зеркало из https://github.com/microsoft/glue.git
771 строка
157 KiB
Plaintext
771 строка
157 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# LUIS Scoring Evaluation\n",
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"This notebook serves to evaluate your LUIS scoring report generated by [GLUE](https://github.com/microsoft/glue)."
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"source": [
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"# Import required packages\r\n",
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"import pandas as pd\r\n",
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"import numpy as np\r\n",
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"import json\r\n",
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"from pandas.io.json import json_normalize\r\n",
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"from sklearn.metrics import classification_report\r\n",
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"from sklearn.metrics import confusion_matrix\r\n",
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"from scikitplot.metrics import plot_roc_curve\r\n",
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"import seaborn as sns; sns.set(rc={'figure.figsize':(10,10)})\r\n",
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"import matplotlib.pyplot as plt \r\n",
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"\r\n",
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"# Notebook configs\r\n",
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"%matplotlib inline\r\n",
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"%load_ext autoreload\r\n",
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"%autoreload 2"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Input Data\n",
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"Below, you will import the scoring file generated by GLUE. "
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"source": [
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"# Import scoring file\r\n",
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"df_scoring = pd.read_csv('../assets/examples/output_files/example_luis_scoring.csv', sep=\",\", encoding=\"utf-8\")\r\n",
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"df_scoring.head()"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>intent</th>\n",
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" <th>text</th>\n",
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" <th>prediction_text</th>\n",
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" <th>score_text</th>\n",
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" <th>prediction_drop_text</th>\n",
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" <th>luis_treshold</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>BookFlight</td>\n",
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" <td>I would like to book a flight to Frankfurt.</td>\n",
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" <td>BookFlight</td>\n",
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" <td>0.898678</td>\n",
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" <td>None</td>\n",
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" <td>0.9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>CancelFlight</td>\n",
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" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
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" <td>CancelFlight</td>\n",
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" <td>0.915191</td>\n",
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" <td>CancelFlight</td>\n",
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" <td>0.9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>ChangeFlight</td>\n",
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" <td>I would like to change my flight to Singapore.</td>\n",
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" <td>ChangeFlight</td>\n",
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" <td>0.916477</td>\n",
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" <td>ChangeFlight</td>\n",
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" <td>0.9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>BookSeat</td>\n",
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" <td>I would like to book a seat on my flight to St...</td>\n",
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" <td>BookSeat</td>\n",
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" <td>0.910444</td>\n",
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" <td>BookSeat</td>\n",
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" <td>0.9</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>CancelFlight</td>\n",
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" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
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" <td>CancelFlight</td>\n",
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" <td>0.915191</td>\n",
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" <td>CancelFlight</td>\n",
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" <td>0.9</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" intent text \\\n",
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"0 BookFlight I would like to book a flight to Frankfurt. \n",
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"1 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
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"2 ChangeFlight I would like to change my flight to Singapore. \n",
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"3 BookSeat I would like to book a seat on my flight to St... \n",
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"4 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
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"\n",
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" prediction_text score_text prediction_drop_text luis_treshold \n",
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"0 BookFlight 0.898678 None 0.9 \n",
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"1 CancelFlight 0.915191 CancelFlight 0.9 \n",
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"2 ChangeFlight 0.916477 ChangeFlight 0.9 \n",
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"3 BookSeat 0.910444 BookSeat 0.9 \n",
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"4 CancelFlight 0.915191 CancelFlight 0.9 "
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]
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},
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"metadata": {},
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"execution_count": 2
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}
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"source": [
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"# Set treshold of scoring file to a variable\r\n",
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"treshold_default = df_scoring['luis_treshold'].loc[0]"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"source": [
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"# Set dummy variables for treshold steps\r\n",
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"treshold_range = np.arange(0.0, 1.0, 0.05)\r\n",
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"treshold_labels = [f'above_treshold_{str(treshold).replace(\".\", \"\")[:3]}' for treshold in treshold_range]\r\n",
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"df_tresholds = df_scoring.copy()\r\n",
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"for treshold in zip(treshold_range, treshold_labels):\r\n",
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" df_tresholds[treshold[1]] = df_tresholds.score_text.apply(lambda x: x >= treshold[0]).astype(int)"
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],
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"outputs": [],
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"metadata": {}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Classification Reports\n",
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"\n",
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"### Report for Predictions"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"source": [
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"print(classification_report(df_scoring.intent, df_scoring.prediction_text))\n",
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"sns.heatmap(confusion_matrix(df_scoring.intent, df_scoring.prediction_text), annot=True)"
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],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" BookFlight 0.50 1.00 0.67 2\n",
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" BookSeat 1.00 0.60 0.75 5\n",
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"CancelFlight 1.00 0.50 0.67 4\n",
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"ChangeFlight 0.71 1.00 0.83 5\n",
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"\n",
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" accuracy 0.75 16\n",
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" macro avg 0.80 0.78 0.73 16\n",
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"weighted avg 0.85 0.75 0.74 16\n",
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"\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x275cbc3fe08>"
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]
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},
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"metadata": {},
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"execution_count": 5
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"metadata": {
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"scrolled": false,
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"tags": []
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}
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{
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"cell_type": "markdown",
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"source": [
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"### Report for treshold-validated Predictions"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"source": [
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"print(classification_report(df_scoring.intent, df_scoring['prediction_drop_text']))\n",
|
|
"sns.heatmap(confusion_matrix(df_scoring.intent, df_scoring['prediction_drop_text']), annot=True)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" BookFlight 0.00 0.00 0.00 2\n",
|
|
" BookSeat 1.00 0.60 0.75 5\n",
|
|
"CancelFlight 1.00 0.50 0.67 4\n",
|
|
"ChangeFlight 0.71 1.00 0.83 5\n",
|
|
" None 0.00 0.00 0.00 0\n",
|
|
"\n",
|
|
" accuracy 0.62 16\n",
|
|
" macro avg 0.54 0.42 0.45 16\n",
|
|
"weighted avg 0.79 0.62 0.66 16\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x275df220788>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 6
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"image/png": 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"<Figure size 432x288 with 2 Axes>"
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}
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{
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"cell_type": "markdown",
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"source": [
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"#### Wrong Classifications"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"source": [
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"# Create df only including wrong classifications\n",
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"df_wrong_classifications = df_scoring[~(df_scoring.intent == df_scoring.prediction_text)]\n",
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"display(df_wrong_classifications)"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>intent</th>\n",
|
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" <th>text</th>\n",
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|
" <th>prediction_text</th>\n",
|
|
" <th>score_text</th>\n",
|
|
" <th>prediction_drop_text</th>\n",
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" <th>luis_treshold</th>\n",
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|
" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>7</th>\n",
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|
" <td>CancelFlight</td>\n",
|
|
" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
|
|
" <td>ChangeFlight</td>\n",
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|
" <td>0.915191</td>\n",
|
|
" <td>ChangeFlight</td>\n",
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|
" <td>0.9</td>\n",
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|
" </tr>\n",
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|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>BookSeat</td>\n",
|
|
" <td>I would like to book a seat on my flight to St...</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.910444</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.9</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>CancelFlight</td>\n",
|
|
" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
|
|
" <td>ChangeFlight</td>\n",
|
|
" <td>0.915191</td>\n",
|
|
" <td>ChangeFlight</td>\n",
|
|
" <td>0.9</td>\n",
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|
" </tr>\n",
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|
" <tr>\n",
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|
" <th>14</th>\n",
|
|
" <td>BookSeat</td>\n",
|
|
" <td>I would like to book a seat on my flight to St...</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.910444</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" </tr>\n",
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|
" </tbody>\n",
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|
"</table>\n",
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"</div>"
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],
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"text/plain": [
|
|
" intent text \\\n",
|
|
"7 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
|
|
"9 BookSeat I would like to book a seat on my flight to St... \n",
|
|
"12 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
|
|
"14 BookSeat I would like to book a seat on my flight to St... \n",
|
|
"\n",
|
|
" prediction_text score_text prediction_drop_text luis_treshold \n",
|
|
"7 ChangeFlight 0.915191 ChangeFlight 0.9 \n",
|
|
"9 BookFlight 0.910444 BookFlight 0.9 \n",
|
|
"12 ChangeFlight 0.915191 ChangeFlight 0.9 \n",
|
|
"14 BookFlight 0.910444 BookFlight 0.9 "
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|
]
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},
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"metadata": {}
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}
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],
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"metadata": {
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"scrolled": false
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}
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},
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{
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"cell_type": "code",
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|
"execution_count": 8,
|
|
"source": [
|
|
"# Create bar plot of frequently misclassified values, if there are any\n",
|
|
"if len(df_wrong_classifications) > 0:\n",
|
|
" print(df_scoring[~(df_scoring.intent == df_scoring.prediction_text)]['intent'].value_counts())\n",
|
|
" df_scoring[~(df_scoring.intent == df_scoring.prediction_text)]['intent'].value_counts().sort_values().plot(kind = 'barh')\n",
|
|
"else:\n",
|
|
" print(\"Nothing to display, as no wrong classifications!\")"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"BookSeat 2\n",
|
|
"CancelFlight 2\n",
|
|
"Name: intent, dtype: int64\n"
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|
]
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"metadata": {
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"tags": []
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Bad scores, but correctly classified"
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],
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"source": [
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"df_correct_bad_score = df_scoring.loc[(df_scoring.score_text < treshold_default) & (df_scoring.intent == df_scoring.prediction_text)]\n",
|
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"display(df_correct_bad_score)"
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],
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"outputs": [
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{
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>intent</th>\n",
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" <th>text</th>\n",
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" <th>prediction_text</th>\n",
|
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" <th>score_text</th>\n",
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" <th>prediction_drop_text</th>\n",
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" <th>luis_treshold</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
|
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" <td>BookFlight</td>\n",
|
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" <td>I would like to book a flight to Frankfurt.</td>\n",
|
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" <td>BookFlight</td>\n",
|
|
" <td>0.898678</td>\n",
|
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" <td>None</td>\n",
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" <td>0.9</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>15</th>\n",
|
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" <td>BookFlight</td>\n",
|
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" <td>I would like to book a flight to Frankfurt.</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.898678</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
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],
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"text/plain": [
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" intent text prediction_text \\\n",
|
|
"0 BookFlight I would like to book a flight to Frankfurt. BookFlight \n",
|
|
"15 BookFlight I would like to book a flight to Frankfurt. BookFlight \n",
|
|
"\n",
|
|
" score_text prediction_drop_text luis_treshold \n",
|
|
"0 0.898678 None 0.9 \n",
|
|
"15 0.898678 None 0.9 "
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"source": [
|
|
"# Distribution of bad scores but correctly classified\n",
|
|
"am = sns.boxplot(y='score_text', data=df_correct_bad_score)\n",
|
|
"am.set_ylim([0, 1])"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
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"(0, 1)"
|
|
]
|
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},
|
|
"metadata": {},
|
|
"execution_count": 10
|
|
},
|
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{
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"output_type": "display_data",
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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}
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}
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],
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"metadata": {}
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},
|
|
{
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|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"source": [
|
|
"# Most frequent correct classifications even tho having bad confidence scores\n",
|
|
"df_scoring.loc[(df_scoring.score_text < treshold_default) & (df_scoring.intent == df_scoring.prediction_text)]['prediction_text'].value_counts()"
|
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],
|
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"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
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"data": {
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"text/plain": [
|
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"BookFlight 2\n",
|
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"Name: prediction_text, dtype: int64"
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]
|
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},
|
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"metadata": {},
|
|
"execution_count": 11
|
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}
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],
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"metadata": {}
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},
|
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{
|
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"cell_type": "markdown",
|
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"source": [
|
|
"### Good scores, but wrongly classified"
|
|
],
|
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"metadata": {}
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 12,
|
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"source": [
|
|
"print('Good scores, but wrongly classified')\n",
|
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"display(df_scoring[(df_scoring.score_text > treshold_default) & (df_scoring.intent != df_scoring.prediction_text)].head())"
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],
|
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"outputs": [
|
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{
|
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"output_type": "stream",
|
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"name": "stdout",
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"text": [
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"Good scores, but wrongly classified\n"
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]
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},
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{
|
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"output_type": "display_data",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>intent</th>\n",
|
|
" <th>text</th>\n",
|
|
" <th>prediction_text</th>\n",
|
|
" <th>score_text</th>\n",
|
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" <th>prediction_drop_text</th>\n",
|
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" <th>luis_treshold</th>\n",
|
|
" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>CancelFlight</td>\n",
|
|
" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
|
|
" <td>ChangeFlight</td>\n",
|
|
" <td>0.915191</td>\n",
|
|
" <td>ChangeFlight</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>BookSeat</td>\n",
|
|
" <td>I would like to book a seat on my flight to St...</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.910444</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>CancelFlight</td>\n",
|
|
" <td>I want to cancel my journey to Kuala Lumpur</td>\n",
|
|
" <td>ChangeFlight</td>\n",
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|
" <td>0.915191</td>\n",
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|
" <td>ChangeFlight</td>\n",
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|
" <td>0.9</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>BookSeat</td>\n",
|
|
" <td>I would like to book a seat on my flight to St...</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.910444</td>\n",
|
|
" <td>BookFlight</td>\n",
|
|
" <td>0.9</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" intent text \\\n",
|
|
"7 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
|
|
"9 BookSeat I would like to book a seat on my flight to St... \n",
|
|
"12 CancelFlight I want to cancel my journey to Kuala Lumpur \n",
|
|
"14 BookSeat I would like to book a seat on my flight to St... \n",
|
|
"\n",
|
|
" prediction_text score_text prediction_drop_text luis_treshold \n",
|
|
"7 ChangeFlight 0.915191 ChangeFlight 0.9 \n",
|
|
"9 BookFlight 0.910444 BookFlight 0.9 \n",
|
|
"12 ChangeFlight 0.915191 ChangeFlight 0.9 \n",
|
|
"14 BookFlight 0.910444 BookFlight 0.9 "
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"tags": []
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"source": [
|
|
"# Distribution of good scores but wrongly classified\n",
|
|
"ax = sns.boxplot(y='score_text', data=df_scoring[(df_scoring.score_text > treshold_default) & (df_scoring.intent != df_scoring.prediction_text)])\n",
|
|
"ax.set_ylim([0, 1])"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"(0, 1)"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 13
|
|
},
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
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"image/png": 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