Email Classification Model for Single Text Feature.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "epUBTaEqb2fE",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "![giskard_logo.png](https://raw.githubusercontent.com/Giskard-AI/giskard/main/readme/Logo_full_darkgreen.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JvW0aV-n0aFE",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# About Giskard\n",
    "\n",
    "Open-Source CI/CD platform for ML teams. Deliver ML products, better & faster. \n",
    "\n",
    "*   Collaborate faster with feedback from business stakeholders.\n",
    "*   Deploy automated tests to eliminate regressions, errors & biases.\n",
    "\n",
    "🏡 [Website](https://giskard.ai/)\n",
    "\n",
    "📗 [Documentation](https://docs.giskard.ai/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing `giskard` and other packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install giskard transformers torch nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect the external worker in daemon mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!giskard worker start -d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mqxJYzAU0hKC",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Start by creating a ML model 🚀🚀🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xKSXB9A8X9z4",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Download the categorized email files from Berkeley."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "osMPzLZuXtIJ",
    "outputId": "d53485aa-5cd9-4ad6-b55a-7d0f99397c62",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "!wget http:https://bailando.sims.berkeley.edu/enron/enron_with_categories.tar.gz\n",
    "!tar zxf enron_with_categories.tar.gz\n",
    "!rm enron_with_categories.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "raBI-KqFxUJA",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import email\n",
    "import glob\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from string import punctuation\n",
    "\n",
    "import pandas as pd\n",
    "from dateutil import parser\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import model_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQkpEBbZYJUC",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Various imports and the list of categories from http:https://bailando.sims.berkeley.edu/enron/enron_categories.txt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8Bgkv4U7YOZ6",
    "outputId": "1d9aff49-e8a2-4174-d390-a631a1c7c07f",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "stoplist = set(stopwords.words('english') + list(punctuation))\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "\n",
    "# http:https://bailando.sims.berkeley.edu/enron/enron_categories.txt\n",
    "idx_to_cat = {\n",
    "    1: 'REGULATION',\n",
    "    2: 'INTERNAL',\n",
    "    3: 'INFLUENCE',\n",
    "    4: 'INFLUENCE',\n",
    "    5: 'INFLUENCE',\n",
    "    6: 'CALIFORNIA CRISIS',\n",
    "    7: 'INTERNAL',\n",
    "    8: 'INTERNAL',\n",
    "    9: 'INFLUENCE',\n",
    "    10: 'REGULATION',\n",
    "    11: 'talking points',\n",
    "    12: 'meeting minutes',\n",
    "    13: 'trip reports'}\n",
    "\n",
    "idx_to_cat2 = {\n",
    "    1: 'regulations and regulators (includes price caps)',\n",
    "    2: 'internal projects -- progress and strategy',\n",
    "    3: ' company image -- current',\n",
    "    4: 'company image -- changing / influencing',\n",
    "    5: 'political influence / contributions / contacts',\n",
    "    6: 'california energy crisis / california politics',\n",
    "    7: 'internal company policy',\n",
    "    8: 'internal company operations',\n",
    "    9: 'alliances / partnerships',\n",
    "    10: 'legal advice',\n",
    "    11: 'talking points',\n",
    "    12: 'meeting minutes',\n",
    "    13: 'trip reports'}\n",
    "\n",
    "\n",
    "LABEL_CAT = 3  # we'll be using the 2nd-level category \"Primary topics\" because the two first levels provide categories that are not mutually exclusive. see : https://bailando.berkeley.edu/enron/enron_categories.txt\n",
    "\n",
    "#get_labels returns a dictionary representation of these labels. \n",
    "def get_labels(filename):\n",
    "    with open(filename + '.cats') as f:\n",
    "        labels = defaultdict(dict)\n",
    "        line = f.readline()\n",
    "        while line:\n",
    "            line = line.split(',')\n",
    "            top_cat, sub_cat, freq = int(line[0]), int(line[1]), int(line[2])\n",
    "            labels[top_cat][sub_cat] = freq\n",
    "            line = f.readline()\n",
    "    return dict(labels)\n",
    "\n",
    "\n",
    "email_files = [f.replace('.cats', '') for f in glob.glob('enron_with_categories/*/*.cats')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nhXid2KuYprZ",
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Build dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lpeOLv-vkoMC",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "columns_name = ['Target', 'Subject', 'Content', 'Week_day', 'Year', 'Month', 'Hour', 'Nb_of_forwarded_msg']\n",
    "\n",
    "\n",
    "data = pd.DataFrame(columns=columns_name)\n",
    "\n",
    "for email_file in email_files:\n",
    "    values_to_add = {}\n",
    "\n",
    "    #Target is the sub-category with maximum frequency\n",
    "    if LABEL_CAT in get_labels(email_file):\n",
    "      sub_cat_dict = get_labels(email_file)[LABEL_CAT]\n",
    "      target_int = max(sub_cat_dict, key=sub_cat_dict.get)\n",
    "      values_to_add['Target'] = str(idx_to_cat[target_int])\n",
    "\n",
    "    #Features are metadata from the email object\n",
    "    filename = email_file+'.txt'\n",
    "    with open(filename) as f:\n",
    "\n",
    "      message = email.message_from_string(f.read())\n",
    "  \n",
    "      values_to_add['Subject'] = str(message['Subject'])\n",
    "      values_to_add['Content'] = str(message.get_payload())\n",
    "     \n",
    "      date_time_obj = parser.parse(message['Date'])\n",
    "      values_to_add['Week_day'] = date_time_obj.strftime(\"%A\")\n",
    "      values_to_add['Year'] = date_time_obj.strftime(\"%Y\")\n",
    "      values_to_add['Month'] = date_time_obj.strftime(\"%B\")\n",
    "      values_to_add['Hour'] = int(date_time_obj.strftime(\"%H\"))\n",
    "\n",
    "      # Count number of forwarded mails\n",
    "      number_of_messages = 0\n",
    "      for line in message.get_payload().split('\\n'):\n",
    "        if ('forwarded' in line.lower() or 'original' in line.lower()) and '--' in line:\n",
    "            number_of_messages += 1\n",
    "      values_to_add['Nb_of_forwarded_msg'] = number_of_messages\n",
    "    \n",
    "    row_to_add = pd.Series(values_to_add)\n",
    "    data = data.append(row_to_add, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Filter Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#We filter 879 rows (if Primary topics exists (i.e. if coarse genre 1.1 is selected) )\n",
    "data_filtered = data[data[\"Target\"].notnull()]\n",
    "\n",
    "#Exclude target category with very few rows ; 812 rows remains\n",
    "excluded_category = [idx_to_cat[i] for i in [11,12,13]]\n",
    "data_filtered = data_filtered[data_filtered[\"Target\"].isin(excluded_category) == False]\n",
    "num_classes = len(data_filtered[\"Target\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "column_types={       \n",
    "        'Target': \"category\",\n",
    "        \"Content\": \"text\"\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Training with scikit learn pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# feature_types is used to declare the features the model is trained on\n",
    "feature_types = {i:column_types[i] for i in column_types if i!='Target'}\n",
    "\n",
    "# Pipeline for text transformer\n",
    "text_transformer = Pipeline([\n",
    "                      ('vect', CountVectorizer(stop_words=stoplist)),\n",
    "                      ('tfidf', TfidfTransformer())\n",
    "                     ])\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "      ('text_Mail', text_transformer, \"Content\")\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Pipeline for the model Logistic Regression\n",
    "clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
    "                      ('classifier', LogisticRegression(max_iter =1000))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Split train/test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "feature_types = {i:column_types[i] for i in column_types if i!=\"Target\"}\n",
    "Y = data_filtered[\"Target\"]\n",
    "X = data_filtered.drop(columns=[\"Target\"])[list(feature_types.keys())]\n",
    "X_train,X_test,Y_train,Y_test = model_selection.train_test_split(X, Y,test_size=0.20, random_state = 30, stratify = Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Learning phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Fit and score your model\n",
    "clf.fit(X_train, Y_train)\n",
    "print(\"model score: %.3f\" % clf.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Prepare data to upload on Giskard\n",
    "train_data = pd.concat([X_train, Y_train], axis=1)\n",
    "test_data = pd.concat([X_test, Y_test ], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Upload the model in Giskard 🚀🚀🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Initiate a project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from giskard import GiskardClient\n",
    "\n",
    "url = \"http:https://localhost:19000\" # If Giskard is installed locally (for installation, see: https://docs.giskard.ai/start/guides/installation)\n",
    "#url = \"http:https://app.giskard.ai\" # If you want to upload on giskard URL\n",
    "token = \"YOUR GENERATED TOKEN\" # you can generate your API token in the Admin tab of the Giskard application (for installation, see: https://docs.giskard.ai/start/guides/installation)\n",
    "\n",
    "client = GiskardClient(url, token)\n",
    "\n",
    "# your_project = client.create_project(\"project_key\", \"PROJECT_NAME\", \"DESCRIPTION\")\n",
    "# Choose the arguments you want. But \"project_key\" should be unique and in lower case\n",
    "enron = client.create_project(\"enron_demo\", \"Enron Mails Classification\", \"Project to classify enron mails\")\n",
    "\n",
    "# If you've already created a project with the key \"enron_demo\" use\n",
    "#enron = client.get_project(\"enron_demo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Upload your model and a dataset (see [documentation](https://docs.giskard.ai/start/guides/upload-your-model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "enron.upload_model_and_df(\n",
    "    prediction_function=clf.predict_proba, # Python function which takes pandas dataframe as input and returns probabilities for classification model OR returns predictions for regression model\n",
    "    model_type='classification', # \"classification\" for classification model OR \"regression\" for regression model\n",
    "    df=test_data, # The dataset you want to use to inspect your model\n",
    "    column_types=column_types, # # A dictionary with columns names of df as key and types(category, numeric, text) of columns as values\n",
    "    target='Target', # The column name in df corresponding to the actual target variable (ground truth).\n",
    "    feature_names=list(feature_types.keys()), # List of the feature names of prediction_function\n",
    "    model_name='logistic_regression_model', # Name of the model\n",
    "    dataset_name='test_data', # Name of the dataset\n",
    "    classification_labels=clf.classes_ # List of the classification labels of your prediction\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 🌟 If you want to upload a dataset without a model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## For example, let's upload the train set in Giskard, this is key to create drift tests in Giskard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "enron.upload_df(\n",
    "    df=train_data, # The dataset you want to upload\n",
    "    column_types=column_types, # All the column types of df\n",
    "    target=\"Target\", # Do not pass this parameter if dataset doesn't contain target column\n",
    "    name=\"train_data\" # Name of the dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "You can also upload new production data to use it as a validatation set for your existing model. In that case, you might not have the ground truth target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "production_data = data.drop(columns=\"Target\")[list(feature_types.keys())]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "enron.upload_df(\n",
    "    df=production_data, # The dataset you want to upload\n",
    "    column_types={\"Content\": \"text\"}, # All the column types without the target\n",
    "    name=\"production_data\"  # Name of the dataset\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 🌟 If you just want to upload a model without a dataframe\n",
    "#### Lets try a Hugging Face pytorch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\n",
    "import torch\n",
    "from transformers import TrainingArguments, Trainer\n",
    "from transformers import BertTokenizer, BertForSequenceClassification\n",
    "\n",
    "np.random.seed(112)\n",
    "\n",
    "# Read data\n",
    "data = data_filtered\n",
    "\n",
    "# Define pretrained tokenizer and model\n",
    "# model_name = \"bert-base-uncased\" # For large BERT Model\n",
    "model_name = \"cross-encoder/ms-marco-TinyBERT-L-2\" # For tiny BERT Model\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "model = BertForSequenceClassification.from_pretrained(model_name, num_labels=4, ignore_mismatched_sizes=True)\n",
    "\n",
    "for param in model.base_model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# ----- 1. Preprocess data -----#\n",
    "# Preprocess data\n",
    "\n",
    "X = list(data[\"Content\"])\n",
    "classification_labels_mapping = {'REGULATION': 0,'INTERNAL': 1,'CALIFORNIA CRISIS': 2,'INFLUENCE': 3}\n",
    "y = list(data_filtered['Target'].map(classification_labels_mapping))\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)\n",
    "X_train_tokenized = tokenizer(X_train, padding=True, truncation=True, max_length=128)\n",
    "X_val_tokenized = tokenizer(X_val, padding=True, truncation=True, max_length=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Create torch dataset\n",
    "class Dataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels=None):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        if self.labels:\n",
    "            item[\"labels\"] = torch.tensor(self.labels[idx])\n",
    "        return item\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.encodings[\"input_ids\"])\n",
    "\n",
    "train_dataset = Dataset(X_train_tokenized, y_train)\n",
    "val_dataset = Dataset(X_val_tokenized, y_val)\n",
    "\n",
    "# ----- 2. Fine-tune pretrained model -----#\n",
    "# Define Trainer parameters\n",
    "def compute_metrics(p):\n",
    "    pred, labels = p\n",
    "    pred = np.argmax(pred, axis=1)\n",
    "\n",
    "    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n",
    "    recall = recall_score(y_true=labels, y_pred=pred, average='macro')\n",
    "    precision = precision_score(y_true=labels, y_pred=pred, average='macro')\n",
    "    f1 = f1_score(y_true=labels, y_pred=pred, average='macro')\n",
    "\n",
    "    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n",
    "\n"
   ]
  },
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   "execution_count": null,
   "metadata": {
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    "# Define Trainer\n",
    "args = TrainingArguments(\n",
    "    output_dir=\"output\",\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=500,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    num_train_epochs=1,\n",
    "    seed=0,\n",
    "    load_best_model_at_end=True,\n",
    ")\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=args,\n",
    "    train_dataset=train_dataset,\n",
    "    eval_dataset=val_dataset,\n",
    "    compute_metrics=compute_metrics\n",
    "    )\n",
    "\n",
    "# Train pre-trained model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
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    }
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    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
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     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def predict(test_dataset):\n",
    "    test_dataset= test_dataset.squeeze() if len(test_dataset) >1 else test_dataset\n",
    "    X_test = list(test_dataset)\n",
    "    X_test_tokenized = tokenizer(X_test, padding=True, truncation=True, max_length=512)\n",
    "\n",
    "    # Create torch dataset\n",
    "    test_dataset = Dataset(X_test_tokenized)\n",
    "\n",
    "    # Define test trainer\n",
    "    test_trainer = Trainer(model)\n",
    "\n",
    "    # Make prediction\n",
    "    raw_pred, _, _ = test_trainer.predict(test_dataset)\n",
    "    predictions = torch.nn.functional.softmax(torch.from_numpy(raw_pred), dim=-1)\n",
    "    predictions = predictions.cpu().detach().numpy()\n",
    "\n",
    "    return predictions"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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    }
   },
   "outputs": [],
   "source": [
    "feature_names = ['Content']\n",
    "test_df = data_filtered[feature_names][:5]\n",
    "predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "enron.upload_model(\n",
    "    prediction_function=predict, # Python function which takes pandas dataframe as input and returns probabilities for classification model OR returns predictions for regression model\n",
    "    model_type='classification', # \"classification\" for classification model OR \"regression\" for regression model\n",
    "    validate_df=data_filtered.head(), # Optional. Validation df is not uploaded in the app, it's only used to check whether the model has the good format\n",
    "    target='Target', # Optional. target should be a column of validate_df. Pass this parameter only if validate_df is being passed\n",
    "    feature_names=feature_names,# List of the feature names of prediction_function\n",
    "    name='bert_pytorch_model', # Name of the model\n",
    "    classification_labels=list(classification_labels_mapping.keys()) # List of the classification labels of your prediction\n",
    ")"
   ]
  }
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