@ -0,0 +1,67 @@
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from nltk.corpus import stopwords
+
+import datetime
+from joblib import dump, load
+import os
+from pathlib import Path
+
+from typing import Union
+
+from src.preprocessing_text import TextProcessor
+
+
+def create_pipeline() -> Pipeline:
+ """
+ Creates classification pipeline
+ """
+
+ # define preprocessor
+ tp = TextProcessor()
+
+ # define vectorizer
+ stop_words_ge = stopwords.words("german")
+ vectorizer = TfidfVectorizer(
+ stop_words=stop_words_ge, ngram_range=(1, 4), max_features=3000
+ )
+
+ # define model
+ mnb = MultinomialNB(alpha=0.1)
+
+ # set pipeline
+ pipe = Pipeline([("processor", tp), ("vectorizer", vectorizer), ("mnb", mnb)])
+
+ return pipe
+
+
+def create_path() -> Union[str, os.PathLike]:
+ """
+ Creates path to store trained model
+ """
+ if not os.path.exists("saved_models/MNB/"):
+ os.makedirs("saved_models/MNB/")
+
+ timestemp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
+
+ return Path("saved_models/MNB/" + timestemp + ".joblib")
+
+
+def save_model(pipe: Pipeline, path):
+ """
+ Saves trained model
+ :param pipe: Trained pipeline
+ """
+ dump(pipe, path)
+
+
+def load_model(path: Union[str, os.PathLike]) -> Pipeline:
+ """
+ Loads trained model
+ :param path: Path to pipeline
+ """
+ pipe = load(path)
+
+ return pipe
diff --git a/src/eval_utils.py b/src/eval_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ac84a1eecb3ab9dbee79581239dce9c8b112569
--- /dev/null
+++ b/src/eval_utils.py
@@ -0,0 +1,15 @@
+def gen_scores_dict(precision: float, recall: float, f1: float, accuracy: float):
+ """
+ Generates dictionary containing most important scores
+ :param precision: Precision score
+ :param recall: Recall score
+ :param f1: F1 score
+ :param accuracy: Accuracy score
+ """
+ results = dict()
+ results["precision"] = precision
+ results["recall"] = recall
+ results["f1"] = f1
+ results["accuracy"] = accuracy
+
+ return results
diff --git a/src/prepare_bert_tf.py b/src/prepare_bert_tf.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5e315f4de03b22afb16694fb4f3d797f4a5e0de
--- /dev/null
+++ b/src/prepare_bert_tf.py
@@ -0,0 +1,58 @@
+import pandas as pd
+from datasets import Dataset, DatasetDict
+from sklearn.model_selection import train_test_split
+import evaluate
+import numpy as np
+from transformers import create_optimizer
+
+
+def df2dict(df: pd.DataFrame, test_size: float = 0.2, split_data: bool = True):
+ """
+ Converts Dataframe into Huggingface Dataset
+ :param df: input dataframe
+ :param test_size: size of test set
+ :param split_data: whether data should be split or not
+ """
+ #df.sample(10000, replace=True)
+
+ if split_data:
+ train, test = train_test_split(df, test_size=test_size)
+
+ ds_train = Dataset.from_pandas(train)
+ ds_test = Dataset.from_pandas(test)
+
+ ds = DatasetDict()
+ ds["train"] = ds_train
+ ds["test"] = ds_test
+
+ else:
+ ds = Dataset.from_pandas(df)
+
+ #ds = DatasetDict()
+ #ds["test"] = ds_all
+
+ return ds
+
+
+def compute_metrics(eval_pred):
+ """
+ Computes metrics during training
+ """
+ accuracy = evaluate.load("accuracy")
+ predictions, labels = eval_pred
+ predictions = np.argmax(predictions, axis=1)
+
+ return accuracy.compute(predictions=predictions, references=labels)
+
+
+def prepare_training(dataset, batch_size: int = 16, num_epochs: int = 5):
+ """
+ Prepares training and sets params
+ """
+ batches_per_epoch = len(dataset["train"]) // batch_size
+ total_train_steps = int(batches_per_epoch * num_epochs)
+ optimizer, schedule = create_optimizer(
+ init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps
+ )
+
+ return optimizer, schedule
diff --git a/src/preprocessing.py b/src/preprocessing.py
index 59f7476f855218a69ee9dfd70b8dce1be8c478d5..421eb48713cb3b44c7ba55687907ab4edbe419b5 100644
--- a/src/preprocessing.py
+++ b/src/preprocessing.py
@@ -7,34 +7,28 @@ import pandas as pd
from pathlib import Path
from typing import Union
-import time
class DataProcessor(object):
-
def __init__(self, path_data: Union[str, os.PathLike]):
-
"""
:param path_data: Path to input dataframe.
"""
self.path_data = path_data
-
def get_lang_detector(self, nlp, name):
"""
Gets language detector.
"""
return LanguageDetector(seed=42)
-
- def detect_language(self, text: str, nlp_model):
+ def detect_language(self, text: str, nlp_model):
"""Detect language per comment.
:param text: Text of comment.
"""
doc = nlp_model(text)
language = doc._.language
- return language['language']
-
+ return language["language"]
def init_nlp_model(self):
"""
@@ -42,12 +36,10 @@ class DataProcessor(object):
"""
self.nlp_model = spacy.load("en_core_web_sm")
Language.factory("language_detector", func=self.get_lang_detector)
- self.nlp_model.add_pipe('language_detector', last=True)
-
+ self.nlp_model.add_pipe("language_detector", last=True)
def add_language(self):
- """Add language column to dataframe and saves new file.
- """
+ """Add language column to dataframe and saves new file."""
# Load data
df = pd.read_csv(self.path_data)
@@ -55,14 +47,11 @@ class DataProcessor(object):
# Detect language
self.init_nlp_model()
- lang = df_new.text.apply(self.detect_language, nlp_model = self.nlp_model)
- df_new['language'] = lang
+ lang = df_new.text.apply(self.detect_language, nlp_model=self.nlp_model)
+ df_new["language"] = lang
# Save new file
f = self.path_data
fname_new = f"{os.path.splitext(os.path.basename(f))[0]}_preproc.csv"
path_new = Path(Path(self.path_data).parent).joinpath(fname_new)
df_new.to_csv(path_new)
-
-
-
diff --git a/src/preprocessing_df.py b/src/preprocessing_df.py
new file mode 100644
index 0000000000000000000000000000000000000000..421eb48713cb3b44c7ba55687907ab4edbe419b5
--- /dev/null
+++ b/src/preprocessing_df.py
@@ -0,0 +1,57 @@
+import spacy
+from spacy.language import Language
+from spacy_language_detection import LanguageDetector
+
+import os
+import pandas as pd
+from pathlib import Path
+from typing import Union
+
+
+class DataProcessor(object):
+ def __init__(self, path_data: Union[str, os.PathLike]):
+ """
+ :param path_data: Path to input dataframe.
+ """
+ self.path_data = path_data
+
+ def get_lang_detector(self, nlp, name):
+ """
+ Gets language detector.
+ """
+ return LanguageDetector(seed=42)
+
+ def detect_language(self, text: str, nlp_model):
+ """Detect language per comment.
+ :param text: Text of comment.
+ """
+ doc = nlp_model(text)
+ language = doc._.language
+
+ return language["language"]
+
+ def init_nlp_model(self):
+ """
+ Initializes NLP model for langugae detection
+ """
+ self.nlp_model = spacy.load("en_core_web_sm")
+ Language.factory("language_detector", func=self.get_lang_detector)
+ self.nlp_model.add_pipe("language_detector", last=True)
+
+ def add_language(self):
+ """Add language column to dataframe and saves new file."""
+
+ # Load data
+ df = pd.read_csv(self.path_data)
+ df_new = df.copy()
+
+ # Detect language
+ self.init_nlp_model()
+ lang = df_new.text.apply(self.detect_language, nlp_model=self.nlp_model)
+ df_new["language"] = lang
+
+ # Save new file
+ f = self.path_data
+ fname_new = f"{os.path.splitext(os.path.basename(f))[0]}_preproc.csv"
+ path_new = Path(Path(self.path_data).parent).joinpath(fname_new)
+ df_new.to_csv(path_new)
diff --git a/src/preprocessing_text.py b/src/preprocessing_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb0a615807f64f6c7fd4d4561fafef0000f0d657
--- /dev/null
+++ b/src/preprocessing_text.py
@@ -0,0 +1,290 @@
+import string
+
+from collections import Counter
+import emoji
+import itertools
+import numpy as np
+import os
+import pandas as pd
+import re
+from sklearn.base import BaseEstimator, TransformerMixin
+import spacy
+from tqdm import tqdm
+
+from typing import Union, List
+
+
+class TextLoader(object):
+ """
+ Loads text data from specific path
+ """
+
+ def __init__(self, path: Union[str, os.PathLike]):
+ self.path = path
+
+ def load_col(self, col_name: str) -> List:
+ """
+ Loads specific column of dataframe to use less memory
+ :param col_name: Column name in dataframe
+ """
+ col = pd.read_csv(self.path, usecols=[col_name]).values
+ col = list(itertools.chain.from_iterable(col))
+ return col
+
+ def load_text_csv(
+ self,
+ newspaper: str = None,
+ lang: str = None,
+ topic: str = None,
+ hsprob: list = None,
+ load_subset: bool = False,
+ remove_duplicates: bool = False,
+ min_num_words: int = None,
+ ) -> pd.DataFrame:
+ """
+ Loads dataframe and extracts text depending on newspaper and langugae
+ """
+ if load_subset:
+ newspaper_col = self.load_col(col_name="originTenantId")
+ language_col = self.load_col(col_name="language")
+ text_col = self.load_col(col_name="text")
+ rejected_col = self.load_col(col_name="rejected")
+
+ df = pd.DataFrame(
+ {
+ "text": text_col,
+ "originTenantId": newspaper_col,
+ "language": language_col,
+ "rejected": rejected_col,
+ }
+ )
+
+ else:
+ df = pd.read_csv(self.path)
+ # df = df.sample(100000)
+
+ df = df.rename(columns={"rejected": "label"})
+ df_filter = self.filter_df(
+ df,
+ min_num_words,
+ remove_duplicates,
+ newspaper,
+ lang,
+ topic,
+ hsprob
+ )
+
+ return df_filter
+
+ def filter_df(
+ self,
+ df: pd.DataFrame,
+ min_num_words: int,
+ remove_duplicates: bool,
+ newspaper: str,
+ lang: str,
+ topic: str,
+ hsprob: list,
+ ) -> pd.DataFrame:
+ """
+ Filters data depending on given arguments.
+ :param df: Input dataframe
+ :param min_words: minimal amount of words per topic
+ :param remove_duplicates: Boolean flag whether or not to remove duplicates.
+ :param newspaper: Name of newspaper
+ :param lang: Language
+ :param topic: Topic
+ :param hsprob: List with min max values for hate speech probability
+ """
+
+ if min_num_words:
+ df = self.filter_min_words(df)
+
+ if newspaper:
+ df = self.filter_newspaper(df, newspaper=newspaper)
+
+ if lang:
+ df = self.filter_language(df, lang=lang)
+
+ if topic:
+ df = self.filter_topic(df, topic=topic)
+
+ if hsprob:
+ df = self.filter_hsprob(df, thresh=hsprob)
+
+ if remove_duplicates:
+ df = self.remove_duplicate_comments(df)
+
+ #df = df[["text", "originTenantId", "label", "topic"]]
+ df = df[["text", "originTenantId", "label", "topic", "hsprob"]]
+
+ return df
+
+ def filter_newspaper(self, df: pd.DataFrame, newspaper: str):
+ """
+ Filters out comments from specific newspaper.
+ :param df: Input dataframe
+ :param newspaper: Name of newspaper
+ """
+ return df.loc[(df.originTenantId == newspaper)]
+
+ def filter_language(self, df: pd.DataFrame, lang: str):
+ """
+ Filters out comments with specific language
+ :param df: Input dataframe
+ :param lang: Language
+ """
+ return df.loc[(df.language == lang)]
+
+ def filter_topic(self, df: pd.DataFrame, topic: str):
+ """
+ Filters out comments with specific topic
+ :param df: Input dataframe
+ :param lang: Language
+ """
+ return df.loc[(df.topic == topic)]
+
+ def filter_min_words(self, df: pd.DataFrame, min_words: int = 3):
+ """Filters out comments with less than min words
+ :param df: Input dataframe
+ :param min_words: minimal amount of words per topic
+ """
+ return df[np.array([len((re.findall(r"\w+", t))) for t in df.text]) > min_words]
+
+ def filter_hsprob(self, df: pd.DataFrame, thresh: list):
+ """
+ Filters out comments from specific newspaper.
+ :param df: Input dataframe
+ :param newspaper: Name of newspaper
+ """
+ return df.loc[(df.hsprob > thresh[0])&(df.hsprob < thresh[1])]
+
+ def get_comments_per_topic(self, df, num_topic: int = 10) -> dict:
+ """
+ Returns dictionary containing df's per topic for most common topics.
+ :param num_topic: Number f most common topics
+ """
+ # df = pd.read_csv(self.path)
+ # df = df.rename(columns={"rejected": "label"})
+
+ topics = Counter(df["topic"]).most_common(num_topic)
+
+ return topics
+
+ def find_duplicate_comments(self, df: pd.DataFrame) -> np.ndarray:
+ """ "
+ Find duplicate comments in dataframe
+ :param df: Input dataframe
+ """
+ c_comm = Counter(df.text.values)
+ duplicate_comments = np.array(list(c_comm.keys()))[
+ np.where(np.array(list(c_comm.values())) > 1)
+ ]
+
+ # indices_repetitions = np.concatenate(
+ # [
+ # np.where(df.text == d)[0][
+ # np.argsort(df.createdAt.iloc[np.where(df.text == d)[0]].values)[:-1]
+ # ]
+ # for d in tqdm(duplicate_comments)
+ # ]
+ # )
+ if len(duplicate_comments) > 0:
+ indices_repetitions = np.concatenate(
+ [np.where(df.text == d)[0] for d in tqdm(duplicate_comments)]
+ )
+ else:
+ indices_repetitions = np.array([])
+
+ return indices_repetitions
+
+ def remove_duplicate_comments(self, df: pd.DataFrame) -> pd.DataFrame:
+ """Removes duplicates from dataframe
+ :param df: Input dataframe
+ """
+ print("Find and remove duplicates")
+ indices = self.find_duplicate_comments(df)
+ if len(indices) > 0:
+ return df.drop(df.index[indices])
+ else:
+ return df
+
+
+class TextProcessor(BaseEstimator, TransformerMixin):
+ def __init__(self, lowercase=True): # params setting single steps to True or False
+ self.punctuation = list(string.punctuation)
+ self.punctuation.remove("@")
+ self.punctuation = "".join(self.punctuation)
+ self.lowercase = lowercase
+
+ def fit(self, X, y=None):
+ """
+ Fits preprocessing to data
+ """
+ return self
+
+ def transform(self, X):
+ """
+ Transforms data after fitting
+ """
+ text_proc = X.apply(self.preprocess)
+ return text_proc
+
+ def preprocess(self, text) -> str:
+ """
+ Applies preprocessing to text
+ :param text: Input text
+ :param nlp: Loaded nlp model
+ """
+ text_proc = self.transcripe_emojis(text)
+ text_proc = self.remove_spaces(text)
+ text_proc = self.remove_punctuation(text_proc)
+ text_proc = self.remove_mentions(text_proc)
+ if self.lowercase:
+ text_proc = self.fold_case(text_proc)
+
+ return text_proc
+
+ def remove_spaces(self, text: str) -> str:
+ """
+ Removes extra white spaces and linebreaks
+ :param text: Input text
+ """
+ return " ".join(text.split())
+
+ def remove_punctuation(self, text: str) -> str:
+ """
+ Removes puntuation from text except @
+ :param text: Input text
+ """
+ return text.translate(str.maketrans("", "", self.punctuation))
+
+ def fold_case(self, text: str) -> str:
+ """
+ Transforms text to lowercase
+ :param text: Input text
+ """
+ return text.casefold()
+
+ def remove_mentions(self, text: str) -> str:
+ """
+ Removes @-mentions from text
+ :param text: Input text
+ """
+ return re.sub("@([a-zA-Z0-9]{1,15})", "", text)
+
+ def lemmatize_text(self, text: str) -> str:
+ """
+ Lemmatizes text
+ :param text: Input text
+ """
+ doc = self.nlp(text)
+ return " ".join([word.lemma_ for word in doc])
+
+ def transcripe_emojis(self, text: str) -> str:
+ """
+ Transcripes emojis into words
+ """
+ return emoji.demojize(text, language="de", delimiters=("", "")).replace(
+ "_", " "
+ )
diff --git a/src/train_logs.py b/src/train_logs.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c22d41b961cfe60e2fa44324b5b968d4e426a39
--- /dev/null
+++ b/src/train_logs.py
@@ -0,0 +1,123 @@
+from typing import Tuple, Union, Optional
+import os
+
+import pandas as pd
+import numpy as np
+
+
+def save_logs(
+ path_repo: Union[str, os.PathLike],
+ path_model: Union[str, os.PathLike],
+ input_data: Union[str, os.PathLike],
+ text_preprocessing: bool,
+ newspaper: str,
+ lang: str,
+ topic: str,
+ hsprob: list,
+ remove_duplicates: bool,
+ min_num_words: int,
+ model_name: str,
+ val_score: Optional[Union[str, os.PathLike]] = None,
+ pretrained_model: Optional[str] = None,
+):
+ """
+ Saves training logs which can be used during evaluation
+ :param path_repo: Path to repository
+ :param path_model: Path to trained model
+ :param input_data: Path to used train data
+ :param text_preprocessing: Boolean flag whether preprocessing was used or not
+ :param newspaper: Name of newspaper
+ :param lang: Selected language
+ :param topic: Selected topic
+ :param hsprob: List with min max values for hate speech probability
+ :param remove_duplicates: Boolean flag whether duplicates should be removed
+ :param min_num_words: Minimum number of words per comment
+ :param model_name: Name of model
+ :param pretrained_model: Name of pretrained BERT model
+ """
+ logs = dict()
+ logs["path_repo"] = path_repo
+ logs["path_model"] = path_model
+ logs["input_data"] = input_data
+ logs["text_preprocessing"] = text_preprocessing
+ logs["newspaper"] = newspaper
+ logs["lang"] = lang
+ logs["topic"] = topic
+ logs["hsprob"] = hsprob
+ logs["remove_duplicates"] = remove_duplicates
+ logs["min_num_words"] = min_num_words
+ logs["val_score"] = val_score
+ logs["pretrained_model"] = pretrained_model
+
+ path_logs = (path_repo).joinpath("saved_models/" + model_name + "_logs/")
+ if not os.path.exists(path_logs):
+ os.makedirs(path_logs)
+
+ df_logs = pd.DataFrame.from_dict(logs, orient="index", columns=["logs"])
+
+ df_logs.to_csv(path_logs.joinpath(path_model.stem).with_suffix(".csv"))
+
+
+def load_logs(
+ train_logs: Union[str, os.PathLike]
+) -> Tuple[
+ Union[str, os.PathLike],
+ Union[str, os.PathLike],
+ str,
+ bool,
+ str,
+ str,
+ str,
+ list,
+ bool,
+ int,
+]:
+ """
+ Loads training logs
+ :param train_logs: Path to csv-file containing logs
+ """
+ df = pd.read_csv(train_logs, index_col="Unnamed: 0")
+ path_repo = df.loc["path_repo"].values[0]
+ path_model = df.loc["path_model"].values[0]
+ input_data = df.loc["input_data"].values[0].replace("train", "test")
+ text_preprocessing = df.loc["text_preprocessing"].values[0]
+ newspaper = df.loc["newspaper"].values[0]
+ lang = df.loc["lang"].values[0]
+ topic = df.loc["topic"].values[0]
+ hsprob = eval(df.loc["hsprob"].values[0])
+ remove_duplicates = df.loc["remove_duplicates"].values[0]
+ min_num_words = df.loc["min_num_words"].values[0]
+ pretrained_model = df.loc["pretrained_model"].values[0]
+
+ # check whether topic is str or NaN
+ if topic != topic:
+ topic = None
+
+ if pretrained_model:
+ return (
+ path_repo,
+ path_model,
+ input_data,
+ text_preprocessing,
+ newspaper,
+ lang,
+ topic,
+ hsprob,
+ remove_duplicates,
+ min_num_words,
+ pretrained_model,
+ )
+
+ else:
+ return (
+ path_repo,
+ path_model,
+ input_data,
+ text_preprocessing,
+ newspaper,
+ lang,
+ topic,
+ hsprob,
+ remove_duplicates,
+ min_num_words,
+ )