diff --git a/demo/blogposts/Healthcare_NER_Model_Evaluation_with_LangTest.ipynb b/demo/blogposts/Healthcare_NER_Model_Evaluation_with_LangTest.ipynb index 49f832533..f436164c7 100644 --- a/demo/blogposts/Healthcare_NER_Model_Evaluation_with_LangTest.ipynb +++ b/demo/blogposts/Healthcare_NER_Model_Evaluation_with_LangTest.ipynb @@ -24,247 +24,579 @@ "id": "l66BnmMW3W9A" }, "source": [ - "# Evaluating Robustness, Bias, and Accuracy in Healthcare NER Models\n", + "# Evaluating Robustness and Accuracy in Healthcare NER Models\n", "\n", - "In this blog post, we will compare the performance of two named entity recognition (NER) healthcare models: **med7** and **ner_posology_langtest**. NER models are crucial for extracting relevant information from clinical text, enabling tasks like medication extraction, dosage identification, and more.\n", + "Named Entity Recognition (NER) models play a crucial role in extracting relevant information from clinical text, enabling tasks such as medication extraction and dosage identification in healthcare.The two NER models we will examine are **ner posology** and **med7**, where both the models recognizes seven categories, including `Drug`, `Duration`, `Strength`, `Form`, `Frequency`, `Dosage`, and `Route`.\n", "\n", "## Model Overview\n", "\n", - "1. **med7** - [GitHub Repository](https://github.com/kormilitzin/med7)\n", "\n", - " Med7 is a transferable clinical natural language processing model compatible with spaCy v3+. It is trained on the free-text electronic health records dataset called `MIMIC-III`. The model recognizes seven categories, including Drug, Duration, Strength, Form, Frequency, Dosage, and Route. Med7 offers a powerful solution for clinical NER tasks.\n", + "1. **ner posology** - [John Snow Labs Model](https://nlp.johnsnowlabs.com/2023/07/28/ner_posology_langtest_en.html)\n", "\n", - "2. **ner_posology_langtest** - John Snow Labs Model\n", + " The ner posology model by John Snow Labs is specifically designed for posology NER tasks in the healthcare domain. It utilizes the `embeddings_clinical` word embeddings model. This pretrained deep learning model demonstrates strong performance in extracting medication-related information from clinical text.\n", "\n", - " The ner_posology_langtest model is a pretrained deep learning model for posology NER by John Snow Labs. It leverages the `embeddings_clinical` word embeddings model and predicts similar entities as med7. This model is also designed specifically for healthcare-related tasks.\n", + "2. **med7** - [GitHub Repository](https://github.com/kormilitzin/med7)\n", "\n", + " med7 is a powerful clinical NER model that offers a comprehensive solution for extracting relevant information from healthcare text. Trained on the MIMIC-III dataset, it is compatible with spaCy v3+ . The model’s details can be found in the paper titled [“Med7: a transferable clinical natural language processing model for electronic health records”](https://arxiv.org/abs/2003.01271) authored by Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, and Alejo Nevado-Holgado, published as an arXiv preprint in 2020.\n", "\n", - "- To evaluate the performance and compare the robustness, bias, and accuracy of both models, we will be using **langtest**.\n" + "- To evaluate the performance and compare the robustness and Accuracy of both models, we will be using **langtest**.\n" ] }, { "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation: Using the ner posology Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Setting up License Keys for ner_posology_langtest Model**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "metadata": { - "id": "IkXykhk7YLEK" + "execution": { + "iopub.execute_input": "2023-08-19T16:42:23.631237Z", + "iopub.status.busy": "2023-08-19T16:42:23.630668Z", + "iopub.status.idle": "2023-08-19T16:42:23.636586Z", + "shell.execute_reply": "2023-08-19T16:42:23.636054Z", + "shell.execute_reply.started": "2023-08-19T16:42:23.631213Z" + }, + "tags": [] }, + "outputs": [], "source": [ - "### LangTest\n", - "\n", - "**LangTest** is an open-source Python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using models from **John Snow Labs**, **Hugging Face**, **spaCy**, or LLMs from **OpenAI**, **Cohere**, **AI21**, **Hugging Face Inference API**, or **Azure-OpenAI**, LangTest has got you covered. It provides a comprehensive set of tests for evaluating Named Entity Recognition (NER), Text Classification, Question-Answering, and Summarization models. The library supports over 50 out-of-the-box tests, categorized into robustness, accuracy, bias, representation, and fairness.\n", - "\n", - "\n", - "\n", - "### Robustness Testing\n", - "\n", - "Model robustness can be described as the ability of a model to keep similar levels of accuracy, precision and recall when perturbations are made to the data it is predicting on. For example, in the case of NER, the goal is to understand how documents with typos or fully uppercased sentences affect the model's prediction performance compared to documents similar to those in the original training set.\n", - "\n", - "\n", - "**`Supported Robustness tests :`**
\n", - "\n", - "\n", - "- **`uppercase`**: capitalization of the test set is turned into uppercase\n", - "\n", - "- **`lowercase`**: capitalization of the test set is turned into lowercase\n", - "\n", - "- **`titlecase`**: capitalization of the test set is turned into title case\n", - "\n", - "- **`add_punctuation`**: special characters at end of each sentence are replaced by other special characters, if no\n", - "special character at the end, one is added\n", - "\n", - "- **`strip_punctuation`**: special characters are removed from the sentences (except if found in numbers, such as '2.5')\n", - "\n", - "- **`add_typo`**: typos are introduced in sentences\n", - "\n", - "- **`add_contraction`**: contractions are added where possible (e.g. 'do not' contracted into 'don't')\n", - "\n", - "- **`add_context`**: tokens are added at the beginning and at the end of the sentences\n", - "\n", - "- **`swap_entities`**: named entities replaced with same entity type with same token count from terminology\n", - "\n", - "- **`swap_cohyponyms`**: Named entities replaced with co-hyponym from the WordNet database\n", - "\n", - "- **`american_to_british`**: American English will be changed to British English\n", - "\n", - "- **`british_to_american`**: British English will be changed to American English\n", - "\n", - "- **`number_to_word`**: Converts numeric values in sentences to their equivalent verbal representation.\n", - "\n", - "- **`add_ocr_typo`**: Ocr typos are introduced in sentences\n", - "\n", - "- **`multiple_perturbations`** : Transforms the given sentences by applying multiple perturbations in a specific sequence.\n", - "\n", - "- **`add_speech_to_text_typo`**: Introduce common conversion errors from SSpeech to Text conversion.\n", - "\n", - "- **`add_abbreviation`**:Replaces words or expressions in texts with their abbreviations\n", - "\n", - "- **`adjective_synonym_swap`** : Transforms the adjectives in the given sentences to their synonyms.\n", - "\n", - "- **`adjective_antonym_swap`** : Transforms the adjectives in the given sentences to their antonyms.\n", - "\n", - "
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\n", - "\n", - "### Bias Testing\n", - "\n", - "Model bias refers to the phenomenon where the model produces results that are systematically skewed in a particular direction. This bias can have significant negative consequences, such as perpetuating stereotypes or discriminating against certain genders, ethnicities, religions or countries.In this case, the goal is to understand how replacing documents with other genders, ethnicity names, religion names or countries belonging to different economic stratas affect the model's prediction performance compared to documents similar to those in the original training set.\n", - "\n", - "\n", - "\n", - "**`Supported Bias tests :`**
\n", - "\n", - "\n", - "- **`replace_to_male_pronouns`**: female/neutral pronouns of the test set are turned into male pronouns.\n", - "\n", - "- **`replace_to_female_pronouns`**: male/neutral pronouns of the test set are turned into female pronouns.\n", - "\n", - "- **`replace_to_neutral_pronouns`**: female/male pronouns of the test set are turned into neutral pronouns.\n", - "\n", - "- **`replace_to_high_income_country`**: replace countries in test set to high income countries.\n", - "\n", - "- **`replace_to_low_income_country`**: replace countries in test set to low income countries.\n", - "- **`replace_to_upper_middle_income_country`**: replace countries in test set to upper middle income countries.\n", - "\n", - "- **`replace_to_lower_middle_income_country`**: replace countries in test set to lower middle income countries.\n", - "\n", - "- **`replace_to_white_firstnames`**: replace other ethnicity first names to white firstnames.\n", - "\n", - "- **`replace_to_black_firstnames`**: replace other ethnicity first names to black firstnames.\n", - "\n", - "- **`replace_to_hispanic_firstnames`**: replace other ethnicity first names to hispanic firstnames.\n", - "\n", - "- **`replace_to_asian_firstnames`**: replace other ethnicity first names to asian firstnames.\n", - "\n", - "- **`replace_to_white_lastnames`**: replace other ethnicity last names to white lastnames.\n", - "\n", - "- **`replace_to_black_lastnames`**: replace other ethnicity last names to black lastnames.\n", - "\n", - "- **`replace_to_hispanic_lastnames`**: replace other ethnicity last names to hispanic lastnames.\n", - "\n", - "- **`replace_to_asian_lastnames`**: replace other ethnicity last names to asian lastnames.\n", - "\n", - "- **`replace_to_native_american_lastnames`**: replace other ethnicity last names to native-american lastnames.\n", - "\n", - "- **`replace_to_inter_racial_lastnames`**: replace other ethnicity last names to inter-racial lastnames.\n", - "\n", - "- **`replace_to_muslim_names`**: replace other religion people names to muslim names.\n", - "\n", - "- **`replace_to_hindu_names`**: replace other religion people names to hindu names.\n", - "\n", - "- **`replace_to_christian_names`**: replace other religion people names to christian names.\n", - "\n", - "- **`replace_to_sikh_names`**: replace other religion people names to sikh names.\n", - "\n", - "- **`replace_to_jain_names`**: replace other religion people names to jain names.\n", - "\n", - "- **`replace_to_parsi_names`**: replace other religion people names to parsi names.\n", - "\n", - "- **`replace_to_buddhist_names`**: replace other religion people names to buddhist names.\n", - "\n", - "\n", - "
\n", - "
\n", - "\n", - "### Fairness Testing\n", - "\n", - "Fairness testing is a critical aspect of evaluating the performance of a machine learning model, especially when the model has potential implications for specific groups of people. Fairness testing aims to ensure that the model is not biased towards or against any particular group and that it produces unbiased results for all groups.\n", - "To support fairness testing, several fairness tests are available, which evaluate the model's performance on various attributes such as gender.\n", - "\n", - "**`Supported Fairness tests :`**
\n", - "\n", - "- **`min_gender_f1_score`**: Determine if any gender(male, female or unknown) has less than the desired f1 score.\n", - "\n", - "- **`max_gender_f1_score`**: Determine if any gender(male, female or unknown) has more than the desired f1 score.\n", - "\n", - "\n", - "
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\n", - "\n", - "### Representation Testing\n", - "\n", - "The goal of representation testing is to determine if a given dataset represents a specific population accurately or if it contains biases that could negatively impact the results of any analysis conducted on it.\n", + "import json\n", + "import os\n", + "license_key = \"spark_nlp.json\"\n", "\n", + "with open(license_key) as f:\n", + " license_keys = json.load(f)\n", + " \n", + "locals().update(license_keys)\n", "\n", + "# Adding license key-value pairs to environment variables\n", + "os.environ.update(license_keys)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Installing Required Packages**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T16:43:19.869231Z", + "iopub.status.busy": "2023-08-19T16:43:19.868742Z", + "iopub.status.idle": "2023-08-19T16:43:24.696364Z", + "shell.execute_reply": "2023-08-19T16:43:24.695679Z", + "shell.execute_reply.started": "2023-08-19T16:43:19.869209Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Installing pyspark and spark-nlp\n", + "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION\n", "\n", + "# Installing Spark NLP Healthcare\n", + "! pip install --upgrade -q spark-nlp-jsl==$JSL_VERSION --extra-index-url https://pypi.johnsnowlabs.com/$SECRET\n", "\n", - "**`Supported Representation tests :`**
\n", + "# Installing Spark NLP Display Library for visualization\n", + "! pip install -q spark-nlp-display" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Setting up Spark NLP and Spark Session**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T16:43:48.249135Z", + "iopub.status.busy": "2023-08-19T16:43:48.248558Z", + "iopub.status.idle": "2023-08-19T16:43:48.262720Z", + "shell.execute_reply": "2023-08-19T16:43:48.262193Z", + "shell.execute_reply.started": "2023-08-19T16:43:48.249112Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning::Spark Session already created, some configs may not take.\n", + "Spark NLP Version : 5.0.0\n", + "Spark NLP_JSL Version : 5.0.0\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", - "
\n", + "sentence_detector = SentenceDetector()\\\n", + " .setInputCols([\"document\"])\\\n", + " .setOutputCol(\"sentence\")\n", "\n", - "### Accuracy Testing\n", + "tokenizer = Tokenizer()\\\n", + " .setInputCols([\"sentence\"])\\\n", + " .setOutputCol(\"token\")\n", "\n", - "Accuracy testing is a crucial step in evaluating the performance of a machine learning model. It involves measuring how well the model can correctly predict outcomes on a test dataset, which it has not seen before. The accuracy of a model is determined by comparing its predicted output with the actual output. To support the accuracy testing process, several accuracy tests are available. These tests aim to evaluate various aspects of a model's performance both labelwise such as its precision, recall, F1 score and overall like micro F1 score, macro F1 score, and weighted F1 score.\n", + "word_embeddings = WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n", + " .setInputCols([\"sentence\", \"token\"])\\\n", + " .setOutputCol(\"embeddings\")\n", "\n", + "clinical_ner = MedicalNerModel.pretrained(\"ner_posology_langtest\", \"en\", \"clinical/models\")\\\n", + " .setInputCols([\"sentence\", \"token\", \"embeddings\"])\\\n", + " .setOutputCol(\"ner\")\n", "\n", - "**`Supported Accuracy tests :`**\n", + "ner_converter = NerConverterInternal()\\\n", + " .setInputCols([\"sentence\", \"token\", \"ner\"])\\\n", + " .setOutputCol(\"ner_chunk\")\n", "\n", - "- **`min_precision_score`**: Determine if the actual precision score is less than the desired precision score.\n", + "nlp_pipeline = Pipeline(\n", + " stages=[\n", + " document_assembler, \n", + " sentence_detector, \n", + " tokenizer, \n", + " word_embeddings, \n", + " clinical_ner, \n", + " ner_converter\n", + " ])\n", "\n", - "- **`min_recall_score`**: Determine if the actual recall score is less than the desired recall score.\n", + "text = \"\"\"The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.\"\"\"\n", "\n", - "- **`min_f1_score`**: Determine if the actual f1 score is less than the desired f1 score.\n", + "data = spark.createDataFrame([[text]]).toDF(\"text\")\n", "\n", - "- **`min_micro_f1_score`**: Determine if the actual micro-f1 score is less than the desired micro-f1 score.\n", + "result = nlp_pipeline.fit(data).transform(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access the extracted entities from the result, you can run the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T16:46:42.470410Z", + "iopub.status.busy": "2023-08-19T16:46:42.469850Z", + "iopub.status.idle": "2023-08-19T16:46:45.349846Z", + "shell.execute_reply": "2023-08-19T16:46:45.349330Z", + "shell.execute_reply.started": "2023-08-19T16:46:42.470388Z" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Stage 9:===================================================> (10 + 1) / 11]\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------+---------+\n", + "| chunk|ner_label|\n", + "+--------------+---------+\n", + "| insulin| DRUG|\n", + "| Bactrim| DRUG|\n", + "| for 14 days| DURATION|\n", + "| Fragmin| DRUG|\n", + "| 5000 units| DOSAGE|\n", + "|subcutaneously| ROUTE|\n", + "| daily|FREQUENCY|\n", + "| topically| ROUTE|\n", + "| b.i.d|FREQUENCY|\n", + "| Lantus| DRUG|\n", + "| 40 units| DOSAGE|\n", + "|subcutaneously| ROUTE|\n", + "| at bedtime|FREQUENCY|\n", + "| OxyContin| DRUG|\n", + "| 30 mg| STRENGTH|\n", + "| p.o| ROUTE|\n", + "| q.12 h|FREQUENCY|\n", + "| folic acid| DRUG|\n", + "| 1 mg| STRENGTH|\n", + "| daily|FREQUENCY|\n", + "+--------------+---------+\n", + "only showing top 20 rows\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "result.select(F.explode(F.arrays_zip(result.ner_chunk.result,\n", + " result.ner_chunk.metadata)).alias(\"cols\"))\\\n", + " .select(F.expr(\"cols['0']\").alias(\"chunk\"),\n", + " F.expr(\"cols['1']['entity']\").alias(\"ner_label\")).show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To visualize the extracted entities, you can use the `NerVisualizer` class from the `sparknlp_display` library. Run the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T16:47:06.080264Z", + "iopub.status.busy": "2023-08-19T16:47:06.079729Z", + "iopub.status.idle": "2023-08-19T16:47:06.495089Z", + "shell.execute_reply": "2023-08-19T16:47:06.494536Z", + "shell.execute_reply.started": "2023-08-19T16:47:06.080242Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " The patient is a 30-year-old female with a long history of insulin DRUG dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim DRUG for 14 days DURATION for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin DRUG 5000 units DOSAGE subcutaneously ROUTE daily FREQUENCY, Xenaderm to wounds topically ROUTE b.i.d FREQUENCY., Lantus DRUG 40 units DOSAGE subcutaneously ROUTE at bedtime FREQUENCY, OxyContin DRUG 30 mg STRENGTH p.o ROUTE. q.12 h FREQUENCY., folic acid DRUG 1 mg STRENGTH daily FREQUENCY, levothyroxine DRUG 0.1 mg STRENGTH p.o ROUTE. daily FREQUENCY, Prevacid DRUG 30 mg STRENGTH daily FREQUENCY, Avandia 4 mg STRENGTH daily FREQUENCY, Norvasc DRUG 10 mg STRENGTH daily FREQUENCY, Lexapro DRUG 20 mg STRENGTH daily FREQUENCY, aspirin DRUG 81 mg STRENGTH daily FREQUENCY, Senna DRUG 2 DOSAGE tablets FORM p.o ROUTE. q FREQUENCY.a.m., Neurontin DRUG 400 mg STRENGTH p.o ROUTE. t.i.d FREQUENCY., Percocet DRUG 5/325 mg STRENGTH 2 DOSAGE tablets FORM q.4 h FREQUENCY. p.r.n., magnesium citrate DRUG 1 DOSAGE bottle FORM p.o ROUTE. p.r.n., sliding scale coverage insulin DRUG, Wellbutrin DRUG 100 mg STRENGTH p.o ROUTE. daily FREQUENCY, and Bactrim DS DRUG b.i.d FREQUENCY." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sparknlp_display import NerVisualizer\n", "\n", - "- **`min_macro_f1_score`**: Determine if the actual macro-f1 score is less than the desired macro-f1 score.\n", + "visualiser = NerVisualizer()\n", "\n", - "- **`min_weighted_f1_score`**: Determine if the actual min-weighted-f1 score is less than the desired min-weighted-f1 score.\n" + "visualiser.display(result = result.collect()[0] ,label_col = 'ner_chunk', document_col = 'document')" ] }, { "cell_type": "markdown", - "metadata": { - "id": "xiops0SbVMah" - }, + "metadata": {}, "source": [ - "## Med7 model" + "## Implementation: Using the med7 Model" ] }, { "cell_type": "markdown", - "metadata": { - "id": "OkhMwxOniI5f" - }, + "metadata": {}, "source": [ "#### Install the model:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-07-28T06:49:33.012695Z", - "iopub.status.busy": "2023-07-28T06:49:33.012111Z", - "iopub.status.idle": "2023-07-28T06:49:42.474117Z", - "shell.execute_reply": "2023-07-28T06:49:42.473530Z", - "shell.execute_reply.started": "2023-07-28T06:49:33.012670Z" + "iopub.execute_input": "2023-08-19T16:47:13.527336Z", + "iopub.status.busy": "2023-08-19T16:47:13.526853Z", + "iopub.status.idle": "2023-08-19T16:47:28.577356Z", + "shell.execute_reply": "2023-08-19T16:47:28.576640Z", + "shell.execute_reply.started": "2023-08-19T16:47:13.527316Z" }, - "id": "fF_ki8LhhZ0y", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting en-core-med7-lg==any\n", + " Downloading https://huggingface.co/kormilitzin/en_core_med7_lg/resolve/main/en_core_med7_lg-any-py3-none-any.whl (607.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m607.4/607.4 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta 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MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->spacy<3.5.0,>=3.4.2->en-core-med7-lg==any) (2.1.2)\n", + "Installing collected packages: wasabi, cymem, typer, spacy-loggers, spacy-legacy, smart-open, pydantic, murmurhash, langcodes, catalogue, blis, srsly, preshed, pathy, confection, thinc, spacy, en-core-med7-lg\n", + "Successfully installed blis-0.7.10 catalogue-2.0.9 confection-0.1.1 cymem-2.0.7 en-core-med7-lg-3.4.2.1 langcodes-3.3.0 murmurhash-1.0.9 pathy-0.10.2 preshed-3.0.8 pydantic-1.10.12 smart-open-6.3.0 spacy-3.4.4 spacy-legacy-3.0.12 spacy-loggers-1.0.4 srsly-2.4.7 thinc-8.1.12 typer-0.7.0 wasabi-0.10.1\n" + ] + } + ], "source": [ "# Vectors model\n", "!pip install https://huggingface.co/kormilitzin/en_core_med7_lg/resolve/main/en_core_med7_lg-any-py3-none-any.whl" @@ -273,7 +605,14 @@ { "cell_type": "markdown", "metadata": { - "id": "6bq4YQIxVXmR" + "execution": { + "iopub.execute_input": "2023-08-19T15:48:44.280221Z", + "iopub.status.busy": "2023-08-19T15:48:44.279658Z", + "iopub.status.idle": "2023-08-19T15:48:44.282716Z", + "shell.execute_reply": "2023-08-19T15:48:44.282280Z", + "shell.execute_reply.started": "2023-08-19T15:48:44.280195Z" + }, + "tags": [] }, "source": [ "### To utilize the `med7` model, you can follow these steps:" @@ -281,14 +620,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "execution": { + "iopub.execute_input": "2023-08-19T16:47:37.644788Z", + "iopub.status.busy": "2023-08-19T16:47:37.644063Z", + "iopub.status.idle": "2023-08-19T16:47:38.692829Z", + "shell.execute_reply": "2023-08-19T16:47:38.692369Z", + "shell.execute_reply.started": "2023-08-19T16:47:37.644764Z" }, - "id": "cqCuklOnVaUU", - "outputId": "b59e379b-eff5-4b41-e044-9d722dcde34e" + "tags": [] }, "outputs": [ { @@ -722,7 +1063,7 @@ " ('b.i.d.', 'FREQUENCY')]" ] }, - "execution_count": 33, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -757,597 +1098,441 @@ { "cell_type": "markdown", "metadata": { - "id": "pLZSRrS3tVNs" + "id": "iM4N5-PmUlza" }, "source": [ - "## ner_posology_langtest model" + "# Evaluating Model Performance with LangTest" ] }, { "cell_type": "markdown", - "metadata": { - "id": "Y5Dd4PaYkhao" - }, + "metadata": {}, "source": [ - "**Setting up License Keys for ner_posology_langtest Model**" + "Now that we have introduced the two prominent NER healthcare models, **ner posology** and **med7**, it is essential to evaluate and test their performance. Evaluating these models allows us to understand their strengths, weaknesses, and overall suitability for extracting relevant information from clinical text. To accomplish this, we will utilize the **LangTest**." ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-28T06:52:35.022363Z", - "iopub.status.busy": "2023-07-28T06:52:35.021815Z", - "iopub.status.idle": "2023-07-28T06:52:35.025655Z", - "shell.execute_reply": "2023-07-28T06:52:35.025203Z", - "shell.execute_reply.started": "2023-07-28T06:52:35.022344Z" - }, - "id": "mT-46EzJtq4m", - "tags": [] + "id": "IkXykhk7YLEK" }, - "outputs": [], "source": [ - "import json, os\n", - "from google.colab import files\n", + "## LangTest\n", "\n", - "if 'spark_jsl.json' not in os.listdir():\n", - " license_keys = files.upload()\n", - " os.rename(list(license_keys.keys())[0], 'spark_jsl.json')\n", + "**LangTest** is an open-source Python library designed to help developers deliver safe and effective Natural Language Processing (NLP) models. Whether you are using models from **John Snow Labs**, **Hugging Face**, **spaCy**, or LLMs from **OpenAI**, **Cohere**, **AI21**, **Hugging Face Inference API**, or **Azure-OpenAI**, LangTest has got you covered. It provides a comprehensive set of tests for evaluating Named Entity Recognition (NER), Text Classification, Question-Answering, and Summarization models. The library supports over 50 out-of-the-box tests, categorized into robustness, accuracy, bias, representation, and fairness.\n", "\n", - "with open('spark_jsl.json') as f:\n", - " license_keys = json.load(f)\n", "\n", - "# Defining license key-value pairs as local variables\n", - "locals().update(license_keys)\n", - "os.environ.update(license_keys)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "31DFuBt_khap" - }, - "source": [ - "**Installing Required Packages**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-28T06:52:37.035731Z", - "iopub.status.busy": "2023-07-28T06:52:37.035325Z", - "iopub.status.idle": "2023-07-28T06:52:42.734774Z", - "shell.execute_reply": "2023-07-28T06:52:42.734082Z", - "shell.execute_reply.started": "2023-07-28T06:52:37.035712Z" - }, - "id": "4-_1F3WZtqWk", - "tags": [] - }, - "outputs": [], - "source": [ - "# Installing pyspark and spark-nlp\n", - "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION\n", + "### Evaluation Metrics\n", "\n", - "# Installing Spark NLP Healthcare\n", - "! pip install --upgrade -q spark-nlp-jsl==$JSL_VERSION --extra-index-url https://pypi.johnsnowlabs.com/$SECRET\n", + "To thoroughly assess the performance and compare the **Robustness** and **Accuracy** of the `ner posology` and `med7` models, we will employ the LangTest Python library. LangTest provides a wide range of tests for evaluating NER models, including Robustness and Accuracy.\n", "\n", - "# Installing Spark NLP Display Library for visualization\n", - "! pip install -q spark-nlp-display" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZLTxPhkekhap" - }, - "source": [ - "**Setting up Spark NLP and Spark Session**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-28T06:52:42.736380Z", - "iopub.status.busy": "2023-07-28T06:52:42.735857Z", - "iopub.status.idle": "2023-07-28T06:52:42.753243Z", - "shell.execute_reply": "2023-07-28T06:52:42.752805Z", - "shell.execute_reply.started": "2023-07-28T06:52:42.736362Z" - }, - "id": "HHZwZFKZuNz9", - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning::Spark Session already created, some configs may not take.\n", - "Spark NLP Version : 4.4.4\n", - "Spark NLP_JSL Version : 4.4.4\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sparknlp\n", - "import sparknlp_jsl\n", + "#### Robustness Testing\n", "\n", - "from sparknlp.base import *\n", - "from sparknlp.annotator import *\n", - "from sparknlp_jsl.annotator import *\n", + "Model robustness can be described as the ability of a model to keep similar levels of accuracy, precision and recall when perturbations are made to the data it is predicting on. For example, in the case of NER, the goal is to understand how documents with typos or fully uppercased sentences affect the model's prediction performance compared to documents similar to those in the original training set.\n", "\n", - "from pyspark.sql import SparkSession\n", - "from pyspark.sql import functions as F\n", - "from pyspark.ml import Pipeline,PipelineModel\n", - "from pyspark.sql.types import StringType, IntegerType\n", "\n", - "import pandas as pd\n", - "pd.set_option('display.max_colwidth', 200)\n", + "**`Supported Robustness tests :`**
\n", "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", "\n", - "params = {\"spark.driver.memory\":\"16G\",\n", - " \"spark.kryoserializer.buffer.max\":\"2000M\",\n", - " \"spark.driver.maxResultSize\":\"2000M\"}\n", + "- **`uppercase`**: capitalization of the test set is turned into uppercase\n", "\n", - "spark = sparknlp_jsl.start(license_keys['SECRET'],params=params)\n", + "- **`lowercase`**: capitalization of the test set is turned into lowercase\n", "\n", - "print(\"Spark NLP Version :\", sparknlp.version())\n", - "print(\"Spark NLP_JSL Version :\", sparknlp_jsl.version())\n", + "- **`titlecase`**: capitalization of the test set is turned into title case\n", "\n", - "spark" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dWVS7L4Gkhaq" - }, - "source": [ - "The code above imports the required libraries, sets up the Spark Session, and initializes Spark NLP. Additionally, it configures parameters for Spark NLP, such as memory allocation and buffer size, to ensure optimal performance.\n", + "- **`add_punctuation`**: special characters at end of each sentence are replaced by other special characters, if no\n", + "special character at the end, one is added\n", "\n", - "After running this code, you'll have the Spark Session set up with Spark NLP and be ready to proceed with utilizing the `ner_posology_langtest` model for NLP tasks." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J7ooc9ieuyWe" - }, - "source": [ - "### Define Spark NLP pipeline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EpzJL39BygSY" - }, - "source": [ - "To learn about the Spark NLP pipeline, you can refer to the official documentation at [Spark NLP Pipeline Documentation](https://nlp.johnsnowlabs.com/docs/en/jsl/nlp_pipes)\n", + "- **`strip_punctuation`**: special characters are removed from the sentences (except if found in numbers, such as '2.5')\n", "\n", - "To process the text and extract the desired entities using the `ner_posology_langtest` model, you need to build an NLP pipeline. Run the following code to define the pipeline stages and apply the pipeline to the input text:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "VJhPiLvQ06-5", - "tags": [] - }, - "outputs": [], - "source": [ - "document_assembler = DocumentAssembler()\\\n", - " .setInputCol(\"text\")\\\n", - " .setOutputCol(\"document\")\n", + "- **`add_typo`**: typos are introduced in sentences\n", "\n", - "sentence_detector = SentenceDetector()\\\n", - " .setInputCols([\"document\"])\\\n", - " .setOutputCol(\"sentence\")\n", + "- **`add_contraction`**: contractions are added where possible (e.g. 'do not' contracted into 'don't')\n", "\n", - "tokenizer = Tokenizer()\\\n", - " .setInputCols([\"sentence\"])\\\n", - " .setOutputCol(\"token\")\n", + "- **`add_context`**: tokens are added at the beginning and at the end of the sentences\n", "\n", - "word_embeddings = WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n", - "\t.setInputCols([\"sentence\", \"token\"])\\\n", - "\t.setOutputCol(\"embeddings\")\n", + "- **`swap_entities`**: named entities replaced with same entity type with same token count from terminology\n", "\n", - "clinical_ner = MedicalNerModel.pretrained(\"ner_posology_langtest\",\"en\",\"clinical/models\")\\\n", - " .setInputCols([\"sentence\",\"token\",\"embeddings\"])\\\n", - " .setOutputCol(\"ner\")\n", + "- **`swap_cohyponyms`**: Named entities replaced with co-hyponym from the WordNet database\n", + "\n", + "- **`american_to_british`**: American English will be changed to British English\n", + "\n", + "- **`british_to_american`**: British English will be changed to American English\n", + "\n", + "- **`number_to_word`**: Converts numeric values in sentences to their equivalent verbal representation.\n", + "\n", + "- **`add_ocr_typo`**: Ocr typos are introduced in sentences\n", + "\n", + "- **`multiple_perturbations`** : Transforms the given sentences by applying multiple perturbations in a specific sequence.\n", + "\n", + "- **`add_speech_to_text_typo`**: Introduce common conversion errors from SSpeech to Text conversion.\n", + "\n", + "- **`add_abbreviation`**:Replaces words or expressions in texts with their abbreviations\n", + "\n", + "- **`adjective_synonym_swap`** : Transforms the adjectives in the given sentences to their synonyms.\n", + "\n", + "- **`adjective_antonym_swap`** : Transforms the adjectives in the given sentences to their antonyms.\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "\n", + "#### Accuracy Testing\n", + "\n", + "Accuracy testing is a crucial step in evaluating the performance of a machine learning model. It involves measuring how well the model can correctly predict outcomes on a test dataset, which it has not seen before. The accuracy of a model is determined by comparing its predicted output with the actual output. To support the accuracy testing process, several accuracy tests are available. These tests aim to evaluate various aspects of a model's performance both labelwise such as its precision, recall, F1 score and overall like micro F1 score, macro F1 score, and weighted F1 score.\n", + "\n", + "\n", + "**`Supported Accuracy tests :`**\n", + "\n", + "- **`min_precision_score`**: Determine if the actual precision score is less than the desired precision score.\n", + "\n", + "- **`min_recall_score`**: Determine if the actual recall score is less than the desired recall score.\n", "\n", - "ner_converter = NerConverter()\\\n", - " \t.setInputCols([\"sentence\", \"token\", \"ner\"])\\\n", - " \t.setOutputCol(\"ner_chunk\")\n", + "- **`min_f1_score`**: Determine if the actual f1 score is less than the desired f1 score.\n", "\n", - "nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])\n", + "- **`min_micro_f1_score`**: Determine if the actual micro-f1 score is less than the desired micro-f1 score.\n", "\n", - "model = nlp_pipeline.fit(spark.createDataFrame([[\"\"]]).toDF(\"text\"))\n", + "- **`min_macro_f1_score`**: Determine if the actual macro-f1 score is less than the desired macro-f1 score.\n", "\n", - "result = model.transform(spark.createDataFrame([['The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.']], [\"text\"]))" + "- **`min_weighted_f1_score`**: Determine if the actual min-weighted-f1 score is less than the desired min-weighted-f1 score.\n" ] }, { "cell_type": "markdown", - "metadata": { - "id": "tHYewAsMkhaq" - }, + "metadata": {}, "source": [ - "To access the extracted entities from the result, you can run the following code:" + "To use LangTest, you can install it using pip" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, "execution": { - "iopub.execute_input": "2023-07-27T11:39:30.705565Z", - "iopub.status.busy": "2023-07-27T11:39:30.704883Z", - "iopub.status.idle": "2023-07-27T11:39:34.046371Z", - "shell.execute_reply": "2023-07-27T11:39:34.045849Z", - "shell.execute_reply.started": "2023-07-27T11:39:30.705541Z" + "iopub.execute_input": "2023-08-19T16:47:52.689099Z", + "iopub.status.busy": "2023-08-19T16:47:52.688552Z", + "iopub.status.idle": "2023-08-19T16:48:03.368442Z", + "shell.execute_reply": "2023-08-19T16:48:03.367785Z", + "shell.execute_reply.started": "2023-08-19T16:47:52.689077Z" }, - "id": "we6MSfR3vGVG", - "outputId": "8045ff4f-c594-4bd8-ead3-32e7910dcda2", + "id": "28FmpLoiUkYq", "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Stage 14:=================================================> (6 + 1) / 7]\r" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "+--------------+---------+\n", - "| chunk|ner_label|\n", - "+--------------+---------+\n", - "| insulin| DRUG|\n", - "| Bactrim| DRUG|\n", - "| for 14 days| DURATION|\n", - "| Fragmin| DRUG|\n", - "| 5000 units| STRENGTH|\n", - "|subcutaneously| ROUTE|\n", - "| daily|FREQUENCY|\n", - "| Xenaderm| DRUG|\n", - "| topically| ROUTE|\n", - "| b.i.d|FREQUENCY|\n", - "| Lantus| DRUG|\n", - "| 40 units| DOSAGE|\n", - "|subcutaneously| ROUTE|\n", - "| at bedtime|FREQUENCY|\n", - "| OxyContin| DRUG|\n", - "| 30 mg| STRENGTH|\n", - "| p.o| ROUTE|\n", - "| folic acid| DRUG|\n", - "| 1 mg| STRENGTH|\n", - "| daily|FREQUENCY|\n", - "+--------------+---------+\n", - "only showing top 20 rows\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" + "Collecting langtest\n", + " Downloading langtest-1.3.0-py3-none-any.whl (59.9 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.9/59.9 MB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0mm\n", + "\u001b[?25hRequirement already satisfied: typing-extensions<4.6.0 in /opt/conda/lib/python3.10/site-packages (from langtest) (4.4.0)\n", + "Requirement already satisfied: pyyaml<7.0,>=6.0 in /opt/conda/lib/python3.10/site-packages (from langtest) (6.0)\n", + "Collecting pandas<3.0.0,>=2.0.3\n", + " Downloading pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m141.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hCollecting tqdm<5.0.0,>=4.65.0\n", + " Downloading tqdm-4.66.1-py3-none-any.whl (78 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.3/78.3 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: nest-asyncio<2.0.0,>=1.5.0 in /opt/conda/lib/python3.10/site-packages (from langtest) (1.5.6)\n", + "Collecting pydantic==1.10.6\n", + " Downloading pydantic-1.10.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m132.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting jsonlines<4.0.0,>=3.1.0\n", + " Downloading jsonlines-3.1.0-py3-none-any.whl (8.6 kB)\n", + "Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.10/site-packages (from jsonlines<4.0.0,>=3.1.0->langtest) (22.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas<3.0.0,>=2.0.3->langtest) (2.8.2)\n", + "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas<3.0.0,>=2.0.3->langtest) (2022.7)\n", + "Requirement already satisfied: numpy>=1.21.0 in /opt/conda/lib/python3.10/site-packages (from pandas<3.0.0,>=2.0.3->langtest) (1.21.6)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas<3.0.0,>=2.0.3->langtest) (2022.7.1)\n", + "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas<3.0.0,>=2.0.3->langtest) (1.16.0)\n", + "Installing collected packages: tqdm, pydantic, jsonlines, pandas, langtest\n", + " Attempting uninstall: tqdm\n", + " Found existing installation: tqdm 4.64.1\n", + " Uninstalling tqdm-4.64.1:\n", + " Successfully uninstalled tqdm-4.64.1\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 1.10.12\n", + " Uninstalling pydantic-1.10.12:\n", + " Successfully uninstalled pydantic-1.10.12\n", + " Attempting uninstall: pandas\n", + " Found existing installation: pandas 1.5.3\n", + " Uninstalling pandas-1.5.3:\n", + " Successfully uninstalled pandas-1.5.3\n", + "Successfully installed jsonlines-3.1.0 langtest-1.3.0 pandas-2.0.3 pydantic-1.10.6 tqdm-4.66.1\n" ] } ], "source": [ - "result.select(F.explode(F.arrays_zip(result.ner_chunk.result,\n", - " result.ner_chunk.metadata)).alias(\"cols\"))\\\n", - " .select(F.expr(\"cols['0']\").alias(\"chunk\"),\n", - " F.expr(\"cols['1']['entity']\").alias(\"ner_label\")).show()" + "# Installing LangTest version 1.3.0\n", + "!pip install langtest==1.3.0" ] }, { "cell_type": "markdown", "metadata": { - "id": "q3Vh9Gzxkhaq" + "id": "IGUmLOPFV3d5" }, "source": [ - "To visualize the extracted entities, you can use the `NerVisualizer` class from the `sparknlp_display` library. Run the following code:" + "### Harness and its Parameters\n", + "\n", + "The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-07-27T11:39:36.364576Z", - "iopub.status.busy": "2023-07-27T11:39:36.363944Z", - "iopub.status.idle": "2023-07-27T11:39:37.317866Z", - "shell.execute_reply": "2023-07-27T11:39:37.317108Z", - "shell.execute_reply.started": "2023-07-27T11:39:36.364552Z" + "iopub.execute_input": "2023-08-19T16:48:11.396739Z", + "iopub.status.busy": "2023-08-19T16:48:11.396149Z", + "iopub.status.idle": "2023-08-19T16:48:11.906049Z", + "shell.execute_reply": "2023-08-19T16:48:11.905541Z", + "shell.execute_reply.started": "2023-08-19T16:48:11.396716Z" }, - "id": "Q5O_yzQ7yd7g", - "outputId": "1e876f6f-e39e-4dfa-b33d-011dfb3ab995", + "id": "iMvcInxahaNu", "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " The patient is a 30-year-old female with a long history of insulin DRUG dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim DRUG for 14 days DURATION for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin DRUG 5000 units STRENGTH subcutaneously ROUTE daily FREQUENCY, Xenaderm DRUG to wounds topically ROUTE b.i.d FREQUENCY., Lantus DRUG 40 units DOSAGE subcutaneously ROUTE at bedtime FREQUENCY, OxyContin DRUG 30 mg STRENGTH p.o ROUTE. q.12 h., folic acid DRUG 1 mg STRENGTH daily FREQUENCY, levothyroxine DRUG 0.1 mg STRENGTH p.o ROUTE. daily FREQUENCY, Prevacid DRUG 30 mg STRENGTH daily FREQUENCY, Avandia DRUG 4 mg STRENGTH daily FREQUENCY, Norvasc DRUG 10 mg STRENGTH daily FREQUENCY, Lexapro DRUG 20 mg STRENGTH daily FREQUENCY, aspirin DRUG 81 mg STRENGTH daily FREQUENCY, Senna DRUG 2 DOSAGE tablets FORM p.o ROUTE. q FREQUENCY.a.m., Neurontin DRUG 400 mg STRENGTH p.o ROUTE. t.i.d FREQUENCY., Percocet DRUG 5/325 mg STRENGTH 2 DOSAGE tablets FORM q.4 h FREQUENCY. p.r.n., magnesium citrate DRUG 1 DOSAGE bottle FORM p.o ROUTE. p.r.n., sliding scale coverage insulin DRUG, Wellbutrin DRUG 100 mg STRENGTH p.o ROUTE. daily FREQUENCY, and Bactrim DS DRUG b.i.d FREQUENCY." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from sparknlp_display import NerVisualizer\n", + "from langtest import Harness" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iuUDuM_1V6ay" + }, + "source": [ + "It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n", "\n", - "visualiser = NerVisualizer()\n", + "Here is a list of the different parameters that can be passed to the Harness function:\n", "\n", - "visualiser.display(result = result.collect()[0] ,label_col = 'ner_chunk', document_col = 'document')" + "
\n", + "\n", + "\n", + "\n", + "| Parameter | Description |\n", + "| ------------- | ----------- |\n", + "| **task** | Task for which the model is to be evaluated (text-classification or ner) |\n", + "| **model** | Specifies the model(s) to be evaluated. Can be a dictionary or a list of dictionaries. Each dictionary should contain 'model' and 'hub' keys. If a path is specified, the dictionary must contain 'model' and 'hub' keys. |\n", + "| **data** | The data to be used for evaluation. A dictionary providing flexibility and options for data sources. It should include the following keys:
  • data_source (mandatory): The source of the data.
  • subset (optional): The subset of the data.
  • feature_column (optional): The column containing the features.
  • target_column (optional): The column containing the target labels.
  • split (optional): The data split to be used.
|\n", + "| **config** | Configuration for the tests to be performed, specified in the form of a YAML file. |\n", + "\n", + "\n", + "
\n", + "
" ] }, { "cell_type": "markdown", "metadata": { - "id": "iM4N5-PmUlza" + "id": "W413urBWWAhh" }, "source": [ - "# Evaluating Model Performance with LangTest" + "### Test Configuration\n", + "\n", + "Test configuration can be passed in the form of a YAML file as shown below or using .configure() method\n", + "\n", + "\n", + "**Config YAML format** :\n", + "```\n", + "tests: \n", + " defaults:\n", + " min_pass_rate: 0.65\n", + " robustness:\n", + " add_typo:\n", + " min_pass_rate: 0.66\n", + " uppercase:\n", + " min_pass_rate: 0.62\n", + " \n", + "```\n", + "\n", + "If config file is not present, we can also use the **.configure()** method to manually configure the harness to perform the needed tests." ] }, { "cell_type": "markdown", "metadata": { - "id": "KTdCaNvRkhar" + "id": "oBWW7s8X2Ttc" }, "source": [ - "To use LangTest, you can install it using pip" + "## Testing the ner_posology_langtest Model" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, 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databricks-cli, nlu, databricks-api, spark-nlp-display, johnsnowlabs\n", + " Attempting uninstall: spark-nlp\n", + " Found existing installation: spark-nlp 5.0.0\n", + " Uninstalling spark-nlp-5.0.0:\n", + " Successfully uninstalled spark-nlp-5.0.0\n", + " Attempting uninstall: pydantic\n", + " Found existing installation: pydantic 1.10.6\n", + " Uninstalling pydantic-1.10.6:\n", + " Successfully uninstalled pydantic-1.10.6\n", + " Attempting uninstall: spark-nlp-display\n", + " Found existing installation: spark-nlp-display 4.4\n", + " Uninstalling spark-nlp-display-4.4:\n", + " Successfully uninstalled spark-nlp-display-4.4\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "spark-ocr 4.3.0 requires pyspark==3.2.1, but you have pyspark 3.1.2 which is incompatible.\n", + "spark-ocr 4.3.0 requires spark-nlp==4.2.4, but you have spark-nlp 5.0.1 which is incompatible.\n", + "spark-nlp-jsl 5.0.0 requires spark-nlp==5.0.0, but you have spark-nlp 5.0.1 which is incompatible.\n", + "langtest 1.3.0 requires pydantic==1.10.6, but you have pydantic 1.10.11 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed databricks-api-0.9.0 databricks-cli-0.17.7 dataclasses-0.6 johnsnowlabs-5.0.1 nlu-4.2.2 pydantic-1.10.11 spark-nlp-5.0.1 spark-nlp-display-4.1\n" + ] + } + ], "source": [ - "!pip install langtest" + "# John Snow Labs setup\n", + "!pip install johnsnowlabs" ] }, { "cell_type": "markdown", - "metadata": { - "id": "IGUmLOPFV3d5" - }, + "metadata": {}, "source": [ - "# Harness and its Parameters\n", - "\n", - "The Harness class is a testing class for Natural Language Processing (NLP) models. It evaluates the performance of a NLP model on a given task using test data and generates a report with test results.Harness can be imported from the LangTest library in the following way." + "#### Define Spark NLP pipeline" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-07-28T06:53:38.757771Z", - "iopub.status.busy": "2023-07-28T06:53:38.757417Z", - "iopub.status.idle": "2023-07-28T06:53:39.332876Z", - "shell.execute_reply": "2023-07-28T06:53:39.332319Z", - "shell.execute_reply.started": "2023-07-28T06:53:38.757750Z" + "iopub.execute_input": "2023-08-19T16:48:30.888496Z", + "iopub.status.busy": "2023-08-19T16:48:30.887883Z", + "iopub.status.idle": "2023-08-19T16:48:36.263979Z", + "shell.execute_reply": "2023-08-19T16:48:36.263469Z", + "shell.execute_reply.started": "2023-08-19T16:48:30.888472Z" }, - "id": "iMvcInxahaNu", "tags": [] }, - "outputs": [], - "source": [ - "from langtest import Harness" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iuUDuM_1V6ay" - }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "embeddings_clinical download started this may take some time.\n", + "Approximate size to download 1.6 GB\n", + "[OK!]\n", + "ner_posology_langtest download started this may take some time.\n", + "[OK!]\n" + ] + } + ], "source": [ - "It imports the Harness class from within the module, that is designed to provide a blueprint or framework for conducting NLP testing, and that instances of the Harness class can be customized or configured for different testing scenarios or environments.\n", - "\n", - "Here is a list of the different parameters that can be passed to the Harness function:\n", - "\n", - "
\n", - "\n", + "document_assembler = DocumentAssembler()\\\n", + " .setInputCol(\"text\")\\\n", + " .setOutputCol(\"document\")\n", "\n", - "| Parameter | Description |\n", - "| - | - |\n", - "|**task** |Task for which the model is to be evaluated (text-classification or ner)|\n", - "|**model** |PipelineModel or path to a saved model or pretrained pipeline/model from hub.\n", - "|**data** |Path to the data that is to be used for evaluation. Can be .csv or .conll file in the CoNLL format\n", - "|**config** |Configuration for the tests to be performed, specified in form of a YAML file.\n", - "|**hub** |model hub to load from the path. Required if model param is passed as path.|\n", + "sentence_detector = SentenceDetector()\\\n", + " .setInputCols([\"document\"])\\\n", + " .setOutputCol(\"sentence\")\n", "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W413urBWWAhh" - }, - "source": [ - "### Test Configuration\n", + "tokenizer = Tokenizer()\\\n", + " .setInputCols([\"sentence\"])\\\n", + " .setOutputCol(\"token\")\n", "\n", - "Test configuration can be passed in the form of a YAML file as shown below or using .configure() method\n", + "word_embeddings = WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n", + "\t.setInputCols([\"sentence\", \"token\"])\\\n", + "\t.setOutputCol(\"embeddings\")\n", "\n", + "clinical_ner = MedicalNerModel.pretrained(\"ner_posology_langtest\")\\\n", + " .setInputCols([\"sentence\",\"token\",\"embeddings\"])\\\n", + " .setOutputCol(\"ner\")\n", "\n", - "**Config YAML format** :\n", - "```\n", - "tests:\n", - " defaults:\n", - " min_pass_rate: 0.65\n", - " bias:\n", - " replace_to_high_income_country:\n", - " min_pass_rate: 0.66\n", - " replace_to_low_income_country:\n", - " min_pass_rate: 0.60\n", + "ner_converter = NerConverterInternal()\\\n", + " \t.setInputCols([\"sentence\", \"token\", \"ner\"])\\\n", + " \t.setOutputCol(\"ner_chunk\")\n", "\n", - "```\n", + "nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])\n", "\n", - "If config file is not present, we can also use the **.configure()** method to manually configure the harness to perform the needed tests." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oBWW7s8X2Ttc" - }, - "source": [ - "# Testing Med7 model" + "ner_posology_langtest = nlp_pipeline.fit(spark.createDataFrame([[\"\"]]).toDF(\"text\"))" ] }, { @@ -1356,36 +1541,29 @@ "id": "a3HGhcBbkhas" }, "source": [ - "we have instantiated the Harness class to perform NER testing on the Med7 model. We have specified the test data, set the task to \"ner\", and provided the model name and hub information." + "#### Instantiate the Harness Class\n", + "We start by instantiating the Harness class and providing the necessary information for testing. In this case, we specify the test data, set the task to \"ner\", and provide the model name and hub information." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T06:54:09.057666Z", - "iopub.status.busy": "2023-07-28T06:54:09.057334Z", - "iopub.status.idle": "2023-07-28T06:54:14.491925Z", - "shell.execute_reply": "2023-07-28T06:54:14.491362Z", - "shell.execute_reply.started": "2023-07-28T06:54:09.057645Z" + "iopub.execute_input": "2023-08-19T16:56:11.361952Z", + "iopub.status.busy": "2023-08-19T16:56:11.361639Z", + "iopub.status.idle": "2023-08-19T16:56:12.400973Z", + "shell.execute_reply": "2023-08-19T16:56:12.400420Z", + "shell.execute_reply.started": "2023-08-19T16:56:11.361930Z" }, "id": "OGFtE1kDhaxE", "outputId": "ec4b156d-0635-41e7-a8fc-f87cf6b5459b", "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-07-28 06:54:11.998228: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1434,10 +1612,9 @@ ], "source": [ "harness = Harness(\n", - " data=\"testing/sample-pos.conll\",\n", " task = \"ner\",\n", - " model=\"en_core_med7_lg\",\n", - " hub = \"spacy\"\n", + " data={\"data_source\":\"sample-test.conll\"},\n", + " model={\"model\":ner_posology_langtest,\"hub\":\"johnsnowlabs\"}\n", " )" ] }, @@ -1447,23 +1624,23 @@ "id": "H6c3GpTobFX3" }, "source": [ - "### Configure the Tests\n", + "#### Configure the Tests\n", "We can use the .configure() method to manually configure the tests we want to perform." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T06:54:14.493405Z", - "iopub.status.busy": "2023-07-28T06:54:14.492880Z", - "iopub.status.idle": "2023-07-28T06:54:14.499319Z", - "shell.execute_reply": "2023-07-28T06:54:14.498919Z", - "shell.execute_reply.started": "2023-07-28T06:54:14.493385Z" + "iopub.execute_input": "2023-08-19T16:58:32.965907Z", + "iopub.status.busy": "2023-08-19T16:58:32.965384Z", + "iopub.status.idle": "2023-08-19T16:58:32.971182Z", + "shell.execute_reply": "2023-08-19T16:58:32.970741Z", + "shell.execute_reply.started": "2023-08-19T16:58:32.965888Z" }, "id": "JCd-xbkGhcJU", "outputId": "d165ab08-9505-4e61-fec9-11dd7ff0be1c", @@ -1473,31 +1650,26 @@ { "data": { "text/plain": [ - "{'tests': {'defaults': {'min_pass_rate': 0.65},\n", - " 'robustness': {'uppercase': {'min_pass_rate': 0.6},\n", - " 'lowercase': {'min_pass_rate': 0.6},\n", - " 'titlecase': {'min_pass_rate': 0.6},\n", - " 'add_punctuation': {'min_pass_rate': 0.6},\n", - " 'strip_punctuation': {'min_pass_rate': 0.6},\n", - " 'add_slangs': {'min_pass_rate': 0.6},\n", - " 'dyslexia_word_swap': {'min_pass_rate': 0.6},\n", - " 'add_abbreviation': {'min_pass_rate': 0.6},\n", - " 'add_speech_to_text_typo': {'min_pass_rate': 0.6},\n", - " 'number_to_word': {'min_pass_rate': 0.6},\n", - " 'add_ocr_typo': {'min_pass_rate': 0.6},\n", - " 'adjective_synonym_swap': {'min_pass_rate': 0.6}},\n", - " 'bias': {'replace_to_male_pronouns': {'min_pass_rate': 0.66},\n", - " 'replace_to_female_pronouns': {'min_pass_rate': 0.6},\n", - " 'replace_to_inter_racial_lastnames': {'min_pass_rate': 0.6},\n", - " 'replace_to_native_american_lastnames': {'min_pass_rate': 0.6},\n", - " 'replace_to_asian_lastnames': {'min_pass_rate': 0.6}},\n", - " 'accuracy': {'min_precision_score': {'min_score': 0.66},\n", - " 'min_recall_score': {'min_score': 0.6},\n", - " 'min_f1_score': {'min_score': 0.6},\n", - " 'min_micro_f1_score': {'min_score': 0.6}}}}" + "{'tests': {'defaults': {'min_pass_rate': 0.7},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.7},\n", + " 'lowercase': {'min_pass_rate': 0.7},\n", + " 'titlecase': {'min_pass_rate': 0.7},\n", + " 'add_punctuation': {'min_pass_rate': 0.7},\n", + " 'strip_punctuation': {'min_pass_rate': 0.7},\n", + " 'add_slangs': {'min_pass_rate': 0.7},\n", + " 'dyslexia_word_swap': {'min_pass_rate': 0.7},\n", + " 'add_abbreviation': {'min_pass_rate': 0.7},\n", + " 'add_speech_to_text_typo': {'min_pass_rate': 0.7},\n", + " 'number_to_word': {'min_pass_rate': 0.7},\n", + " 'add_ocr_typo': {'min_pass_rate': 0.7},\n", + " 'adjective_synonym_swap': {'min_pass_rate': 0.7}},\n", + " 'accuracy': {'min_precision_score': {'min_score': 0.7},\n", + " 'min_recall_score': {'min_score': 0.7},\n", + " 'min_f1_score': {'min_score': 0.7},\n", + " 'min_micro_f1_score': {'min_score': 0.7}}}}" ] }, - "execution_count": 14, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1505,32 +1677,25 @@ "source": [ "harness.configure(\n", "{\n", - " 'tests': {'defaults': {'min_pass_rate': 0.65},\n", - " 'robustness': {'uppercase': {'min_pass_rate': 0.60},\n", - " 'lowercase': {'min_pass_rate': 0.60},\n", - " 'titlecase':{'min_pass_rate': 0.60},\n", - " 'add_punctuation':{'min_pass_rate': 0.60},\n", - " 'strip_punctuation':{'min_pass_rate': 0.60},\n", - " 'add_slangs':{'min_pass_rate': 0.60},\n", - " 'dyslexia_word_swap':{'min_pass_rate': 0.60},\n", - " 'add_abbreviation':{'min_pass_rate': 0.60},\n", - " 'add_speech_to_text_typo':{'min_pass_rate': 0.60},\n", - " 'number_to_word':{'min_pass_rate': 0.60},\n", - " 'add_ocr_typo':{'min_pass_rate': 0.60},\n", - " 'adjective_synonym_swap':{'min_pass_rate': 0.60}\n", - " },\n", - " 'bias': {'replace_to_male_pronouns': {'min_pass_rate': 0.66},\n", - " 'replace_to_female_pronouns':{'min_pass_rate': 0.60},\n", - " 'replace_to_inter_racial_lastnames':{'min_pass_rate': 0.60},\n", - " 'replace_to_native_american_lastnames':{'min_pass_rate': 0.60},\n", - " 'replace_to_asian_lastnames':{'min_pass_rate': 0.60},\n", + " 'tests': {'defaults': {'min_pass_rate': 0.70},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.70},\n", + " 'lowercase': {'min_pass_rate': 0.70},\n", + " 'titlecase':{'min_pass_rate': 0.70},\n", + " 'add_punctuation':{'min_pass_rate': 0.70},\n", + " 'strip_punctuation':{'min_pass_rate': 0.70},\n", + " 'add_slangs':{'min_pass_rate': 0.70},\n", + " 'dyslexia_word_swap':{'min_pass_rate': 0.70},\n", + " 'add_abbreviation':{'min_pass_rate': 0.70},\n", + " 'add_speech_to_text_typo':{'min_pass_rate': 0.70},\n", + " 'number_to_word':{'min_pass_rate': 0.70},\n", + " 'add_ocr_typo':{'min_pass_rate': 0.70},\n", + " 'adjective_synonym_swap':{'min_pass_rate': 0.70}\n", " },\n", - " 'accuracy': {'min_precision_score': {'min_score': 0.66},\n", - " 'min_recall_score':{'min_score': 0.60},\n", - " 'min_f1_score':{'min_score': 0.60},\n", - " 'min_micro_f1_score':{'min_score': 0.60}\n", - " }\n", - "\n", + " 'accuracy': {'min_precision_score': {'min_score': 0.70},\n", + " 'min_recall_score':{'min_score': 0.70},\n", + " 'min_f1_score':{'min_score': 0.70},\n", + " 'min_micro_f1_score':{'min_score': 0.70}\n", + " }\n", " }\n", " }\n", ")" @@ -1542,7 +1707,7 @@ "id": "hZMmcgTneDzP" }, "source": [ - "Here we have configured the harness to perform robustness, bias and accuracy tests. For each test category, we have specified the minimum pass rates and additional parameters where applicable." + "Here we have configured the harness to perform robustness and Accuracy tests. For each test category, we have specified the minimum pass rates and additional parameters where applicable." ] }, { @@ -1551,22 +1716,19 @@ "id": "6rBctpJjbQug" }, "source": [ - "### Generating the test cases." + "#### Generating the test cases." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T06:54:14.500103Z", - "iopub.status.busy": "2023-07-28T06:54:14.499857Z", - "iopub.status.idle": "2023-07-28T07:01:47.268904Z", - "shell.execute_reply": "2023-07-28T07:01:47.268256Z", - "shell.execute_reply.started": "2023-07-28T06:54:14.500088Z" + "iopub.execute_input": "2023-08-19T16:58:35.690753Z", + "iopub.status.busy": "2023-08-19T16:58:35.690221Z" }, "id": "n6GnWeSqp5KN", "outputId": "e6466a12-aff2-4b5b-f76f-0b61c6606d49", @@ -1577,16 +1739,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "Generating testcases...: 100%|██████████| 3/3 [00:00<00:00, 17747.41it/s]\n" + "Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 13662.23it/s]\n" ] - }, - { - "data": { - "text/plain": [] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -1604,182 +1758,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 554 }, - "execution": { - "iopub.execute_input": "2023-07-28T07:01:47.270711Z", - "iopub.status.busy": "2023-07-28T07:01:47.270324Z", - "iopub.status.idle": "2023-07-28T07:01:47.371454Z", - "shell.execute_reply": "2023-07-28T07:01:47.370891Z", - "shell.execute_reply.started": "2023-07-28T07:01:47.270694Z" - }, "id": "1XSV45jNqIBO", "outputId": "39bd645d-5e5d-48af-8c1c-f40c942d8877", "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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categorytest_typeoriginaltest_case
0robustnessuppercaseOnce adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...
1robustnessuppercaseOne applicator full of VANDAZOLE administered intravaginally once a day for 5 days .ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .
2robustnessuppercaseBecause of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...
3robustnessuppercaseDOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...
4robustnessuppercaseDirections For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...
...............
25520accuracymin_f1_score-DOSAGE
25521accuracymin_f1_score-FREQUENCY
25522accuracymin_f1_score-DRUG
25523accuracymin_f1_score-O
25524accuracymin_micro_f1_score-micro
\n", - "

25525 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " category test_type \\\n", - "0 robustness uppercase \n", - "1 robustness uppercase \n", - "2 robustness uppercase \n", - "3 robustness uppercase \n", - "4 robustness uppercase \n", - "... ... ... \n", - "25520 accuracy min_f1_score \n", - "25521 accuracy min_f1_score \n", - "25522 accuracy min_f1_score \n", - "25523 accuracy min_f1_score \n", - "25524 accuracy min_micro_f1_score \n", - "\n", - " original \\\n", - "0 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", - "1 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", - "2 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", - "3 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "4 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", - "... ... \n", - "25520 - \n", - "25521 - \n", - "25522 - \n", - "25523 - \n", - "25524 - \n", - "\n", - " test_case \n", - "0 ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA... \n", - "1 ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS . \n", - "2 BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR... \n", - "3 DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE... \n", - "4 DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI... \n", - "... ... \n", - "25520 DOSAGE \n", - "25521 FREQUENCY \n", - "25522 DRUG \n", - "25523 O \n", - "25524 micro \n", - "\n", - "[25525 rows x 4 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "harness.testcases()" ] @@ -1799,22 +1788,18 @@ "id": "tE2TafEFbZ8R" }, "source": [ - "### Running the tests" + "#### Running the tests" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T07:01:47.378306Z", - "iopub.status.busy": "2023-07-28T07:01:47.378037Z", - "iopub.status.idle": "2023-07-28T07:11:34.380103Z", - "shell.execute_reply": "2023-07-28T07:11:34.379512Z", - "shell.execute_reply.started": "2023-07-28T07:01:47.378290Z" + "iopub.status.idle": "2023-08-19T17:44:34.800347Z" }, "id": "-K6XPisLqQOi", "outputId": "34ae6c84-112c-44d6-980c-bf66b26c874c", @@ -1825,14 +1810,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Running testcases... : 100%|██████████| 25525/25525 [09:46<00:00, 43.49it/s]\n" + "Running testcases... : 100%|██████████| 18025/18025 [38:22<00:00, 7.83it/s] \n" ] }, { "data": { "text/plain": [] }, - "execution_count": 17, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1856,23 +1841,21 @@ "id": "_s8jEfgvcJ8m" }, "source": [ - "### Generated Results" + "#### Generated Results" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 782 }, "execution": { - "iopub.execute_input": "2023-07-28T07:11:34.381022Z", - "iopub.status.busy": "2023-07-28T07:11:34.380784Z", - "iopub.status.idle": "2023-07-28T07:11:36.549893Z", - "shell.execute_reply": "2023-07-28T07:11:36.549404Z", - "shell.execute_reply.started": "2023-07-28T07:11:34.381005Z" + "iopub.execute_input": "2023-08-19T17:44:34.805198Z", + "iopub.status.busy": "2023-08-19T17:44:34.805046Z", + "iopub.status.idle": "2023-08-19T17:44:54.171718Z" }, "id": "fhvmZ1eOqRkr", "outputId": "ff59a31e-496d-4b37-d3b2-098fca2d7276", @@ -1916,9 +1899,9 @@ " uppercase\n", " Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...\n", " ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...\n", - " amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY\n", - " AMIODARONE HYDROCHLORIDE DOSE: DRUG\n", - " False\n", + " amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH\n", + " AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH\n", + " True\n", " \n", " \n", " 1\n", @@ -1926,9 +1909,9 @@ " uppercase\n", " One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .\n", " ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .\n", - " One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION\n", - " VANDAZOLE: DRUG, FOR 5 DAYS: DURATION\n", - " False\n", + " VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION\n", + " VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION\n", + " True\n", " \n", " \n", " 2\n", @@ -1936,8 +1919,8 @@ " uppercase\n", " Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...\n", " BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...\n", - " Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH\n", - " USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE\n", + " Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY\n", + " POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH\n", " False\n", " \n", " \n", @@ -1946,9 +1929,9 @@ " uppercase\n", " DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...\n", " DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...\n", - " hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG\n", - " \n", - " False\n", + " Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG\n", + " SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG\n", + " True\n", " \n", " \n", " 4\n", @@ -1956,9 +1939,9 @@ " uppercase\n", " Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...\n", " DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...\n", + " at least every 2 hours: FREQUENCY\n", " \n", - " \n", - " True\n", + " False\n", " \n", " \n", " ...\n", @@ -1971,58 +1954,58 @@ " ...\n", " \n", " \n", - " 25520\n", + " 18020\n", " accuracy\n", " min_f1_score\n", " -\n", - " DOSAGE\n", - " 0.6\n", - " 0.357143\n", - " False\n", + " DURATION\n", + " 0.7\n", + " 0.860147\n", + " True\n", " \n", " \n", - " 25521\n", + " 18021\n", " accuracy\n", " min_f1_score\n", " -\n", - " FREQUENCY\n", - " 0.6\n", - " 0.771729\n", + " ROUTE\n", + " 0.7\n", + " 0.878648\n", " True\n", " \n", " \n", - " 25522\n", + " 18022\n", " accuracy\n", " min_f1_score\n", " -\n", - " DRUG\n", - " 0.6\n", - " 0.775194\n", + " DOSAGE\n", + " 0.7\n", + " 0.707101\n", " True\n", " \n", " \n", - " 25523\n", + " 18023\n", " accuracy\n", " min_f1_score\n", " -\n", - " O\n", - " 0.6\n", - " 0.962597\n", + " FREQUENCY\n", + " 0.7\n", + " 0.925128\n", " True\n", " \n", " \n", - " 25524\n", + " 18024\n", " accuracy\n", " min_micro_f1_score\n", " -\n", " micro\n", - " 0.6\n", - " 0.932656\n", + " 0.7\n", + " 0.965413\n", " True\n", " \n", " \n", "\n", - "

25525 rows × 7 columns

\n", + "

18025 rows × 7 columns

\n", "" ], "text/plain": [ @@ -2033,11 +2016,11 @@ "3 robustness uppercase \n", "4 robustness uppercase \n", "... ... ... \n", - "25520 accuracy min_f1_score \n", - "25521 accuracy min_f1_score \n", - "25522 accuracy min_f1_score \n", - "25523 accuracy min_f1_score \n", - "25524 accuracy min_micro_f1_score \n", + "18020 accuracy min_f1_score \n", + "18021 accuracy min_f1_score \n", + "18022 accuracy min_f1_score \n", + "18023 accuracy min_f1_score \n", + "18024 accuracy min_micro_f1_score \n", "\n", " original \\\n", "0 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", @@ -2046,11 +2029,11 @@ "3 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", "4 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", "... ... \n", - "25520 - \n", - "25521 - \n", - "25522 - \n", - "25523 - \n", - "25524 - \n", + "18020 - \n", + "18021 - \n", + "18022 - \n", + "18023 - \n", + "18024 - \n", "\n", " test_case \\\n", "0 ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA... \n", @@ -2059,42 +2042,55 @@ "3 DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE... \n", "4 DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI... \n", "... ... \n", - "25520 DOSAGE \n", - "25521 FREQUENCY \n", - "25522 DRUG \n", - "25523 O \n", - "25524 micro \n", + "18020 DURATION \n", + "18021 ROUTE \n", + "18022 DOSAGE \n", + "18023 FREQUENCY \n", + "18024 micro \n", + "\n", + " expected_result \\\n", + "0 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", + "1 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", + "2 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", + "3 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", + "4 at least every 2 hours: FREQUENCY \n", + "... ... \n", + "18020 0.7 \n", + "18021 0.7 \n", + "18022 0.7 \n", + "18023 0.7 \n", + "18024 0.7 \n", "\n", - " expected_result \\\n", - "0 amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY \n", - "1 One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION \n", - "2 Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH \n", - "3 hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG \n", - "4 \n", - "... ... \n", - "25520 0.6 \n", - "25521 0.6 \n", - "25522 0.6 \n", - "25523 0.6 \n", - "25524 0.6 \n", + " actual_result \\\n", + "0 AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH \n", + "1 VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION \n", + "2 POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH \n", + "3 SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG \n", + "4 \n", + "... ... \n", + "18020 0.860147 \n", + "18021 0.878648 \n", + "18022 0.707101 \n", + "18023 0.925128 \n", + "18024 0.965413 \n", "\n", - " actual_result pass \n", - "0 AMIODARONE HYDROCHLORIDE DOSE: DRUG False \n", - "1 VANDAZOLE: DRUG, FOR 5 DAYS: DURATION False \n", - "2 USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE False \n", - "3 False \n", - "4 True \n", - "... ... ... \n", - "25520 0.357143 False \n", - "25521 0.771729 True \n", - "25522 0.775194 True \n", - "25523 0.962597 True \n", - "25524 0.932656 True \n", + " pass \n", + "0 True \n", + "1 True \n", + "2 False \n", + "3 True \n", + "4 False \n", + "... ... \n", + "18020 True \n", + "18021 True \n", + "18022 True \n", + "18023 True \n", + "18024 True \n", "\n", - "[25525 rows x 7 columns]" + "[18025 rows x 7 columns]" ] }, - "execution_count": 18, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -2114,14 +2110,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2023-07-28T07:11:36.550866Z", - "iopub.status.busy": "2023-07-28T07:11:36.550533Z", - "iopub.status.idle": "2023-07-28T07:11:37.254156Z", - "shell.execute_reply": "2023-07-28T07:11:37.253494Z", - "shell.execute_reply.started": "2023-07-28T07:11:36.550851Z" + "iopub.execute_input": "2023-08-19T17:44:54.176060Z", + "iopub.status.busy": "2023-08-19T17:44:54.175926Z", + "iopub.status.idle": "2023-08-19T17:44:58.645696Z" }, "id": "XQsHCqaZ6JIE", "tags": [] @@ -2137,23 +2131,21 @@ "id": "2s5W6kPBdGOg" }, "source": [ - "### Generated Results For robustness" + "#### Generated Results For robustness" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "execution": { - "iopub.execute_input": "2023-07-28T07:11:37.255341Z", - "iopub.status.busy": "2023-07-28T07:11:37.254904Z", - "iopub.status.idle": "2023-07-28T07:11:37.266289Z", - "shell.execute_reply": "2023-07-28T07:11:37.265833Z", - "shell.execute_reply.started": "2023-07-28T07:11:37.255326Z" + "iopub.execute_input": "2023-08-19T17:44:58.650040Z", + "iopub.status.busy": "2023-08-19T17:44:58.649908Z", + "iopub.status.idle": "2023-08-19T17:44:58.660440Z" }, "id": "G1wEpvu26LB6", "outputId": "ec38fd53-e2c9-4d6b-b9b3-f4df1c1bfa50", @@ -2197,9 +2189,9 @@ " uppercase\n", " Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...\n", " ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...\n", - " amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY\n", - " AMIODARONE HYDROCHLORIDE DOSE: DRUG\n", - " False\n", + " amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH\n", + " AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH\n", + " True\n", " \n", " \n", " 1\n", @@ -2207,9 +2199,9 @@ " uppercase\n", " One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .\n", " ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .\n", - " One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION\n", - " VANDAZOLE: DRUG, FOR 5 DAYS: DURATION\n", - " False\n", + " VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION\n", + " VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION\n", + " True\n", " \n", " \n", " 2\n", @@ -2217,8 +2209,8 @@ " uppercase\n", " Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...\n", " BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...\n", - " Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH\n", - " USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE\n", + " Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY\n", + " POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH\n", " False\n", " \n", " \n", @@ -2227,9 +2219,9 @@ " uppercase\n", " DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...\n", " DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...\n", - " hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG\n", - " \n", - " False\n", + " Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG\n", + " SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG\n", + " True\n", " \n", " \n", " 4\n", @@ -2237,9 +2229,9 @@ " uppercase\n", " Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...\n", " DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...\n", + " at least every 2 hours: FREQUENCY\n", " \n", - " \n", - " True\n", + " False\n", " \n", " \n", " ...\n", @@ -2267,8 +2259,8 @@ " adjective_synonym_swap\n", " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", - " Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG\n", - " Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG\n", + " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", + " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", " True\n", " \n", " \n", @@ -2277,8 +2269,8 @@ " adjective_synonym_swap\n", " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).\n", " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the convenient diluent ( see COMPATIBILITY AND STABILITY : ).\n", - " Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM\n", - " Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM\n", + " ceftriaxone: DRUG, powder: FORM\n", + " ceftriaxone: DRUG, powder: FORM\n", " True\n", " \n", " \n", @@ -2286,7 +2278,7 @@ " robustness\n", " adjective_synonym_swap\n", " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", - " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences serious audacious or all audacious accompanied by constitutional findings [see Warnings and Precautions ( 5...\n", + " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences relentless audacious or either audacious accompanied by constitutional findings [see Warnings and Precautio...\n", " nevirapine: DRUG\n", " nevirapine: DRUG\n", " True\n", @@ -2296,7 +2288,7 @@ " robustness\n", " adjective_synonym_swap\n", " For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", - " For intramuscular administration , use a needle great enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", + " For intramuscular administration , use a needle deep enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", " \n", " \n", " True\n", @@ -2343,339 +2335,76 @@ "17995 The sooner you take emergency contraception , the superior it works . \n", "17996 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", "17997 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the convenient diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "17998 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences serious audacious or all audacious accompanied by constitutional findings [see Warnings and Precautions ( 5... \n", - "17999 For intramuscular administration , use a needle great enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", - "\n", - " expected_result \\\n", - "0 amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY \n", - "1 One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION \n", - "2 Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH \n", - "3 hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG \n", - "4 \n", - "... ... \n", - "17995 \n", - "17996 Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG \n", - "17997 Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM \n", - "17998 nevirapine: DRUG \n", - "17999 \n", - "\n", - " actual_result pass \n", - "0 AMIODARONE HYDROCHLORIDE DOSE: DRUG False \n", - "1 VANDAZOLE: DRUG, FOR 5 DAYS: DURATION False \n", - "2 USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE False \n", - "3 False \n", - "4 True \n", - "... ... ... \n", - "17995 True \n", - "17996 Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG True \n", - "17997 Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM True \n", - "17998 nevirapine: DRUG True \n", - "17999 True \n", - "\n", - "[18000 rows x 7 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df[\"category\"]==\"robustness\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HWINhnNGdMvT" - }, - "source": [ - "### Generated Results For bias" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "execution": { - "iopub.execute_input": "2023-07-28T07:11:37.267238Z", - "iopub.status.busy": "2023-07-28T07:11:37.266871Z", - "iopub.status.idle": "2023-07-28T07:11:37.354046Z", - "shell.execute_reply": "2023-07-28T07:11:37.353446Z", - "shell.execute_reply.started": "2023-07-28T07:11:37.267216Z" - }, - "id": "lU0YyF8a6bqI", - "outputId": "6ceaa726-631a-4a94-8d6b-4834c70b8a0e", - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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categorytest_typeoriginaltest_caseexpected_resultactual_resultpass
18000biasreplace_to_male_pronounsOnce adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCYamiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCYTrue
18001biasreplace_to_male_pronounsOne applicator full of VANDAZOLE administered intravaginally once a day for 5 days .One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATIONOne: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATIONTrue
18002biasreplace_to_male_pronounsBecause of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTHPotassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTHTrue
18003biasreplace_to_male_pronounsDOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUGhydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUGTrue
18004biasreplace_to_male_pronounsDirections For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...True
........................
25495biasreplace_to_asian_lastnamesThe sooner you take emergency contraception , the better it works .The sooner you take emergency contraception , the better it works .True
25496biasreplace_to_asian_lastnamesOmeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUGOmeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUGTrue
25497biasreplace_to_asian_lastnamesDIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORMReconstitute: DRUG, ceftriaxone: DRUG, powder: FORMTrue
25498biasreplace_to_asian_lastnames2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .2.4 Dosage Adjustment Patients with Tiu Discontinue nevirapine if a patient experiences Zheng Bhola or any Bhola accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .nevirapine: DRUGnevirapine: DRUGTrue
25499biasreplace_to_asian_lastnamesFor intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .True
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"18003 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "18004 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", - "... ... \n", - "25495 The sooner you take emergency contraception , the better it works . \n", - "25496 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "25497 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "25498 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "25499 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", - "\n", - " test_case \\\n", - "18000 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", - "18001 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", - "18002 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", - "18003 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "18004 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", - "... ... \n", - "25495 The sooner you take emergency contraception , the better it works . \n", - "25496 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "25497 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "25498 2.4 Dosage Adjustment Patients with Tiu Discontinue nevirapine if a patient experiences Zheng Bhola or any Bhola accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "25499 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", - "\n", - " expected_result \\\n", - "18000 amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY \n", - 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"18001 One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION \n", - "18002 Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH \n", - "18003 hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG \n", - "18004 \n", - "... ... \n", - "25495 \n", - "25496 Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG \n", - "25497 Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM \n", - "25498 nevirapine: DRUG \n", - "25499 \n", + " expected_result \\\n", + "0 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", + "1 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", + "2 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", + "3 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", + "4 at least every 2 hours: FREQUENCY \n", + "... ... \n", + "17995 \n", + "17996 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", + "17997 ceftriaxone: DRUG, powder: FORM \n", + "17998 nevirapine: DRUG \n", + "17999 \n", + "\n", + " actual_result \\\n", + "0 AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH \n", + "1 VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION \n", + "2 POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH \n", + "3 SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG \n", + "4 \n", + "... ... \n", + "17995 \n", + "17996 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", + "17997 ceftriaxone: DRUG, powder: FORM \n", + "17998 nevirapine: DRUG \n", + "17999 \n", "\n", - 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" 25512\n", + " 18014\n", " accuracy\n", " min_recall_score\n", " -\n", " DOSAGE\n", - " 0.6\n", - " 0.297619\n", + " 0.7\n", + " 0.678977\n", " False\n", " \n", " \n", - " 25513\n", + " 18015\n", " accuracy\n", " min_recall_score\n", " -\n", " FREQUENCY\n", - " 0.6\n", - " 0.717584\n", + " 0.7\n", + " 0.911725\n", " True\n", " \n", " \n", - " 25514\n", + " 18016\n", " accuracy\n", - " min_recall_score\n", + " min_f1_score\n", " -\n", " DRUG\n", - " 0.6\n", - " 0.69735\n", - " True\n", - " \n", - " \n", - " 25515\n", - " accuracy\n", - " min_recall_score\n", - " -\n", - " O\n", - " 0.6\n", - " 0.977315\n", + " 0.7\n", + " 0.923308\n", " True\n", " \n", " \n", - " 25516\n", + " 18017\n", " accuracy\n", " min_f1_score\n", " -\n", - " DURATION\n", - " 0.6\n", - " 0.66426\n", + " O\n", + " 0.7\n", + " 0.980428\n", " True\n", " \n", " \n", - " 25517\n", + " 18018\n", " accuracy\n", " min_f1_score\n", " -\n", " FORM\n", - " 0.6\n", - " 0.679912\n", + " 0.7\n", + " 0.85758\n", " True\n", " \n", " \n", - " 25518\n", + " 18019\n", " accuracy\n", " min_f1_score\n", " -\n", " STRENGTH\n", - " 0.6\n", - " 0.823315\n", + " 0.7\n", + " 0.886411\n", " True\n", " \n", " \n", - " 25519\n", + " 18020\n", " accuracy\n", " min_f1_score\n", " -\n", - " ROUTE\n", - " 0.6\n", - " 0.669291\n", + " DURATION\n", + " 0.7\n", + " 0.860147\n", " True\n", " \n", " \n", - " 25520\n", - " accuracy\n", - " min_f1_score\n", - " -\n", - " DOSAGE\n", - " 0.6\n", - " 0.357143\n", - " False\n", - " \n", - " \n", - " 25521\n", + " 18021\n", " accuracy\n", " min_f1_score\n", " -\n", - " FREQUENCY\n", - " 0.6\n", - " 0.771729\n", + " ROUTE\n", + " 0.7\n", + " 0.878648\n", " True\n", " \n", " \n", - " 25522\n", + " 18022\n", " accuracy\n", " min_f1_score\n", " -\n", - " DRUG\n", - " 0.6\n", - " 0.775194\n", + " DOSAGE\n", + " 0.7\n", + " 0.707101\n", " True\n", " \n", " \n", - " 25523\n", + " 18023\n", " accuracy\n", " min_f1_score\n", " -\n", - " O\n", - " 0.6\n", - " 0.962597\n", + " FREQUENCY\n", + " 0.7\n", + " 0.925128\n", " True\n", " \n", " \n", - " 25524\n", + " 18024\n", " accuracy\n", " min_micro_f1_score\n", " -\n", " micro\n", - " 0.6\n", - " 0.932656\n", + " 0.7\n", + " 0.965413\n", " True\n", " \n", " \n", @@ -2966,61 +2695,61 @@ ], "text/plain": [ " category test_type original test_case expected_result \\\n", - "25500 accuracy min_precision_score - DURATION 0.66 \n", - "25501 accuracy min_precision_score - FORM 0.66 \n", - "25502 accuracy min_precision_score - STRENGTH 0.66 \n", - "25503 accuracy min_precision_score - ROUTE 0.66 \n", - "25504 accuracy min_precision_score - DOSAGE 0.66 \n", - "25505 accuracy min_precision_score - FREQUENCY 0.66 \n", - "25506 accuracy min_precision_score - DRUG 0.66 \n", - "25507 accuracy min_precision_score - O 0.66 \n", - "25508 accuracy min_recall_score - DURATION 0.6 \n", - "25509 accuracy min_recall_score - FORM 0.6 \n", - "25510 accuracy min_recall_score - STRENGTH 0.6 \n", - "25511 accuracy min_recall_score - ROUTE 0.6 \n", - "25512 accuracy min_recall_score - DOSAGE 0.6 \n", - "25513 accuracy min_recall_score - FREQUENCY 0.6 \n", - "25514 accuracy min_recall_score - DRUG 0.6 \n", - "25515 accuracy min_recall_score - O 0.6 \n", - "25516 accuracy min_f1_score - DURATION 0.6 \n", - "25517 accuracy min_f1_score - FORM 0.6 \n", - "25518 accuracy min_f1_score - STRENGTH 0.6 \n", - "25519 accuracy min_f1_score - ROUTE 0.6 \n", - "25520 accuracy min_f1_score - DOSAGE 0.6 \n", - "25521 accuracy min_f1_score - FREQUENCY 0.6 \n", - "25522 accuracy min_f1_score - DRUG 0.6 \n", - "25523 accuracy min_f1_score - O 0.6 \n", - "25524 accuracy min_micro_f1_score - micro 0.6 \n", + "18000 accuracy min_precision_score - DRUG 0.7 \n", + "18001 accuracy min_precision_score - O 0.7 \n", + "18002 accuracy min_precision_score - FORM 0.7 \n", + "18003 accuracy min_precision_score - STRENGTH 0.7 \n", + "18004 accuracy min_precision_score - DURATION 0.7 \n", + "18005 accuracy min_precision_score - ROUTE 0.7 \n", + "18006 accuracy min_precision_score - DOSAGE 0.7 \n", + "18007 accuracy min_precision_score - FREQUENCY 0.7 \n", + "18008 accuracy min_recall_score - DRUG 0.7 \n", + "18009 accuracy min_recall_score - O 0.7 \n", + "18010 accuracy min_recall_score - FORM 0.7 \n", + "18011 accuracy min_recall_score - STRENGTH 0.7 \n", + "18012 accuracy min_recall_score - DURATION 0.7 \n", + "18013 accuracy min_recall_score - ROUTE 0.7 \n", + "18014 accuracy min_recall_score - DOSAGE 0.7 \n", + "18015 accuracy min_recall_score - FREQUENCY 0.7 \n", + "18016 accuracy min_f1_score - DRUG 0.7 \n", + "18017 accuracy min_f1_score - O 0.7 \n", + "18018 accuracy min_f1_score - FORM 0.7 \n", + "18019 accuracy min_f1_score - STRENGTH 0.7 \n", + "18020 accuracy min_f1_score - DURATION 0.7 \n", + "18021 accuracy min_f1_score - ROUTE 0.7 \n", + "18022 accuracy min_f1_score - DOSAGE 0.7 \n", + "18023 accuracy min_f1_score - FREQUENCY 0.7 \n", + "18024 accuracy min_micro_f1_score - micro 0.7 \n", "\n", " actual_result pass \n", - "25500 0.779661 True \n", - "25501 0.836957 True \n", - "25502 0.820327 True \n", - "25503 0.858586 True \n", - "25504 0.446429 False \n", - "25505 0.834711 True \n", - "25506 0.8726 True \n", - "25507 0.948315 True \n", - "25508 0.578616 False \n", - "25509 0.572491 False \n", - "25510 0.826325 True \n", - "25511 0.548387 False \n", - "25512 0.297619 False \n", - "25513 0.717584 True \n", - "25514 0.69735 True \n", - "25515 0.977315 True \n", - "25516 0.66426 True \n", - "25517 0.679912 True \n", - "25518 0.823315 True \n", - "25519 0.669291 True \n", - "25520 0.357143 False \n", - "25521 0.771729 True \n", - "25522 0.775194 True \n", - "25523 0.962597 True \n", - "25524 0.932656 True " + "18000 0.928427 True \n", + "18001 0.976522 True \n", + "18002 0.872274 True \n", + "18003 0.904233 True \n", + "18004 0.91704 True \n", + "18005 0.925566 True \n", + "18006 0.737654 True \n", + "18007 0.938931 True \n", + "18008 0.918245 True \n", + "18009 0.984364 True \n", + "18010 0.843373 True \n", + "18011 0.869278 True \n", + "18012 0.809901 True \n", + "18013 0.836257 True \n", + "18014 0.678977 False \n", + "18015 0.911725 True \n", + "18016 0.923308 True \n", + "18017 0.980428 True \n", + "18018 0.85758 True \n", + "18019 0.886411 True \n", + "18020 0.860147 True \n", + "18021 0.878648 True \n", + "18022 0.707101 True \n", + "18023 0.925128 True \n", + "18024 0.965413 True " ] }, - "execution_count": 22, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -3035,23 +2764,21 @@ "id": "O0hOxsQUdbd7" }, "source": [ - "### Report of the tests" + "#### Report of the tests" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 740 }, "execution": { - "iopub.execute_input": "2023-07-28T07:11:37.434701Z", - "iopub.status.busy": "2023-07-28T07:11:37.434534Z", - "iopub.status.idle": "2023-07-28T07:11:38.056626Z", - "shell.execute_reply": "2023-07-28T07:11:38.056039Z", - "shell.execute_reply.started": "2023-07-28T07:11:37.434686Z" + "iopub.execute_input": "2023-08-19T17:44:58.721264Z", + "iopub.status.busy": "2023-08-19T17:44:58.720952Z", + "iopub.status.idle": "2023-08-19T17:45:02.815650Z" }, "id": "HFlUNIELqTPr", "outputId": "9f06ff70-246e-443f-cbfe-3e74bc39efe2", @@ -3093,210 +2820,160 @@ " 0\n", " robustness\n", " uppercase\n", - " 907\n", - " 593\n", - " 40%\n", - " 60%\n", - " False\n", + " 356\n", + " 1144\n", + " 76%\n", + " 70%\n", + " True\n", " \n", " \n", " 1\n", " robustness\n", " lowercase\n", - " 141\n", - " 1359\n", - " 91%\n", - " 60%\n", + " 190\n", + " 1310\n", + " 87%\n", + " 70%\n", " True\n", " \n", " \n", " 2\n", " robustness\n", " titlecase\n", - " 595\n", - " 905\n", - " 60%\n", - " 60%\n", + " 281\n", + " 1219\n", + " 81%\n", + " 70%\n", " True\n", " \n", " \n", " 3\n", " robustness\n", " add_punctuation\n", - " 9\n", - " 1491\n", - " 99%\n", - " 60%\n", + " 0\n", + " 1500\n", + " 100%\n", + " 70%\n", " True\n", " \n", " \n", " 4\n", " robustness\n", " strip_punctuation\n", - " 27\n", - " 1473\n", + " 23\n", + " 1477\n", " 98%\n", - " 60%\n", + " 70%\n", " True\n", " \n", " \n", " 5\n", " robustness\n", " add_slangs\n", - " 99\n", - " 1401\n", - " 93%\n", - " 60%\n", + " 115\n", + " 1385\n", + " 92%\n", + " 70%\n", " True\n", " \n", " \n", " 6\n", " robustness\n", " dyslexia_word_swap\n", - " 166\n", - " 1334\n", - " 89%\n", - " 60%\n", + " 138\n", + " 1362\n", + " 91%\n", + " 70%\n", " True\n", " \n", " \n", " 7\n", " robustness\n", " add_abbreviation\n", - " 337\n", - " 1163\n", - " 78%\n", - " 60%\n", + " 275\n", + " 1225\n", + " 82%\n", + " 70%\n", " True\n", " \n", " \n", " 8\n", " robustness\n", " add_speech_to_text_typo\n", - " 513\n", - " 987\n", - " 66%\n", - " 60%\n", + " 370\n", + " 1130\n", + " 75%\n", + " 70%\n", " True\n", " \n", " \n", " 9\n", " robustness\n", " number_to_word\n", - " 422\n", - " 1078\n", - " 72%\n", - " 60%\n", + " 395\n", + " 1105\n", + " 74%\n", + " 70%\n", " True\n", " \n", " \n", " 10\n", " robustness\n", " add_ocr_typo\n", - " 437\n", - " 1063\n", - " 71%\n", - " 60%\n", + " 260\n", + " 1240\n", + " 83%\n", + " 70%\n", " True\n", " \n", " \n", " 11\n", " robustness\n", " adjective_synonym_swap\n", - " 123\n", - " 1377\n", - " 92%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 12\n", - " bias\n", - " replace_to_male_pronouns\n", - " 21\n", - " 1479\n", - " 99%\n", - " 66%\n", - " True\n", - " \n", - " \n", - " 13\n", - " bias\n", - " replace_to_female_pronouns\n", - " 22\n", - " 1478\n", - " 99%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 14\n", - " bias\n", - " replace_to_inter_racial_lastnames\n", - " 234\n", - " 1266\n", - " 84%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 15\n", - " bias\n", - " replace_to_native_american_lastnames\n", - " 215\n", - " 1285\n", - " 86%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 16\n", - " bias\n", - " replace_to_asian_lastnames\n", - " 214\n", - " 1286\n", - " 86%\n", - " 60%\n", + " 125\n", + " 1375\n", + " 92%\n", + " 70%\n", " True\n", " \n", " \n", - " 17\n", + " 12\n", " accuracy\n", " min_precision_score\n", - " 1\n", - " 7\n", - " 88%\n", - " 65%\n", + " 0\n", + " 8\n", + " 100%\n", + " 70%\n", " True\n", " \n", " \n", - " 18\n", + " 13\n", " accuracy\n", " min_recall_score\n", - " 4\n", - " 4\n", - " 50%\n", - " 65%\n", - " False\n", + " 1\n", + " 7\n", + " 88%\n", + " 70%\n", + " True\n", " \n", " \n", - " 19\n", + " 14\n", " accuracy\n", " min_f1_score\n", - " 1\n", - " 7\n", - " 88%\n", - " 65%\n", + " 0\n", + " 8\n", + " 100%\n", + " 70%\n", " True\n", " \n", " \n", - " 20\n", + " 15\n", " accuracy\n", " min_micro_f1_score\n", " 0\n", " 1\n", " 100%\n", - " 65%\n", + " 70%\n", " True\n", " \n", " \n", @@ -3304,54 +2981,44 @@ "" ], "text/plain": [ - " category test_type fail_count pass_count \\\n", - "0 robustness uppercase 907 593 \n", - "1 robustness lowercase 141 1359 \n", - "2 robustness titlecase 595 905 \n", - "3 robustness add_punctuation 9 1491 \n", - "4 robustness strip_punctuation 27 1473 \n", - "5 robustness add_slangs 99 1401 \n", - "6 robustness dyslexia_word_swap 166 1334 \n", - "7 robustness add_abbreviation 337 1163 \n", - "8 robustness add_speech_to_text_typo 513 987 \n", - "9 robustness number_to_word 422 1078 \n", - "10 robustness add_ocr_typo 437 1063 \n", - "11 robustness adjective_synonym_swap 123 1377 \n", - "12 bias replace_to_male_pronouns 21 1479 \n", - "13 bias replace_to_female_pronouns 22 1478 \n", - "14 bias replace_to_inter_racial_lastnames 234 1266 \n", - "15 bias replace_to_native_american_lastnames 215 1285 \n", - "16 bias replace_to_asian_lastnames 214 1286 \n", - "17 accuracy min_precision_score 1 7 \n", - "18 accuracy min_recall_score 4 4 \n", - "19 accuracy min_f1_score 1 7 \n", - "20 accuracy min_micro_f1_score 0 1 \n", + " category test_type fail_count pass_count pass_rate \\\n", + "0 robustness uppercase 356 1144 76% \n", + "1 robustness lowercase 190 1310 87% \n", + "2 robustness titlecase 281 1219 81% \n", + "3 robustness add_punctuation 0 1500 100% \n", + "4 robustness strip_punctuation 23 1477 98% \n", + "5 robustness add_slangs 115 1385 92% \n", + "6 robustness dyslexia_word_swap 138 1362 91% \n", + "7 robustness add_abbreviation 275 1225 82% \n", + "8 robustness add_speech_to_text_typo 370 1130 75% \n", + "9 robustness number_to_word 395 1105 74% \n", + "10 robustness add_ocr_typo 260 1240 83% \n", + "11 robustness adjective_synonym_swap 125 1375 92% \n", + "12 accuracy min_precision_score 0 8 100% \n", + "13 accuracy min_recall_score 1 7 88% \n", + "14 accuracy min_f1_score 0 8 100% \n", + "15 accuracy min_micro_f1_score 0 1 100% \n", "\n", - " pass_rate minimum_pass_rate pass \n", - "0 40% 60% False \n", - "1 91% 60% True \n", - "2 60% 60% True \n", - "3 99% 60% True \n", - "4 98% 60% True \n", - "5 93% 60% True \n", - "6 89% 60% True \n", - "7 78% 60% True \n", - "8 66% 60% True \n", - "9 72% 60% True \n", - "10 71% 60% True \n", - "11 92% 60% True \n", - "12 99% 66% True \n", - "13 99% 60% True \n", - "14 84% 60% True \n", - "15 86% 60% True \n", - "16 86% 60% True \n", - "17 88% 65% True \n", - "18 50% 65% False \n", - "19 88% 65% True \n", - "20 100% 65% True " + " minimum_pass_rate pass \n", + "0 70% True \n", + "1 70% True \n", + "2 70% True \n", + "3 70% True \n", + "4 70% True \n", + "5 70% True \n", + "6 70% True \n", + "7 70% True \n", + "8 70% True \n", + "9 70% True \n", + "10 70% True \n", + "11 70% True \n", + "12 70% True \n", + "13 70% True \n", + "14 70% True \n", + "15 70% True " ] }, - "execution_count": 23, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -3360,969 +3027,4147 @@ "harness.report()" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "0vuRbN8s7eAg" - }, - "source": [ - "# Testing the ner_posology_langtest Model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YaGUHo_Ukhaz" - }, - "source": [ - "**Setting up License Keys**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4k23i5jj7hRN" - }, - "outputs": [], - "source": [ - "import json, os\n", - "from google.colab import files\n", - "\n", - "if 'spark_jsl.json' not in os.listdir():\n", - " license_keys = files.upload()\n", - " os.rename(list(license_keys.keys())[0], 'spark_jsl.json')\n", - "\n", - "with open('spark_jsl.json') as f:\n", - " license_keys = json.load(f)\n", - "\n", - "\n", - "\n", - "# Defining license key-value pairs as local variables\n", - "locals().update(license_keys)\n", - "os.environ.update(license_keys)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kNSUesZXkha0" - }, - "source": [ - "**Installing Required Packages**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-27T08:52:58.239017Z", - "iopub.status.busy": "2023-07-27T08:52:58.238519Z", - "iopub.status.idle": "2023-07-27T08:53:05.857467Z", - "shell.execute_reply": "2023-07-27T08:53:05.856747Z", - "shell.execute_reply.started": "2023-07-27T08:52:58.238991Z" - }, - "id": "iRT1dO9h7oY3", - "tags": [] - }, - "outputs": [], - "source": [ - "# Installing pyspark and spark-nlp\n", - "! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION\n", - "\n", - "# Installing Spark NLP Healthcare\n", - "! pip install --upgrade -q spark-nlp-jsl==$JSL_VERSION --extra-index-url https://pypi.johnsnowlabs.com/$SECRET\n", - "\n", - "# Installing Spark NLP Display Library for visualization\n", - "! pip install -q spark-nlp-display" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yvcXcr5c7uYL", - "tags": [] - }, - "outputs": [], - "source": [ - "# John Snow Labs setup\n", - "!pip install johnsnowlabs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fHfPRl9vkha0" - }, - "source": [ - "**Setting up Spark NLP and Spark Session**" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "H3URO3B37qsB", "tags": [] }, "outputs": [], "source": [ - "import sparknlp\n", - "import sparknlp_jsl\n", - "\n", - "from sparknlp.base import *\n", - "from sparknlp.annotator import *\n", - "from sparknlp_jsl.annotator import *\n", - "\n", - "from pyspark.sql import SparkSession\n", - "from pyspark.sql import functions as F\n", - "from pyspark.ml import Pipeline,PipelineModel\n", - "from pyspark.sql.types import StringType, IntegerType\n", - "\n", - "import pandas as pd\n", - "pd.set_option('display.max_colwidth', 200)\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "\n", - "params = {\"spark.driver.memory\":\"16G\",\n", - " \"spark.kryoserializer.buffer.max\":\"2000M\",\n", - " \"spark.driver.maxResultSize\":\"2000M\"}\n", - "\n", - "spark = sparknlp_jsl.start(license_keys['SECRET'],params=params)\n", - "\n", - "print(\"Spark NLP Version :\", sparknlp.version())\n", - "print(\"Spark NLP_JSL Version :\", sparknlp_jsl.version())\n", - "\n", - "spark" + "# saving the report in the form of csv\n", + "report=harness.report()\n", + "report.to_csv(\"ner_posology_langtest_report_.csv\",index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Define Spark NLP pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-28T07:11:38.057443Z", - "iopub.status.busy": "2023-07-28T07:11:38.057291Z", - "iopub.status.idle": "2023-07-28T07:12:42.196060Z", - "shell.execute_reply": "2023-07-28T07:12:42.195405Z", - "shell.execute_reply.started": "2023-07-28T07:11:38.057429Z" - }, - "id": "0yad9JP5yd8z", - "tags": [] - }, - "outputs": [], - "source": [ - "document_assembler = DocumentAssembler()\\\n", - " .setInputCol(\"text\")\\\n", - " .setOutputCol(\"document\")\n", - "\n", - "sentence_detector = SentenceDetector()\\\n", - " .setInputCols([\"document\"])\\\n", - " .setOutputCol(\"sentence\")\n", - "\n", - "tokenizer = Tokenizer()\\\n", - " .setInputCols([\"sentence\"])\\\n", - " .setOutputCol(\"token\")\n", - "\n", - "word_embeddings = WordEmbeddingsModel.pretrained(\"embeddings_clinical\", \"en\", \"clinical/models\")\\\n", - "\t.setInputCols([\"sentence\", \"token\"])\\\n", - "\t.setOutputCol(\"embeddings\")\n", - "\n", - "clinical_ner = MedicalNerModel.pretrained(\"ner_posology_langtest\",\"en\",\"clinical/models\")\\\n", - " .setInputCols([\"sentence\",\"token\",\"embeddings\"])\\\n", - " .setOutputCol(\"ner\")\n", - "\n", - "ner_converter = NerConverter()\\\n", - " \t.setInputCols([\"sentence\", \"token\", \"ner\"])\\\n", - " \t.setOutputCol(\"ner_chunk\")\n", - "\n", - "nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])\n", - "\n", - "ner_posology_langtest = nlp_pipeline.fit(spark.createDataFrame([[\"\"]]).toDF(\"text\"))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b1P5u57akha1" - }, - "source": [ - "To evaluate the performance of the `ner_posology_langtest` model, we will go through a similar testing process." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Vh7QJJfzkha1" - }, - "source": [ - "#### Instantiate the Harness Class\n", - "We start by instantiating the Harness class and providing the necessary information for testing. In this case, we specify the test data, set the task to \"ner\", and provide the model name and hub information." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-28T07:12:42.197454Z", - "iopub.status.busy": "2023-07-28T07:12:42.196899Z", - "iopub.status.idle": "2023-07-28T07:12:44.852563Z", - "shell.execute_reply": "2023-07-28T07:12:44.851859Z", - "shell.execute_reply.started": "2023-07-28T07:12:42.197437Z" - }, - "id": "HkUHmtlu7toH", - "tags": [] - }, - "outputs": [], - "source": [ - "harness = Harness(\n", - " data=\"testing/sample-pos.conll\",\n", - " task = \"ner\",\n", - " model=ner_posology_langtest,\n", - " hub=\"johnsnowlabs\"\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oP9N9AZskha1" - }, - "source": [ - "### Configure the Tests\n", - "We can use the .configure() method to manually configure the tests we want to perform." + "#### Visualizing the Report" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "execution": { - "iopub.execute_input": "2023-07-28T07:12:44.853886Z", - "iopub.status.busy": "2023-07-28T07:12:44.853702Z", - "iopub.status.idle": "2023-07-28T07:12:44.862861Z", - "shell.execute_reply": "2023-07-28T07:12:44.862172Z", - "shell.execute_reply.started": "2023-07-28T07:12:44.853871Z" - }, - "id": "3IQ_Arr88DN5", - "outputId": "5dfe20c1-6ae3-4d1e-d54c-1a83207d7fbb", - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tests': {'defaults': {'min_pass_rate': 0.65},\n", - " 'robustness': {'uppercase': {'min_pass_rate': 0.6},\n", - " 'lowercase': {'min_pass_rate': 0.6},\n", - " 'titlecase': {'min_pass_rate': 0.6},\n", - " 'add_punctuation': {'min_pass_rate': 0.6},\n", - " 'strip_punctuation': {'min_pass_rate': 0.6},\n", - " 'add_slangs': {'min_pass_rate': 0.6},\n", - " 'dyslexia_word_swap': {'min_pass_rate': 0.6},\n", - " 'add_abbreviation': {'min_pass_rate': 0.6},\n", - " 'add_speech_to_text_typo': {'min_pass_rate': 0.6},\n", - " 'number_to_word': {'min_pass_rate': 0.6},\n", - " 'add_ocr_typo': {'min_pass_rate': 0.6},\n", - " 'adjective_synonym_swap': {'min_pass_rate': 0.6}},\n", - " 'bias': {'replace_to_male_pronouns': {'min_pass_rate': 0.66},\n", - " 'replace_to_female_pronouns': {'min_pass_rate': 0.6},\n", - " 'replace_to_inter_racial_lastnames': {'min_pass_rate': 0.6},\n", - " 'replace_to_native_american_lastnames': {'min_pass_rate': 0.6},\n", - " 'replace_to_asian_lastnames': {'min_pass_rate': 0.6}},\n", - " 'accuracy': {'min_precision_score': {'min_score': 0.66},\n", - " 'min_recall_score': {'min_score': 0.6},\n", - " 'min_f1_score': {'min_score': 0.6},\n", - " 'min_micro_f1_score': {'min_score': 0.6}}}}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "harness.configure(\n", - "{\n", - " 'tests': {'defaults': {'min_pass_rate': 0.65},\n", - " 'robustness': {'uppercase': {'min_pass_rate': 0.60},\n", - " 'lowercase': {'min_pass_rate': 0.60},\n", - " 'titlecase':{'min_pass_rate': 0.60},\n", - " 'add_punctuation':{'min_pass_rate': 0.60},\n", - " 'strip_punctuation':{'min_pass_rate': 0.60},\n", - " 'add_slangs':{'min_pass_rate': 0.60},\n", - " 'dyslexia_word_swap':{'min_pass_rate': 0.60},\n", - " 'add_abbreviation':{'min_pass_rate': 0.60},\n", - " 'add_speech_to_text_typo':{'min_pass_rate': 0.60},\n", - " 'number_to_word':{'min_pass_rate': 0.60},\n", - " 'add_ocr_typo':{'min_pass_rate': 0.60},\n", - " 'adjective_synonym_swap':{'min_pass_rate': 0.60}\n", - " },\n", - " 'bias': {'replace_to_male_pronouns': {'min_pass_rate': 0.66},\n", - " 'replace_to_female_pronouns':{'min_pass_rate': 0.60},\n", - " 'replace_to_inter_racial_lastnames':{'min_pass_rate': 0.60},\n", - " 'replace_to_native_american_lastnames':{'min_pass_rate': 0.60},\n", - " 'replace_to_asian_lastnames':{'min_pass_rate': 0.60},\n", - " },\n", - " 'accuracy': {'min_precision_score': {'min_score': 0.66},\n", - " 'min_recall_score':{'min_score': 0.60},\n", - " 'min_f1_score':{'min_score': 0.60},\n", - " 'min_micro_f1_score':{'min_score': 0.60}\n", - " }\n", - "\n", - " }\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hyoy6AW2eabn" - }, - "source": [ - "Here we have configured the harness to perform robustness, bias and accuracy tests" - ] - }, - { - "cell_type": "markdown", + "execution_count": null, "metadata": { - "id": "Cz1XcszHecev" + "execution": { + "iopub.execute_input": "2023-08-25T22:35:21.911587Z", + "iopub.status.busy": "2023-08-25T22:35:21.911011Z", + "iopub.status.idle": "2023-08-25T22:35:23.417710Z", + "shell.execute_reply": "2023-08-25T22:35:23.417073Z", + "shell.execute_reply.started": "2023-08-25T22:35:21.911566Z" + } }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in /opt/conda/lib/python3.10/site-packages (5.13.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from plotly) (8.2.1)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "### Generating the test cases." + "pip install plotly" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, "execution": { - "iopub.execute_input": "2023-07-28T07:12:44.863839Z", - "iopub.status.busy": "2023-07-28T07:12:44.863674Z", - "iopub.status.idle": "2023-07-28T07:21:51.385003Z", - "shell.execute_reply": "2023-07-28T07:21:51.384528Z", - "shell.execute_reply.started": "2023-07-28T07:12:44.863824Z" + "iopub.execute_input": "2023-08-25T22:49:03.747776Z", + "iopub.status.busy": "2023-08-25T22:49:03.747221Z", + "iopub.status.idle": "2023-08-25T22:49:03.824362Z", + "shell.execute_reply": "2023-08-25T22:49:03.823924Z", + "shell.execute_reply.started": "2023-08-25T22:49:03.747755Z" }, - "id": "yIRaCDme8Kzu", - "outputId": "ef7984fc-1054-4fb5-dda3-4d31deb08bf4", "tags": [] }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Generating testcases...: 100%|██████████| 3/3 [00:00<00:00, 11295.25it/s]\n" - 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", 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\"Test Type\"},\n", + " title=\"Pass Rate by Test Type for Robustness Category\")\n", + "bar_fig.update_xaxes(tickangle=45)\n", + "bar_fig.update_layout(width=1000, height=700)\n", + "bar_fig.show()\n", + "\n", + "# Pie Chart: Distribution of Fail Count for the Robustness Category\n", + "pie_fig = px.pie(category_data, names=\"test_type\", values=\"fail_count\",\n", + " title=\"Distribution of Fail Count for Robustness Category\")\n", + "pie_fig.update_layout(width=800, height=600)\n", + "pie_fig.show()\n", + "\n", + "# Category 2: Accuracy\n", + "category_data = report[report[\"category\"] == \"accuracy\"]\n", + "\n", + "# Bar Plot: Pass Rate by Test Type for the Accuracy Category\n", + "bar_fig = px.bar(category_data, x=\"test_type\", y=\"pass_rate\",\n", + " labels={\"pass_rate\": \"Pass Rate\", \"test_type\": \"Test Type\"},\n", + " title=\"Pass Rate by Test Type for Accuracy Category\")\n", + "bar_fig.update_xaxes(tickangle=45)\n", + "bar_fig.update_layout(width=1000, 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categorytest_typeoriginaltest_case
0robustnessuppercaseOnce adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...
1robustnessuppercaseOne applicator full of VANDAZOLE administered intravaginally once a day for 5 days .ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .
2robustnessuppercaseBecause of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...
3robustnessuppercaseDOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...
4robustnessuppercaseDirections For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...
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25520accuracymin_f1_score-DOSAGE
25521accuracymin_f1_score-FREQUENCY
25522accuracymin_f1_score-DRUG
25523accuracymin_f1_score-O
25524accuracymin_micro_f1_score-micro
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" - ], - "text/plain": [ - " category test_type \\\n", - "0 robustness uppercase \n", - "1 robustness uppercase \n", - "2 robustness uppercase \n", - "3 robustness uppercase \n", - "4 robustness uppercase \n", - "... ... ... \n", - "25520 accuracy min_f1_score \n", - "25521 accuracy min_f1_score \n", - "25522 accuracy min_f1_score \n", - "25523 accuracy min_f1_score \n", - "25524 accuracy min_micro_f1_score \n", + "
" ] }, - "execution_count": 28, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "harness.testcases()" + "import plotly.graph_objects as go\n", + "\n", + "avg_pass_rate_by_category = report.groupby('category')['pass_rate'].mean().reset_index()\n", + "\n", + "# Create a table using Plotly\n", + "fig = go.Figure(data=[go.Table(\n", + " header=dict(values=['Category', 'Average Pass Rate (%)']),\n", + " cells=dict(values=[avg_pass_rate_by_category['category'], \n", + " avg_pass_rate_by_category['pass_rate'].apply(lambda x: f'{x:.2f}%')]))\n", + "])\n", + "\n", + "# Customize the layout\n", + "fig.update_layout(title='Average Pass Rates by Category - Table')\n", + "\n", + "# Show the interactive table\n", + "fig.show()" ] }, { "cell_type": "markdown", "metadata": { - "id": "VrLYT1SheiHn" + "id": "0vuRbN8s7eAg" }, "source": [ - "harness.testcases() method gives the produced test cases in form of a pandas data frame." + "## Testing Med7 model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vh7QJJfzkha1" + }, + "source": [ + "#### Instantiate the Harness Class\n", + "We start by instantiating the Harness class and providing the necessary information for testing. In this case, we specify the test data, set the task to \"ner\", and provide the model name and hub information." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T18:08:59.000877Z", + "iopub.status.busy": "2023-08-19T18:08:59.000302Z", + "iopub.status.idle": "2023-08-19T18:09:00.806192Z", + "shell.execute_reply": "2023-08-19T18:09:00.805677Z", + "shell.execute_reply.started": "2023-08-19T18:08:59.000855Z" + }, + "id": "HkUHmtlu7toH", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Configuration : \n", + " {\n", + " \"tests\": {\n", + " \"defaults\": {\n", + " \"min_pass_rate\": 1.0\n", + " },\n", + " \"robustness\": {\n", + " \"add_typo\": {\n", + " \"min_pass_rate\": 0.7\n", + " },\n", + " \"american_to_british\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"accuracy\": {\n", + " \"min_micro_f1_score\": {\n", + " \"min_score\": 0.7\n", + " }\n", + " },\n", + " \"bias\": {\n", + " \"replace_to_female_pronouns\": {\n", + " \"min_pass_rate\": 0.7\n", + " },\n", + " \"replace_to_low_income_country\": {\n", + " \"min_pass_rate\": 0.7\n", + " }\n", + " },\n", + " \"fairness\": {\n", + " \"min_gender_f1_score\": {\n", + " \"min_score\": 0.6\n", + " }\n", + " },\n", + " \"representation\": {\n", + " \"min_label_representation_count\": {\n", + " \"min_count\": 50\n", + " }\n", + " }\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "harness = Harness(\n", + " task = \"ner\",\n", + " data={\"data_source\":\"sample-test.conll\"},\n", + " model={\"model\":\"en_core_med7_lg\",\"hub\":\"spacy\"}\n", + " )" ] }, { "cell_type": "markdown", "metadata": { - "id": "N9O4gHMNej24" + "id": "oP9N9AZskha1" }, "source": [ - "### Running the tests" + "#### Configure the Tests\n", + "We can use the .configure() method to manually configure the tests we want to perform." ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T07:21:51.925444Z", - "iopub.status.busy": "2023-07-28T07:21:51.925290Z", - "iopub.status.idle": "2023-07-28T08:22:05.612466Z", - "shell.execute_reply": "2023-07-28T08:22:05.611983Z", - "shell.execute_reply.started": "2023-07-28T07:21:51.925429Z" + "iopub.execute_input": "2023-08-19T18:09:38.557328Z", + "iopub.status.busy": "2023-08-19T18:09:38.556759Z", + "iopub.status.idle": "2023-08-19T18:09:38.562799Z", + "shell.execute_reply": "2023-08-19T18:09:38.562351Z", + "shell.execute_reply.started": "2023-08-19T18:09:38.557307Z" }, - "id": "_EawiR9I8S6Z", - "outputId": "59649530-997a-40e4-8bb7-f826772e6957", + "id": "3IQ_Arr88DN5", + "outputId": "5dfe20c1-6ae3-4d1e-d54c-1a83207d7fbb", "tags": [] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Running testcases... : 100%|██████████| 25525/25525 [1:00:13<00:00, 7.06it/s]\n" - ] - }, { "data": { - "text/plain": [] + "text/plain": [ + "{'tests': {'defaults': {'min_pass_rate': 0.7},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.7},\n", + " 'lowercase': {'min_pass_rate': 0.7},\n", + " 'titlecase': {'min_pass_rate': 0.7},\n", + " 'add_punctuation': {'min_pass_rate': 0.7},\n", + " 'strip_punctuation': {'min_pass_rate': 0.7},\n", + " 'add_slangs': {'min_pass_rate': 0.7},\n", + " 'dyslexia_word_swap': {'min_pass_rate': 0.7},\n", + " 'add_abbreviation': {'min_pass_rate': 0.7},\n", + " 'add_speech_to_text_typo': {'min_pass_rate': 0.7},\n", + " 'number_to_word': {'min_pass_rate': 0.7},\n", + " 'add_ocr_typo': {'min_pass_rate': 0.7},\n", + " 'adjective_synonym_swap': {'min_pass_rate': 0.7}},\n", + " 'accuracy': {'min_precision_score': {'min_score': 0.7},\n", + " 'min_recall_score': {'min_score': 0.7},\n", + " 'min_f1_score': {'min_score': 0.7},\n", + " 'min_micro_f1_score': {'min_score': 0.7}}}}" + ] }, - "execution_count": 29, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "harness.run()" + "harness.configure(\n", + "{\n", + " 'tests': {'defaults': {'min_pass_rate': 0.70},\n", + " 'robustness': {'uppercase': {'min_pass_rate': 0.70},\n", + " 'lowercase': {'min_pass_rate': 0.70},\n", + " 'titlecase':{'min_pass_rate': 0.70},\n", + " 'add_punctuation':{'min_pass_rate': 0.70},\n", + " 'strip_punctuation':{'min_pass_rate': 0.70},\n", + " 'add_slangs':{'min_pass_rate': 0.70},\n", + " 'dyslexia_word_swap':{'min_pass_rate': 0.70},\n", + " 'add_abbreviation':{'min_pass_rate': 0.70},\n", + " 'add_speech_to_text_typo':{'min_pass_rate': 0.70},\n", + " 'number_to_word':{'min_pass_rate': 0.70},\n", + " 'add_ocr_typo':{'min_pass_rate': 0.70},\n", + " 'adjective_synonym_swap':{'min_pass_rate': 0.70}\n", + " },\n", + " 'accuracy': {'min_precision_score': {'min_score': 0.70},\n", + " 'min_recall_score':{'min_score': 0.70},\n", + " 'min_f1_score':{'min_score': 0.70},\n", + " 'min_micro_f1_score':{'min_score': 0.70}\n", + " }\n", + " }\n", + " }\n", + ")" ] }, { "cell_type": "markdown", "metadata": { - "id": "lBflodckeobP" + "id": "hyoy6AW2eabn" }, "source": [ - "Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test." + "Here we have configured the harness to perform robustness and Accuracy tests" ] }, { "cell_type": "markdown", "metadata": { - "id": "ylaUvFl7et63" + "id": "Cz1XcszHecev" }, "source": [ - "### Generated Results" + "#### Generating the test cases." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 + "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:05.613676Z", - "iopub.status.busy": "2023-07-28T08:22:05.613150Z", - "iopub.status.idle": "2023-07-28T08:22:25.808957Z", - "shell.execute_reply": "2023-07-28T08:22:25.808375Z", - "shell.execute_reply.started": "2023-07-28T08:22:05.613659Z" + "iopub.execute_input": "2023-08-19T18:09:43.904040Z", + "iopub.status.busy": "2023-08-19T18:09:43.903563Z" }, - "id": "PXGkHfb8ey9Y", - "outputId": "cb348a04-727a-46f9-d108-ad4112a6a7b8", + "id": "yIRaCDme8Kzu", + "outputId": "ef7984fc-1054-4fb5-dda3-4d31deb08bf4", "tags": [] }, "outputs": [ { - "data": { - "text/html": [ - "
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categorytest_typeoriginaltest_caseexpected_resultactual_resultpass
0robustnessuppercaseOnce adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTHAMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTHTrue
1robustnessuppercaseOne applicator full of VANDAZOLE administered intravaginally once a day for 5 days .ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATIONVANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATIONTrue
2robustnessuppercaseBecause of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCYPOTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTHFalse
3robustnessuppercaseDOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUGSELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUGTrue
4robustnessuppercaseDirections For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...at least every 2 hours: FREQUENCYFalse
........................
25520accuracymin_f1_score-DOSAGE0.60.707101True
25521accuracymin_f1_score-FREQUENCY0.60.925128True
25522accuracymin_f1_score-DRUG0.60.923308True
25523accuracymin_f1_score-O0.60.980428True
25524accuracymin_micro_f1_score-micro0.60.965413True
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25525 rows × 7 columns

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" - ], - "text/plain": [ - " category test_type \\\n", - "0 robustness uppercase \n", - "1 robustness uppercase \n", - "2 robustness uppercase \n", - "3 robustness uppercase \n", - "4 robustness uppercase \n", - "... ... ... \n", - "25520 accuracy min_f1_score \n", - "25521 accuracy min_f1_score \n", - "25522 accuracy min_f1_score \n", - "25523 accuracy min_f1_score \n", - "25524 accuracy min_micro_f1_score \n", - "\n", - " original \\\n", - "0 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", - "1 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", - "2 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", - "3 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "4 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", - "... ... \n", - "25520 - \n", - "25521 - \n", - "25522 - \n", - "25523 - \n", - "25524 - \n", - "\n", - " test_case \\\n", - "0 ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA... \n", - "1 ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS . \n", - "2 BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR... \n", - "3 DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE... \n", - "4 DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI... \n", - "... ... \n", - "25520 DOSAGE \n", - "25521 FREQUENCY \n", - "25522 DRUG \n", - "25523 O \n", - "25524 micro \n", - "\n", - " expected_result \\\n", - "0 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", - "1 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", - "2 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", - "3 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", - "4 at least every 2 hours: FREQUENCY \n", - "... ... \n", - "25520 0.6 \n", - "25521 0.6 \n", - "25522 0.6 \n", - "25523 0.6 \n", - "25524 0.6 \n", - "\n", - " actual_result \\\n", - "0 AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH \n", - "1 VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION \n", - "2 POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH \n", - "3 SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG \n", - "4 \n", - "... ... \n", - "25520 0.707101 \n", - "25521 0.925128 \n", - "25522 0.923308 \n", - "25523 0.980428 \n", - "25524 0.965413 \n", - "\n", - " pass \n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 True \n", - "4 False \n", - "... ... \n", - "25520 True \n", - "25521 True \n", - "25522 True \n", - "25523 True \n", - "25524 True \n", - "\n", - "[25525 rows x 7 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating testcases...: 100%|██████████| 2/2 [00:00<00:00, 14438.22it/s]\n" + ] } ], "source": [ - "harness.generated_results()" + "harness.generate()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DS6eXqiyefW_" + }, + "source": [ + "harness.generate() method automatically generates the test cases (based on the provided configuration)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919 + }, + "id": "Qh5ECzBQ8SBL", + "outputId": "991b71b9-3566-4c19-cc31-8dbb8aa0e349", + "tags": [] + }, + "outputs": [], + "source": [ + "harness.testcases()" ] }, { "cell_type": "markdown", "metadata": { - "id": "-2ak9DR4e-EA" + "id": "VrLYT1SheiHn" }, "source": [ - "This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed." + "harness.testcases() method gives the produced test cases in form of a pandas data frame." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N9O4gHMNej24" + }, + "source": [ + "#### Running the tests" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:25.809985Z", - "iopub.status.busy": "2023-07-28T08:22:25.809751Z", - "iopub.status.idle": "2023-07-28T08:22:32.369280Z", - "shell.execute_reply": "2023-07-28T08:22:32.368636Z", - "shell.execute_reply.started": "2023-07-28T08:22:25.809969Z" + "iopub.status.idle": "2023-08-19T18:22:40.601810Z" }, - "id": "-Ks-NDWI8WVr", + "id": "_EawiR9I8S6Z", + "outputId": "59649530-997a-40e4-8bb7-f826772e6957", "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Running testcases... : 100%|██████████| 18025/18025 [06:43<00:00, 44.70it/s]\n" + ] + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df= harness.generated_results()" + "harness.run()" ] }, { "cell_type": "markdown", "metadata": { - "id": "t68PxbyofNcx" + "id": "lBflodckeobP" + }, + "source": [ + "Called after harness.generate() and is to used to run all the tests. Returns a pass/fail flag for each test." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ylaUvFl7et63" }, "source": [ - "### Generated Results For robustness" + "#### Generated Results" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:32.370251Z", - "iopub.status.busy": "2023-07-28T08:22:32.370064Z", - "iopub.status.idle": "2023-07-28T08:22:32.381854Z", - "shell.execute_reply": "2023-07-28T08:22:32.381318Z", - "shell.execute_reply.started": "2023-07-28T08:22:32.370234Z" + "iopub.execute_input": "2023-08-19T18:22:40.606953Z", + "iopub.status.busy": "2023-08-19T18:22:40.606798Z", + "iopub.status.idle": "2023-08-19T18:22:42.218813Z" }, - "id": "lg18Ls1k8j2Q", - "outputId": "f571ab47-69ba-4ed4-eb31-f62807eda0a7", + "id": "PXGkHfb8ey9Y", + "outputId": "cb348a04-727a-46f9-d108-ad4112a6a7b8", "tags": [] }, "outputs": [ @@ -4363,9 +7208,9 @@ " uppercase\n", " Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...\n", " ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...\n", - " amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH\n", - " AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH\n", - " True\n", + " amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY\n", + " AMIODARONE HYDROCHLORIDE DOSE: DRUG\n", + " False\n", " \n", " \n", " 1\n", @@ -4373,9 +7218,9 @@ " uppercase\n", " One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .\n", " ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .\n", - " VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION\n", - " VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION\n", - " True\n", + " One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION\n", + " VANDAZOLE: DRUG, FOR 5 DAYS: DURATION\n", + " False\n", " \n", " \n", " 2\n", @@ -4383,8 +7228,8 @@ " uppercase\n", " Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...\n", " BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...\n", - " Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY\n", - " POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH\n", + " Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH\n", + " USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE\n", " False\n", " \n", " \n", @@ -4393,9 +7238,9 @@ " uppercase\n", " DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...\n", " DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...\n", - " Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG\n", - " SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG\n", - " True\n", + " hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG\n", + " \n", + " False\n", " \n", " \n", " 4\n", @@ -4403,9 +7248,9 @@ " uppercase\n", " Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...\n", " DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...\n", - " at least every 2 hours: FREQUENCY\n", " \n", - " False\n", + " \n", + " True\n", " \n", " \n", " ...\n", @@ -4418,73 +7263,73 @@ " ...\n", " \n", " \n", - " 17995\n", - " robustness\n", - " adjective_synonym_swap\n", - " The sooner you take emergency contraception , the better it works .\n", - " The sooner you take emergency contraception , the exceptional it works .\n", - " \n", - " \n", - " True\n", + " 18020\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " DURATION\n", + " 0.7\n", + " 0.66426\n", + " False\n", " \n", " \n", - " 17996\n", - " robustness\n", - " adjective_synonym_swap\n", - " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", - " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", - " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", - " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", - " True\n", + " 18021\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " ROUTE\n", + " 0.7\n", + " 0.669291\n", + " False\n", " \n", " \n", - " 17997\n", - " robustness\n", - " adjective_synonym_swap\n", - " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).\n", - " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the applicable diluent ( see COMPATIBILITY AND STABILITY : ).\n", - " ceftriaxone: DRUG, powder: FORM\n", - " ceftriaxone: DRUG, powder: FORM\n", - " True\n", + " 18022\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " DOSAGE\n", + " 0.7\n", + " 0.357143\n", + " False\n", " \n", " \n", - " 17998\n", - " robustness\n", - " adjective_synonym_swap\n", - " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", - " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences harsh bold or all audacious accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", - " nevirapine: DRUG\n", - " nevirapine: DRUG\n", + " 18023\n", + " accuracy\n", + " min_f1_score\n", + " -\n", + " FREQUENCY\n", + " 0.7\n", + " 0.771729\n", " True\n", " \n", " \n", - " 17999\n", - " robustness\n", - " adjective_synonym_swap\n", - " For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", - " For intramuscular administration , use a needle deep enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", - " \n", - " \n", + " 18024\n", + " accuracy\n", + " min_micro_f1_score\n", + " -\n", + " micro\n", + " 0.7\n", + " 0.932656\n", " True\n", " \n", " \n", "\n", - "

18000 rows × 7 columns

\n", + "

18025 rows × 7 columns

\n", "" ], "text/plain": [ - " category test_type \\\n", - "0 robustness uppercase \n", - "1 robustness uppercase \n", - "2 robustness uppercase \n", - "3 robustness uppercase \n", - "4 robustness uppercase \n", - "... ... ... \n", - "17995 robustness adjective_synonym_swap \n", - "17996 robustness adjective_synonym_swap \n", - "17997 robustness adjective_synonym_swap \n", - "17998 robustness adjective_synonym_swap \n", - "17999 robustness adjective_synonym_swap \n", + " category test_type \\\n", + "0 robustness uppercase \n", + "1 robustness uppercase \n", + "2 robustness uppercase \n", + "3 robustness uppercase \n", + "4 robustness uppercase \n", + "... ... ... \n", + "18020 accuracy min_f1_score \n", + "18021 accuracy min_f1_score \n", + "18022 accuracy min_f1_score \n", + "18023 accuracy min_f1_score \n", + "18024 accuracy min_micro_f1_score \n", "\n", " original \\\n", "0 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", @@ -4493,11 +7338,11 @@ "3 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", "4 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", "... ... \n", - "17995 The sooner you take emergency contraception , the better it works . \n", - "17996 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "17997 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "17998 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "17999 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", + "18020 - \n", + "18021 - \n", + "18022 - \n", + "18023 - \n", + "18024 - \n", "\n", " test_case \\\n", "0 ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA... \n", @@ -4506,89 +7351,100 @@ "3 DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE... \n", "4 DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI... \n", "... ... \n", - "17995 The sooner you take emergency contraception , the exceptional it works . \n", - "17996 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "17997 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the applicable diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "17998 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences harsh bold or all audacious accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "17999 For intramuscular administration , use a needle deep enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", - "\n", - " expected_result \\\n", - "0 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", - "1 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", - "2 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", - "3 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", - "4 at least every 2 hours: FREQUENCY \n", - "... ... \n", - "17995 \n", - "17996 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", - "17997 ceftriaxone: DRUG, powder: FORM \n", - "17998 nevirapine: DRUG \n", - "17999 \n", + "18020 DURATION \n", + "18021 ROUTE \n", + "18022 DOSAGE \n", + "18023 FREQUENCY \n", + "18024 micro \n", "\n", - " actual_result \\\n", - "0 AMIODARONE HYDROCHLORIDE: DRUG, 600 TO 800 MG/DAY: STRENGTH, FOR ONE MONTH: DURATION, 400 MG/DAY: STRENGTH \n", - "1 VANDAZOLE: DRUG, INTRAVAGINALLY: ROUTE, ONCE A DAY: FREQUENCY, FOR 5 DAYS: DURATION \n", - "2 POTASSIUM CHLORIDE EXTENDED-RELEASE: DRUG, CAPSULES: FORM, 8 MEQ: STRENGTH \n", - "3 SELEGILINE HYDROCHLORIDE: DRUG, CAPSULES: FORM, LEVODOPA/CARBIDOPA: DRUG \n", - "4 \n", - "... ... \n", - "17995 \n", - "17996 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", - "17997 ceftriaxone: DRUG, powder: FORM \n", - "17998 nevirapine: DRUG \n", - "17999 \n", + " expected_result \\\n", + "0 amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY \n", + "1 One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION \n", + "2 Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH \n", + "3 hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG \n", + "4 \n", + "... ... \n", + "18020 0.7 \n", + "18021 0.7 \n", + "18022 0.7 \n", + "18023 0.7 \n", + "18024 0.7 \n", "\n", - " pass \n", - "0 True \n", - "1 True \n", - "2 False \n", - "3 True \n", - "4 False \n", - "... ... \n", - "17995 True \n", - "17996 True \n", - "17997 True \n", - "17998 True \n", - "17999 True \n", + " actual_result pass \n", + "0 AMIODARONE HYDROCHLORIDE DOSE: DRUG False \n", + "1 VANDAZOLE: DRUG, FOR 5 DAYS: DURATION False \n", + "2 USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE False \n", + "3 False \n", + "4 True \n", + "... ... ... \n", + "18020 0.66426 False \n", + "18021 0.669291 False \n", + "18022 0.357143 False \n", + "18023 0.771729 True \n", + "18024 0.932656 True \n", "\n", - "[18000 rows x 7 columns]" + "[18025 rows x 7 columns]" ] }, - "execution_count": 32, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df[df[\"category\"]==\"robustness\"]" + "harness.generated_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-2ak9DR4e-EA" + }, + "source": [ + "This method returns the generated results in the form of a pandas dataframe, which provides a convenient and easy-to-use format for working with the test results. You can use this method to quickly identify the test cases that failed and to determine where fixes are needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-19T18:22:42.224454Z", + "iopub.status.busy": "2023-08-19T18:22:42.224322Z", + "iopub.status.idle": "2023-08-19T18:22:42.722080Z" + }, + "id": "-Ks-NDWI8WVr", + "tags": [] + }, + "outputs": [], + "source": [ + "df= harness.generated_results()" ] }, { "cell_type": "markdown", "metadata": { - "id": "2rhoQyMkfIYS" + "id": "t68PxbyofNcx" }, "source": [ - "### Generated Results For bias" + "#### Generated Results For robustness" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:32.382649Z", - "iopub.status.busy": "2023-07-28T08:22:32.382496Z", - "iopub.status.idle": "2023-07-28T08:22:32.457520Z", - "shell.execute_reply": "2023-07-28T08:22:32.457023Z", - "shell.execute_reply.started": "2023-07-28T08:22:32.382634Z" + "iopub.execute_input": "2023-08-19T18:22:42.727828Z", + "iopub.status.busy": "2023-08-19T18:22:42.727690Z", + "iopub.status.idle": "2023-08-19T18:22:42.738417Z" }, - "id": "So0g7_Uk8nCP", - "outputId": "305fbda8-d727-461f-bcb4-c5369d67f4c6", + "id": "lg18Ls1k8j2Q", + "outputId": "f571ab47-69ba-4ed4-eb31-f62807eda0a7", "tags": [] }, "outputs": [ @@ -4624,53 +7480,53 @@ " \n", " \n", " \n", - " 18000\n", - " bias\n", - " replace_to_male_pronouns\n", - " Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...\n", + " 0\n", + " robustness\n", + " uppercase\n", " Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua...\n", - " amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH\n", - " amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH\n", - " True\n", + " ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA...\n", + " amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY\n", + " AMIODARONE HYDROCHLORIDE DOSE: DRUG\n", + " False\n", " \n", " \n", - " 18001\n", - " bias\n", - " replace_to_male_pronouns\n", - " One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .\n", + " 1\n", + " robustness\n", + " uppercase\n", " One applicator full of VANDAZOLE administered intravaginally once a day for 5 days .\n", - " VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION\n", - " VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION\n", - " True\n", + " ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS .\n", + " One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION\n", + " VANDAZOLE: DRUG, FOR 5 DAYS: DURATION\n", + " False\n", " \n", " \n", - " 18002\n", - " bias\n", - " replace_to_male_pronouns\n", - " Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...\n", + " 2\n", + " robustness\n", + " uppercase\n", " Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or...\n", - " Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY\n", - " Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY\n", - " True\n", + " BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR...\n", + " Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH\n", + " USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE\n", + " False\n", " \n", " \n", - " 18003\n", - " bias\n", - " replace_to_male_pronouns\n", - " DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...\n", + " 3\n", + " robustness\n", + " uppercase\n", " DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response...\n", - " Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG\n", - " Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG\n", - " True\n", + " DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE...\n", + " hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG\n", + " \n", + " False\n", " \n", " \n", - " 18004\n", - " bias\n", - " replace_to_male_pronouns\n", - " Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...\n", + " 4\n", + " robustness\n", + " uppercase\n", " Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti...\n", - " at least every 2 hours: FREQUENCY\n", - " at least every 2 hours: FREQUENCY\n", + " DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI...\n", + " \n", + " \n", " True\n", " \n", " \n", @@ -4684,177 +7540,154 @@ " ...\n", " \n", " \n", - " 25495\n", - " bias\n", - " replace_to_asian_lastnames\n", - " The sooner you take emergency contraception , the better it works .\n", + " 17995\n", + " robustness\n", + " adjective_synonym_swap\n", " The sooner you take emergency contraception , the better it works .\n", + " The sooner you take emergency contraception , the improved it works .\n", " \n", " \n", " True\n", " \n", " \n", - " 25496\n", - " bias\n", - " replace_to_asian_lastnames\n", + " 17996\n", + " robustness\n", + " adjective_synonym_swap\n", " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", " Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel .\n", - " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", - " Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG\n", + " Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG\n", + " Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG\n", " True\n", " \n", " \n", - " 25497\n", - " bias\n", - " replace_to_asian_lastnames\n", - " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).\n", + " 17997\n", + " robustness\n", + " adjective_synonym_swap\n", " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ).\n", - " ceftriaxone: DRUG, powder: FORM\n", - " ceftriaxone: DRUG, powder: FORM\n", + " DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the convenient diluent ( see COMPATIBILITY AND STABILITY : ).\n", + " Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM\n", + " Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM\n", " True\n", " \n", " \n", - " 25498\n", - " bias\n", - " replace_to_asian_lastnames\n", + " 17998\n", + " robustness\n", + " adjective_synonym_swap\n", " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", - " 2.4 Dosage Adjustment Patients with Dumlao Discontinue nevirapine if a patient experiences Kancharla Bala or any Bala accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", + " 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences serious bold or each bold accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] .\n", + " nevirapine: DRUG\n", " nevirapine: DRUG\n", - " Dumlao: DRUG, nevirapine: DRUG\n", " True\n", " \n", " \n", - " 25499\n", - " bias\n", - " replace_to_asian_lastnames\n", - " For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", + " 17999\n", + " robustness\n", + " adjective_synonym_swap\n", " For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", + " For intramuscular administration , use a needle deep enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle .\n", " \n", " \n", " True\n", " \n", " \n", "\n", - "

7500 rows × 7 columns

\n", + "

18000 rows × 7 columns

\n", "" ], "text/plain": [ - " category test_type \\\n", - "18000 bias replace_to_male_pronouns \n", - "18001 bias replace_to_male_pronouns \n", - "18002 bias replace_to_male_pronouns \n", - "18003 bias replace_to_male_pronouns \n", - "18004 bias replace_to_male_pronouns \n", - "... ... ... \n", - "25495 bias replace_to_asian_lastnames \n", - "25496 bias replace_to_asian_lastnames \n", - "25497 bias replace_to_asian_lastnames \n", - "25498 bias replace_to_asian_lastnames \n", - "25499 bias replace_to_asian_lastnames \n", + " category test_type \\\n", + "0 robustness uppercase \n", + "1 robustness uppercase \n", + "2 robustness uppercase \n", + "3 robustness uppercase \n", + "4 robustness uppercase \n", + "... ... ... \n", + "17995 robustness adjective_synonym_swap \n", + "17996 robustness adjective_synonym_swap \n", + "17997 robustness adjective_synonym_swap \n", + "17998 robustness adjective_synonym_swap \n", + "17999 robustness adjective_synonym_swap \n", "\n", " original \\\n", - "18000 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", - "18001 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", - "18002 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", - "18003 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "18004 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", + "0 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", + "1 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", + "2 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", + "3 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", + "4 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", "... ... \n", - "25495 The sooner you take emergency contraception , the better it works . \n", - "25496 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "25497 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "25498 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "25499 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", + "17995 The sooner you take emergency contraception , the better it works . \n", + "17996 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", + "17997 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", + "17998 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences severe rash or any rash accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", + "17999 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", "\n", " test_case \\\n", - "18000 Once adequate arrhythmia control is achieved , or if side effects become prominent , reduce amiodarone hydrochloride dose to 600 to 800 mg/day for one month and then to the maintenance dose , usua... \n", - "18001 One applicator full of VANDAZOLE administered intravaginally once a day for 5 days . \n", - "18002 Because of the potential for gastric irritation ( see WARNINGS ), Potassium Chloride Extended-release Capsules , USP , 8 mEq and 10 mEq should be taken with meals and with a full glass of water or... \n", - "18003 DOSAGE AND ADMINISTRATION : Selegiline hydrochloride capsules are intended for administration to Parkinsonian patients receiving levodopa/carbidopa therapy who demonstrate a deteriorating response... \n", - "18004 Directions For sunscreen use : - apply liberally and evenly 15 minutes before sun exposure - reapply at least every 2 hours - use a water resistant sunscreen if swimming or sweating - Sun Protecti... \n", + "0 ONCE ADEQUATE ARRHYTHMIA CONTROL IS ACHIEVED , OR IF SIDE EFFECTS BECOME PROMINENT , REDUCE AMIODARONE HYDROCHLORIDE DOSE TO 600 TO 800 MG/DAY FOR ONE MONTH AND THEN TO THE MAINTENANCE DOSE , USUA... \n", + "1 ONE APPLICATOR FULL OF VANDAZOLE ADMINISTERED INTRAVAGINALLY ONCE A DAY FOR 5 DAYS . \n", + "2 BECAUSE OF THE POTENTIAL FOR GASTRIC IRRITATION ( SEE WARNINGS ), POTASSIUM CHLORIDE EXTENDED-RELEASE CAPSULES , USP , 8 MEQ AND 10 MEQ SHOULD BE TAKEN WITH MEALS AND WITH A FULL GLASS OF WATER OR... \n", + "3 DOSAGE AND ADMINISTRATION : SELEGILINE HYDROCHLORIDE CAPSULES ARE INTENDED FOR ADMINISTRATION TO PARKINSONIAN PATIENTS RECEIVING LEVODOPA/CARBIDOPA THERAPY WHO DEMONSTRATE A DETERIORATING RESPONSE... \n", + "4 DIRECTIONS FOR SUNSCREEN USE : - APPLY LIBERALLY AND EVENLY 15 MINUTES BEFORE SUN EXPOSURE - REAPPLY AT LEAST EVERY 2 HOURS - USE A WATER RESISTANT SUNSCREEN IF SWIMMING OR SWEATING - SUN PROTECTI... \n", "... ... \n", - "25495 The sooner you take emergency contraception , the better it works . \n", - "25496 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", - "25497 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the appropriate diluent ( see COMPATIBILITY AND STABILITY : ). \n", - "25498 2.4 Dosage Adjustment Patients with Dumlao Discontinue nevirapine if a patient experiences Kancharla Bala or any Bala accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", - "25499 For intramuscular administration , use a needle long enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", - "\n", - " expected_result \\\n", - "18000 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", - "18001 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", - "18002 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", - "18003 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", - "18004 at least every 2 hours: FREQUENCY \n", - "... ... \n", - "25495 \n", - "25496 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", - "25497 ceftriaxone: DRUG, powder: FORM \n", - "25498 nevirapine: DRUG \n", - "25499 \n", + "17995 The sooner you take emergency contraception , the improved it works . \n", + "17996 Omeprazole and esomeprazole significantly reduce the antiplatelet activity of Clopidogrel . \n", + "17997 DIRECTIONS FOR USE : Intramuscular Administration : Reconstitute ceftriaxone powder with the convenient diluent ( see COMPATIBILITY AND STABILITY : ). \n", + "17998 2.4 Dosage Adjustment Patients with Rash Discontinue nevirapine if a patient experiences serious bold or each bold accompanied by constitutional findings [see Warnings and Precautions ( 5.2)] . \n", + "17999 For intramuscular administration , use a needle deep enough ( at least 1/2 inch to 5/8 inch ) to ensure the injection is administered into the muscle . \n", "\n", - " actual_result \\\n", - "18000 amiodarone hydrochloride: DRUG, 600 to 800 mg/day: STRENGTH, for one month: DURATION, 400 mg/day: STRENGTH \n", - "18001 VANDAZOLE: DRUG, intravaginally: ROUTE, once a day: FREQUENCY, for 5 days: DURATION \n", - "18002 Potassium Chloride Extended-release: DRUG, Capsules: FORM, 8 mEq: STRENGTH, 10 mEq: STRENGTH, with meals: FREQUENCY \n", - "18003 Selegiline hydrochloride: DRUG, capsules: FORM, levodopa/carbidopa: DRUG \n", - "18004 at least every 2 hours: FREQUENCY \n", - "... ... \n", - "25495 \n", - "25496 Omeprazole: DRUG, esomeprazole: DRUG, antiplatelet activity: DRUG, Clopidogrel: DRUG \n", - "25497 ceftriaxone: DRUG, powder: FORM \n", - "25498 Dumlao: DRUG, nevirapine: DRUG \n", - "25499 \n", + " expected_result \\\n", + "0 amiodarone hydrochloride: DRUG, 800 mg/: STRENGTH, day: FREQUENCY, for one month: DURATION, 400 mg/: STRENGTH, day: FREQUENCY \n", + "1 One: DOSAGE, VANDAZOLE: DRUG, once a day: FREQUENCY, for 5 days: DURATION \n", + "2 Potassium Chloride: DRUG, USP: DRUG, 8 mEq: STRENGTH, 10 mEq: STRENGTH \n", + "3 hydrochloride: DRUG, capsules: FORM, levodopa: DRUG, carbidopa: DRUG \n", + "4 \n", + "... ... \n", + "17995 \n", + "17996 Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG \n", + "17997 Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM \n", + "17998 nevirapine: DRUG \n", + "17999 \n", "\n", - " pass \n", - "18000 True \n", - "18001 True \n", - "18002 True \n", - "18003 True \n", - "18004 True \n", - "... ... \n", - "25495 True \n", - "25496 True \n", - "25497 True \n", - "25498 True \n", - "25499 True \n", + " actual_result pass \n", + "0 AMIODARONE HYDROCHLORIDE DOSE: DRUG False \n", + "1 VANDAZOLE: DRUG, FOR 5 DAYS: DURATION False \n", + "2 USP: DRUG, 8: DOSAGE, MEQ: DRUG, 10: DOSAGE False \n", + "3 False \n", + "4 True \n", + "... ... ... \n", + "17995 True \n", + "17996 Omeprazole: DRUG, esomeprazole: DRUG, Clopidogrel: DRUG True \n", + "17997 Reconstitute: DRUG, ceftriaxone: DRUG, powder: FORM True \n", + "17998 nevirapine: DRUG True \n", + "17999 True \n", "\n", - "[7500 rows x 7 columns]" + "[18000 rows x 7 columns]" ] }, - "execution_count": 33, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df[df[\"category\"]==\"bias\"]" + "df[df[\"category\"]==\"robustness\"]" ] }, { "cell_type": "markdown", - "metadata": { - "id": "qfAhVbobfFbI" - }, + "metadata": {}, "source": [ - "### Generated Results For accuracy" + "#### Generated Results For accuracy" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 877 - }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:32.458290Z", - "iopub.status.busy": "2023-07-28T08:22:32.458137Z", - "iopub.status.idle": "2023-07-28T08:22:32.532835Z", - "shell.execute_reply": "2023-07-28T08:22:32.532392Z", - "shell.execute_reply.started": "2023-07-28T08:22:32.458274Z" + "iopub.execute_input": "2023-08-19T18:22:42.739328Z", + "iopub.status.busy": "2023-08-19T18:22:42.738986Z", + "iopub.status.idle": "2023-08-19T18:22:42.796778Z" }, - "id": "yt0L7pgL8qXj", - "outputId": "b4fc1cb8-9bd2-4c31-e2e1-433fd23ba4f8", "tags": [] }, "outputs": [ @@ -4890,253 +7723,253 @@ " \n", " \n", " \n", - " 25500\n", + " 18000\n", " accuracy\n", " min_precision_score\n", " -\n", - " DURATION\n", - " 0.66\n", - " 0.91704\n", + " DRUG\n", + " 0.7\n", + " 0.8726\n", " True\n", " \n", " \n", - " 25501\n", + " 18001\n", " accuracy\n", " min_precision_score\n", " -\n", - " FORM\n", - " 0.66\n", - " 0.872274\n", + " O\n", + " 0.7\n", + " 0.948315\n", " True\n", " \n", " \n", - " 25502\n", + " 18002\n", " accuracy\n", " min_precision_score\n", " -\n", - " STRENGTH\n", - " 0.66\n", - " 0.904233\n", + " FORM\n", + " 0.7\n", + " 0.836957\n", " True\n", " \n", " \n", - " 25503\n", + " 18003\n", " accuracy\n", " min_precision_score\n", " -\n", - " ROUTE\n", - " 0.66\n", - " 0.925566\n", + " STRENGTH\n", + " 0.7\n", + " 0.820327\n", " True\n", " \n", " \n", - " 25504\n", + " 18004\n", " accuracy\n", " min_precision_score\n", " -\n", - " DOSAGE\n", - " 0.66\n", - " 0.737654\n", + " DURATION\n", + " 0.7\n", + " 0.779661\n", " True\n", " \n", " \n", - " 25505\n", + " 18005\n", " accuracy\n", " min_precision_score\n", " -\n", - " FREQUENCY\n", - " 0.66\n", - " 0.938931\n", + " ROUTE\n", + " 0.7\n", + " 0.858586\n", " True\n", " \n", " \n", - " 25506\n", + " 18006\n", " accuracy\n", " min_precision_score\n", " -\n", - " DRUG\n", - " 0.66\n", - " 0.928427\n", - " True\n", + " DOSAGE\n", + " 0.7\n", + " 0.446429\n", + " False\n", " \n", " \n", - " 25507\n", + " 18007\n", " accuracy\n", " min_precision_score\n", " -\n", - " O\n", - " 0.66\n", - " 0.976522\n", + " FREQUENCY\n", + " 0.7\n", + " 0.834711\n", " True\n", " \n", " \n", - " 25508\n", + " 18008\n", " accuracy\n", " min_recall_score\n", " -\n", - " DURATION\n", - " 0.6\n", - " 0.809901\n", - " True\n", + " DRUG\n", + " 0.7\n", + " 0.69735\n", + " False\n", " \n", " \n", - " 25509\n", + " 18009\n", " accuracy\n", " min_recall_score\n", " -\n", - " FORM\n", - " 0.6\n", - " 0.843373\n", + " O\n", + " 0.7\n", + " 0.977315\n", " True\n", " \n", " \n", - " 25510\n", + " 18010\n", " accuracy\n", " min_recall_score\n", " -\n", - " STRENGTH\n", - " 0.6\n", - " 0.869278\n", - " True\n", + " FORM\n", + " 0.7\n", + " 0.572491\n", + " False\n", " \n", " \n", - " 25511\n", + " 18011\n", " accuracy\n", " min_recall_score\n", " -\n", - " ROUTE\n", - " 0.6\n", - " 0.836257\n", + " STRENGTH\n", + " 0.7\n", + " 0.826325\n", " True\n", " \n", " \n", - " 25512\n", + " 18012\n", " accuracy\n", " min_recall_score\n", " -\n", - " DOSAGE\n", - " 0.6\n", - " 0.678977\n", - " True\n", + " DURATION\n", + " 0.7\n", + " 0.578616\n", + " False\n", " \n", " \n", - " 25513\n", + " 18013\n", " accuracy\n", " min_recall_score\n", " -\n", - " FREQUENCY\n", - " 0.6\n", - " 0.911725\n", - " True\n", + " ROUTE\n", + " 0.7\n", + " 0.548387\n", + " False\n", " \n", " \n", - " 25514\n", + " 18014\n", " accuracy\n", " min_recall_score\n", " -\n", - " DRUG\n", - " 0.6\n", - " 0.918245\n", - " True\n", + " DOSAGE\n", + " 0.7\n", + " 0.297619\n", + " False\n", " \n", " \n", - " 25515\n", + " 18015\n", " accuracy\n", " min_recall_score\n", " -\n", - " O\n", - " 0.6\n", - " 0.984364\n", + " FREQUENCY\n", + " 0.7\n", + " 0.717584\n", " True\n", " \n", " \n", - " 25516\n", + " 18016\n", " accuracy\n", " min_f1_score\n", " -\n", - " DURATION\n", - " 0.6\n", - " 0.860147\n", + " DRUG\n", + " 0.7\n", + " 0.775194\n", " True\n", " \n", " \n", - " 25517\n", + " 18017\n", " accuracy\n", " min_f1_score\n", " -\n", - " FORM\n", - " 0.6\n", - " 0.85758\n", + " O\n", + " 0.7\n", + " 0.962597\n", " True\n", " \n", " \n", - " 25518\n", + " 18018\n", " accuracy\n", - " min_f1_score\n", - " -\n", - " STRENGTH\n", - " 0.6\n", - " 0.886411\n", - " True\n", + " min_f1_score\n", + " -\n", + " FORM\n", + " 0.7\n", + " 0.679912\n", + " False\n", " \n", " \n", - " 25519\n", + " 18019\n", " accuracy\n", " min_f1_score\n", " -\n", - " ROUTE\n", - " 0.6\n", - " 0.878648\n", + " STRENGTH\n", + " 0.7\n", + " 0.823315\n", " True\n", " \n", " \n", - " 25520\n", + " 18020\n", " accuracy\n", " min_f1_score\n", " -\n", - " DOSAGE\n", - " 0.6\n", - " 0.707101\n", - " True\n", + " DURATION\n", + " 0.7\n", + " 0.66426\n", + " False\n", " \n", " \n", - " 25521\n", + " 18021\n", " accuracy\n", " min_f1_score\n", " -\n", - " FREQUENCY\n", - " 0.6\n", - " 0.925128\n", - " True\n", + " ROUTE\n", + " 0.7\n", + " 0.669291\n", + " False\n", " \n", " \n", - " 25522\n", + " 18022\n", " accuracy\n", " min_f1_score\n", " -\n", - " DRUG\n", - " 0.6\n", - " 0.923308\n", - " True\n", + " DOSAGE\n", + " 0.7\n", + " 0.357143\n", + " False\n", " \n", " \n", - " 25523\n", + " 18023\n", " accuracy\n", " min_f1_score\n", " -\n", - " O\n", - " 0.6\n", - " 0.980428\n", + " FREQUENCY\n", + " 0.7\n", + " 0.771729\n", " True\n", " \n", " \n", - " 25524\n", + " 18024\n", " accuracy\n", " min_micro_f1_score\n", " -\n", " micro\n", - " 0.6\n", - " 0.965413\n", + " 0.7\n", + " 0.932656\n", " True\n", " \n", " \n", @@ -5145,61 +7978,61 @@ ], "text/plain": [ " category test_type original test_case expected_result \\\n", - "25500 accuracy min_precision_score - DURATION 0.66 \n", - "25501 accuracy min_precision_score - FORM 0.66 \n", - "25502 accuracy min_precision_score - STRENGTH 0.66 \n", - "25503 accuracy min_precision_score - ROUTE 0.66 \n", - "25504 accuracy min_precision_score - DOSAGE 0.66 \n", - "25505 accuracy min_precision_score - FREQUENCY 0.66 \n", - "25506 accuracy min_precision_score - DRUG 0.66 \n", - "25507 accuracy min_precision_score - O 0.66 \n", - "25508 accuracy min_recall_score - DURATION 0.6 \n", - "25509 accuracy min_recall_score - FORM 0.6 \n", - "25510 accuracy min_recall_score - STRENGTH 0.6 \n", - "25511 accuracy min_recall_score - ROUTE 0.6 \n", - "25512 accuracy min_recall_score - DOSAGE 0.6 \n", - "25513 accuracy min_recall_score - FREQUENCY 0.6 \n", - "25514 accuracy min_recall_score - DRUG 0.6 \n", - "25515 accuracy min_recall_score - O 0.6 \n", - "25516 accuracy min_f1_score - DURATION 0.6 \n", - "25517 accuracy min_f1_score - FORM 0.6 \n", - "25518 accuracy min_f1_score - STRENGTH 0.6 \n", - "25519 accuracy min_f1_score - ROUTE 0.6 \n", - "25520 accuracy min_f1_score - DOSAGE 0.6 \n", - "25521 accuracy min_f1_score - FREQUENCY 0.6 \n", - "25522 accuracy min_f1_score - DRUG 0.6 \n", - "25523 accuracy min_f1_score - O 0.6 \n", - "25524 accuracy min_micro_f1_score - micro 0.6 \n", + "18000 accuracy min_precision_score - DRUG 0.7 \n", + "18001 accuracy min_precision_score - O 0.7 \n", + "18002 accuracy min_precision_score - FORM 0.7 \n", + "18003 accuracy min_precision_score - STRENGTH 0.7 \n", + "18004 accuracy min_precision_score - DURATION 0.7 \n", + "18005 accuracy min_precision_score - ROUTE 0.7 \n", + "18006 accuracy min_precision_score - DOSAGE 0.7 \n", + "18007 accuracy min_precision_score - FREQUENCY 0.7 \n", + "18008 accuracy min_recall_score - DRUG 0.7 \n", + "18009 accuracy min_recall_score - O 0.7 \n", + "18010 accuracy min_recall_score - FORM 0.7 \n", + "18011 accuracy min_recall_score - STRENGTH 0.7 \n", + "18012 accuracy min_recall_score - DURATION 0.7 \n", + "18013 accuracy min_recall_score - ROUTE 0.7 \n", + "18014 accuracy min_recall_score - DOSAGE 0.7 \n", + "18015 accuracy min_recall_score - FREQUENCY 0.7 \n", + "18016 accuracy min_f1_score - DRUG 0.7 \n", + "18017 accuracy min_f1_score - O 0.7 \n", + "18018 accuracy min_f1_score - FORM 0.7 \n", + "18019 accuracy min_f1_score - STRENGTH 0.7 \n", + "18020 accuracy min_f1_score - DURATION 0.7 \n", + "18021 accuracy min_f1_score - ROUTE 0.7 \n", + "18022 accuracy min_f1_score - DOSAGE 0.7 \n", + "18023 accuracy min_f1_score - FREQUENCY 0.7 \n", + "18024 accuracy min_micro_f1_score - micro 0.7 \n", "\n", - " actual_result pass \n", - "25500 0.91704 True \n", - "25501 0.872274 True \n", - "25502 0.904233 True \n", - "25503 0.925566 True \n", - "25504 0.737654 True \n", - "25505 0.938931 True \n", - "25506 0.928427 True \n", - "25507 0.976522 True \n", - "25508 0.809901 True \n", - "25509 0.843373 True \n", - "25510 0.869278 True \n", - "25511 0.836257 True \n", - "25512 0.678977 True \n", - "25513 0.911725 True \n", - "25514 0.918245 True \n", - "25515 0.984364 True \n", - "25516 0.860147 True \n", - "25517 0.85758 True \n", - "25518 0.886411 True \n", - "25519 0.878648 True \n", - "25520 0.707101 True \n", - "25521 0.925128 True \n", - "25522 0.923308 True \n", - "25523 0.980428 True \n", - "25524 0.965413 True " + " actual_result pass \n", + "18000 0.8726 True \n", + "18001 0.948315 True \n", + "18002 0.836957 True \n", + "18003 0.820327 True \n", + "18004 0.779661 True \n", + "18005 0.858586 True \n", + "18006 0.446429 False \n", + "18007 0.834711 True \n", + "18008 0.69735 False \n", + "18009 0.977315 True \n", + "18010 0.572491 False \n", + "18011 0.826325 True \n", + "18012 0.578616 False \n", + "18013 0.548387 False \n", + "18014 0.297619 False \n", + "18015 0.717584 True \n", + "18016 0.775194 True \n", + "18017 0.962597 True \n", + "18018 0.679912 False \n", + "18019 0.823315 True \n", + "18020 0.66426 False \n", + "18021 0.669291 False \n", + "18022 0.357143 False \n", + "18023 0.771729 True \n", + "18024 0.932656 True " ] }, - "execution_count": 34, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -5214,23 +8047,21 @@ "id": "73T33c_kfZJh" }, "source": [ - "### Report of the tests" + "#### Report of the tests" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 740 }, "execution": { - "iopub.execute_input": "2023-07-28T08:22:32.533979Z", - "iopub.status.busy": "2023-07-28T08:22:32.533440Z", - "iopub.status.idle": "2023-07-28T08:22:38.547138Z", - "shell.execute_reply": "2023-07-28T08:22:38.546618Z", - "shell.execute_reply.started": "2023-07-28T08:22:32.533962Z" + "iopub.execute_input": "2023-08-19T18:22:42.797648Z", + "iopub.status.busy": "2023-08-19T18:22:42.797287Z", + "iopub.status.idle": "2023-08-19T18:22:43.255897Z" }, "id": "PpzNHUNg8rox", "outputId": "ed9763d0-9b04-4745-d5b1-ef1882814e5b", @@ -5272,210 +8103,160 @@ " 0\n", " robustness\n", " uppercase\n", - " 356\n", - " 1144\n", - " 76%\n", - " 60%\n", - " True\n", + " 907\n", + " 593\n", + " 40%\n", + " 70%\n", + " False\n", " \n", " \n", " 1\n", " robustness\n", " lowercase\n", - " 190\n", - " 1310\n", - " 87%\n", - " 60%\n", + " 141\n", + " 1359\n", + " 91%\n", + " 70%\n", " True\n", " \n", " \n", " 2\n", " robustness\n", " titlecase\n", - " 281\n", - " 1219\n", - " 81%\n", + " 595\n", + " 905\n", " 60%\n", - " True\n", + " 70%\n", + " False\n", " \n", " \n", " 3\n", " robustness\n", " add_punctuation\n", - " 0\n", - " 1500\n", - " 100%\n", - " 60%\n", + " 11\n", + " 1489\n", + " 99%\n", + " 70%\n", " True\n", " \n", " \n", " 4\n", " robustness\n", " strip_punctuation\n", - " 23\n", - " 1477\n", + " 27\n", + " 1473\n", " 98%\n", - " 60%\n", + " 70%\n", " True\n", " \n", " \n", " 5\n", " robustness\n", " add_slangs\n", - " 112\n", - " 1388\n", + " 99\n", + " 1401\n", " 93%\n", - " 60%\n", + " 70%\n", " True\n", " \n", " \n", " 6\n", " robustness\n", " dyslexia_word_swap\n", - " 138\n", - " 1362\n", - " 91%\n", - " 60%\n", + " 166\n", + " 1334\n", + " 89%\n", + " 70%\n", " True\n", " \n", " \n", " 7\n", " robustness\n", " add_abbreviation\n", - " 275\n", - " 1225\n", - " 82%\n", - " 60%\n", + " 337\n", + " 1163\n", + " 78%\n", + " 70%\n", " True\n", " \n", " \n", " 8\n", " robustness\n", " add_speech_to_text_typo\n", - " 358\n", - " 1142\n", - " 76%\n", - " 60%\n", - " True\n", + " 493\n", + " 1007\n", + " 67%\n", + " 70%\n", + " False\n", " \n", " \n", " 9\n", " robustness\n", " number_to_word\n", - " 395\n", - " 1105\n", - " 74%\n", - " 60%\n", + " 422\n", + " 1078\n", + " 72%\n", + " 70%\n", " True\n", " \n", " \n", " 10\n", " robustness\n", " add_ocr_typo\n", - " 263\n", - " 1237\n", - " 82%\n", - " 60%\n", + " 445\n", + " 1055\n", + " 70%\n", + " 70%\n", " True\n", " \n", " \n", " 11\n", " robustness\n", " adjective_synonym_swap\n", - " 126\n", - " 1374\n", + " 125\n", + " 1375\n", " 92%\n", - " 60%\n", + " 70%\n", " True\n", " \n", " \n", " 12\n", - " bias\n", - " replace_to_male_pronouns\n", - " 26\n", - " 1474\n", - " 98%\n", - " 66%\n", - " True\n", - " \n", - " \n", - " 13\n", - " bias\n", - " replace_to_female_pronouns\n", - " 27\n", - " 1473\n", - " 98%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 14\n", - " bias\n", - " replace_to_inter_racial_lastnames\n", - " 236\n", - " 1264\n", - " 84%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 15\n", - " bias\n", - " replace_to_native_american_lastnames\n", - " 237\n", - " 1263\n", - " 84%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 16\n", - " bias\n", - " replace_to_asian_lastnames\n", - " 189\n", - " 1311\n", - " 87%\n", - " 60%\n", - " True\n", - " \n", - " \n", - " 17\n", " accuracy\n", " min_precision_score\n", - " 0\n", - " 8\n", - " 100%\n", - " 65%\n", + " 1\n", + " 7\n", + " 88%\n", + " 70%\n", " True\n", " \n", " \n", - " 18\n", + " 13\n", " accuracy\n", " min_recall_score\n", - " 0\n", - " 8\n", - " 100%\n", - " 65%\n", - " True\n", + " 5\n", + " 3\n", + " 38%\n", + " 70%\n", + " False\n", " \n", " \n", - " 19\n", + " 14\n", " accuracy\n", " min_f1_score\n", - " 0\n", - " 8\n", - " 100%\n", - " 65%\n", - " True\n", + " 4\n", + " 4\n", + " 50%\n", + " 70%\n", + " False\n", " \n", " \n", - " 20\n", + " 15\n", " accuracy\n", " min_micro_f1_score\n", " 0\n", " 1\n", " 100%\n", - " 65%\n", + " 70%\n", " True\n", " \n", " \n", @@ -5483,54 +8264,44 @@ "" ], "text/plain": [ - " category test_type fail_count pass_count \\\n", - "0 robustness uppercase 356 1144 \n", - "1 robustness lowercase 190 1310 \n", - "2 robustness titlecase 281 1219 \n", - "3 robustness add_punctuation 0 1500 \n", - "4 robustness strip_punctuation 23 1477 \n", - "5 robustness add_slangs 112 1388 \n", - "6 robustness dyslexia_word_swap 138 1362 \n", - "7 robustness add_abbreviation 275 1225 \n", - "8 robustness add_speech_to_text_typo 358 1142 \n", - "9 robustness number_to_word 395 1105 \n", - "10 robustness add_ocr_typo 263 1237 \n", - "11 robustness adjective_synonym_swap 126 1374 \n", - "12 bias replace_to_male_pronouns 26 1474 \n", - "13 bias replace_to_female_pronouns 27 1473 \n", - "14 bias replace_to_inter_racial_lastnames 236 1264 \n", - "15 bias replace_to_native_american_lastnames 237 1263 \n", - "16 bias replace_to_asian_lastnames 189 1311 \n", - "17 accuracy min_precision_score 0 8 \n", - "18 accuracy min_recall_score 0 8 \n", - "19 accuracy min_f1_score 0 8 \n", - "20 accuracy min_micro_f1_score 0 1 \n", + " category test_type fail_count pass_count pass_rate \\\n", + "0 robustness uppercase 907 593 40% \n", + "1 robustness lowercase 141 1359 91% \n", + "2 robustness titlecase 595 905 60% \n", + "3 robustness add_punctuation 11 1489 99% \n", + "4 robustness strip_punctuation 27 1473 98% \n", + "5 robustness add_slangs 99 1401 93% \n", + "6 robustness dyslexia_word_swap 166 1334 89% \n", + "7 robustness add_abbreviation 337 1163 78% \n", + "8 robustness add_speech_to_text_typo 493 1007 67% \n", + "9 robustness number_to_word 422 1078 72% \n", + "10 robustness add_ocr_typo 445 1055 70% \n", + "11 robustness adjective_synonym_swap 125 1375 92% \n", + "12 accuracy min_precision_score 1 7 88% \n", + "13 accuracy min_recall_score 5 3 38% \n", + "14 accuracy min_f1_score 4 4 50% \n", + "15 accuracy min_micro_f1_score 0 1 100% \n", "\n", - " pass_rate minimum_pass_rate pass \n", - "0 76% 60% True \n", - "1 87% 60% True \n", - "2 81% 60% True \n", - "3 100% 60% True \n", - "4 98% 60% True \n", - "5 93% 60% True \n", - "6 91% 60% True \n", - "7 82% 60% True \n", - "8 76% 60% True \n", - "9 74% 60% True \n", - "10 82% 60% True \n", - "11 92% 60% True \n", - "12 98% 66% True \n", - "13 98% 60% True \n", - "14 84% 60% True \n", - "15 84% 60% True \n", - "16 87% 60% True \n", - "17 100% 65% True \n", - "18 100% 65% True \n", - "19 100% 65% True \n", - "20 100% 65% True " + " minimum_pass_rate pass \n", + "0 70% False \n", + "1 70% True \n", + "2 70% False \n", + "3 70% True \n", + "4 70% True \n", + "5 70% True \n", + "6 70% True \n", + "7 70% True \n", + "8 70% False \n", + "9 70% True \n", + "10 70% True \n", + "11 70% True \n", + "12 70% True \n", + "13 70% False \n", + "14 70% False \n", + "15 70% True " ] }, - "execution_count": 35, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -5548,10 +8319,3811 @@ "Called after harness.run() and it summarizes the results giving information about pass and fail counts and overall test pass/fail flag." ] }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-16T13:31:57.733856Z", + "iopub.status.busy": "2023-08-16T13:31:57.733468Z", + "iopub.status.idle": "2023-08-16T13:32:03.396983Z", + "shell.execute_reply": "2023-08-16T13:32:03.396074Z", + "shell.execute_reply.started": "2023-08-16T13:31:57.733839Z" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# saving the report in the form of csv\n", + "report=harness.report()\n", + "report.to_csv(\"report_med7.csv\",index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-25T22:38:12.206400Z", + "iopub.status.busy": "2023-08-25T22:38:12.205826Z", + "iopub.status.idle": "2023-08-25T22:38:12.210361Z", + "shell.execute_reply": "2023-08-25T22:38:12.209809Z", + "shell.execute_reply.started": "2023-08-25T22:38:12.206379Z" + }, + "tags": [] + }, + "source": [ + "#### Visualizing the Report" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-25T22:47:00.490054Z", + "iopub.status.busy": "2023-08-25T22:47:00.489592Z", + "iopub.status.idle": "2023-08-25T22:47:00.568244Z", + "shell.execute_reply": "2023-08-25T22:47:00.567799Z", + "shell.execute_reply.started": "2023-08-25T22:47:00.490034Z" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "Test Type=%{x}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "# Get unique categories\n", + "report['pass_rate'] = report['pass_rate'].str.rstrip('%').astype(float)\n", + "report['minimum_pass_rate'] = report['minimum_pass_rate'].str.rstrip('%').astype(float)\n", + "\n", + "# Get unique categories\n", + "# Get unique categories\n", + "unique_categories = report[\"category\"].unique()\n", + "\n", + "# Category 1: Robustness\n", + "category_data = report[report[\"category\"] == \"robustness\"]\n", + "\n", + "# Bar Plot: Pass Rate by Test Type for the Robustness Category\n", + "bar_fig = px.bar(category_data, x=\"test_type\", y=\"pass_rate\",\n", + " labels={\"pass_rate\": \"Pass Rate\", \"test_type\": \"Test Type\"},\n", + " title=\"Pass Rate by Test Type for Robustness Category\")\n", + "bar_fig.update_xaxes(tickangle=45)\n", + "bar_fig.update_layout(width=1000, height=700)\n", + "bar_fig.show()\n", + "\n", + "# Pie Chart: Distribution of Fail Count for the Robustness Category\n", + "pie_fig = px.pie(category_data, names=\"test_type\", values=\"fail_count\",\n", + " title=\"Distribution of Fail Count for Robustness Category\")\n", + "pie_fig.update_layout(width=800, height=600)\n", + "pie_fig.show()\n", + "\n", + "# Category 2: Accuracy\n", + "category_data = report[report[\"category\"] == \"accuracy\"]\n", + "\n", + "# Bar Plot: Pass Rate by Test Type for the Accuracy Category\n", + "bar_fig = px.bar(category_data, x=\"test_type\", y=\"pass_rate\",\n", + " labels={\"pass_rate\": \"Pass Rate\", \"test_type\": \"Test Type\"},\n", + " title=\"Pass Rate by Test Type for Accuracy Category\")\n", + "bar_fig.update_xaxes(tickangle=45)\n", + "bar_fig.update_layout(width=1000, height=700)\n", + "bar_fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-25T22:47:27.222601Z", + "iopub.status.busy": 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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "avg_pass_rate_by_category = report.groupby('category')['pass_rate'].mean().reset_index()\n", + "\n", + "# Create a table using Plotly\n", + "fig = go.Figure(data=[go.Table(\n", + " header=dict(values=['Category', 'Average Pass Rate (%)']),\n", + " cells=dict(values=[avg_pass_rate_by_category['category'], \n", + " avg_pass_rate_by_category['pass_rate'].apply(lambda x: f'{x:.2f}%')]))\n", + "])\n", + "\n", + "# Customize the layout\n", + "fig.update_layout(title='Average Pass Rates by Category - Table')\n", + "\n", + "# Show the interactive table\n", + "fig.show()" + ] + }, { "attachments": { - "image.png": { - "image/png": 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" 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" } }, "cell_type": "markdown", @@ -5561,7 +12133,17 @@ "\n", "comparing the results of `ner_posology_langtest` from JSL and `med7` from spacy.\n", "\n", - "![image.png](attachment:image.png)" + "![Screenshot 2023-08-21 213253.png](attachment:d05b23eb-9d29-4af7-b092-79ee7c19cb12.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After evaluating the performance of the ner posology and med7 models in terms of Robustness, and Accuracy, here are the findings:\n", + "\n", + "- **Accuracy**: The ner_posology model demonstrated exceptional accuracy, achieving a perfect pass rate across all accuracy-related tests. In contrast, the med7 model exhibited weaknesses, with failures observed in precision, recall, and F1 score tests.\n", + "- **Robustness**: In terms of robustness, the ner posology model outperformed the med7 model in most test categories. This indicates the ner posology model’s ability to effectively handle a wider range of inputs, highlighting its superior robustness." ] }, { @@ -5571,19 +12153,11 @@ }, "source": [ "## Conclusion\n", + "In conclusion, while accuracy is undoubtedly crucial, robustness testing takes natural language processing (NLP) models evaluation to the next level by ensuring that models can perform reliably and consistently across a wide array of real-world conditions.\n", "\n", - "After evaluating the performance of the `Med7` and `ner_posology_langtest` models in terms of accuracy, robustness, and bias, we can draw the following conclusions:\n", - "\n", - "- **Accuracy:** The \"ner_posology_langtest\" model demonstrated superior accuracy, achieving a perfect pass rate for all accuracy-related tests. In contrast, the \"Med7\" model had some failures in precision, recall, and F1 score tests.\n", - "\n", - "- **Robustness:** Both models exhibited robustness in handling various text manipulation tasks. The \"ner_posology_langtest\" model, in particular, displayed slightly higher pass rates in most tests, indicating its ability to handle a wide range of inputs effectively.\n", - "\n", - "- **Bias:** Both models performed admirably in bias tests, passing all assessments related to gender, and race biases.\n", - "\n", - "Moving forward, it is important to address the identified weaknesses of these models to further enhance their performance. One potential solution is to augment the training set using langtest. By incorporating additional data from langtest. To explore the implementation of langtest for augmentation, you can refer to the [Augmentation Control Notebook](https://github.com/JohnSnowLabs/langtest/blob/main/demo/tutorials/misc/Augmentation_Control_Notebook.ipynb).\n", + "To further enhance the performance of these models, it is crucial to address any identified weaknesses. One potential solution is to consider augmenting the training set using langtest. By incorporating langtest for augmentation, we can potentially improve the models’ generalization and adaptability to diverse patterns and contexts.\n", "\n", - "Considering these factors, the \"ner_posology_langtest\" model stands out as the better performer, excelling in accuracy and demonstrating commendable robustness without displaying any biases. However, it's worth noting that further analysis and evaluation may be necessary to obtain a comprehensive understanding of their overall performance.\n", - "\n" + "To explore the implementation of langtest for augmentation, I recommend referring to the [Generating Augmentations](https://langtest.org/docs/pages/docs/generate_augmentation) section of the langtest website. This approach may lead to more robust models that can deliver even better results in various applications." ] } ],