notebooks/transcription_confidence.ipynb
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"source": [
"# Confidence Levels of Google Cloud Vision API's transcriptions\n",
"## `merged_df` csv has the API transcriptions, human transcriptions, calculated error between the two and the confidence level for each submission\n",
"### `confidence_flag.py` file in the app is created from this notebook"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Build csv with both api and human transcriptions"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"import pandas as pd\n",
"import pylcs\n",
"import os.path\n",
"import seaborn as sns\n",
"from scipy import stats"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Bring in the human transcribed and Google API transcribed csv's\n",
"human = pd.read_csv('human_metrics.csv')\n",
"api = pd.read_csv('api_metrics.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Keep only the transcriptions and the story_ID\n",
"# Rename to specify api transcripption or human transcription\n",
"\n",
"human = human[['story_id', 'transcription']].rename(columns={'transcription': 'human transcription'})\n",
"api = api[['story_id', 'transcription']].rename(columns={'transcription': 'api transcription'})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# There were 3 story_id's that were not correct\n",
"# That also made the datatype of the entire column an object - need to convert to int to merge\n",
"\n",
"human['story_id'][55] = 3215\n",
"human['story_id'][64] = 3213\n",
"human['story_id'][104] = 5240\n",
"\n",
"human['story_id'] = human['story_id'].astype(str).astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n\n api transcription \n0 Page. I 3132 Once there was a little cheatah a... \n1 D she was very, a berenang The pony that didn'... \n2 Rainbow the Unica unicom named some een Pearl ... \n3 O gum drop land gumdrop. land is prace Were gu... \n4 The secret fifth grade E am Anella I am starti... ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"# Merge two tables on story_id\n",
"merged_df = pd.merge(human, api, on= 'story_id')\n",
"merged_df.head()"
]
},
{
"source": [
"### Calculate error and add to df"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def calc_error(t1, t2):\n",
" \"\"\"Takes in two different transcriptions of a story, returns the error score\"\"\"\n",
"\n",
" # cut out the spaces in both -- the human transcripts use spaces visually\n",
" # this may also eliminate real space errors but I think it's better\n",
" t1 = t1.replace(\" \", \"\")\n",
" t2 = t2.replace(\" \", \"\")\n",
"\n",
" # transcripts may differ in exact length\n",
" avg_length = (len(t1) + len(t2)) / 2\n",
"\n",
" # result: average number of single-character errors per character\n",
" return pylcs.levenshtein_distance(t1, t2) / avg_length"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Add an error column to have index locations to index to\n",
"merged_df['error'] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n5 3105 My alarm clock went off. I got out of bed, put... \n6 3129 "found it" said Katrina, a villan who roamed a... \n7 3116 1 So once there was a turtle name Cacti. He li... \n8 3111 Lia Hi my name is Lia Clark. My twin sister's ... \n9 3118 1 " This is my jungle. I had it first and you ... \n\n api transcription error \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 \n1 D she was very, a berenang The pony that didn'... 0.364450 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 \n4 The secret fifth grade E am Anella I am starti... 0.136770 \n5 brushed my My darm dock went off. I got out of... 0.145624 \n6 found it" said Katrina, a villan who roamed ar... 0.064485 \n7 so a vermewhere log and 1 once there was turtl... 0.169014 \n8 Hi my name is lia Clark, My twin sister's name... 0.025674 \n9 you this you you want 1 "This is my jungle. I ... 0.112222 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n </tr>\n <tr>\n <th>5</th>\n <td>3105</td>\n <td>My alarm clock went off. I got out of bed, put...</td>\n <td>brushed my My darm dock went off. I got out of...</td>\n <td>0.145624</td>\n </tr>\n <tr>\n <th>6</th>\n <td>3129</td>\n <td>\"found it\" said Katrina, a villan who roamed a...</td>\n <td>found it\" said Katrina, a villan who roamed ar...</td>\n <td>0.064485</td>\n </tr>\n <tr>\n <th>7</th>\n <td>3116</td>\n <td>1 So once there was a turtle name Cacti. He li...</td>\n <td>so a vermewhere log and 1 once there was turtl...</td>\n <td>0.169014</td>\n </tr>\n <tr>\n <th>8</th>\n <td>3111</td>\n <td>Lia Hi my name is Lia Clark. My twin sister's ...</td>\n <td>Hi my name is lia Clark, My twin sister's name...</td>\n <td>0.025674</td>\n </tr>\n <tr>\n <th>9</th>\n <td>3118</td>\n <td>1 \" This is my jungle. I had it first and you ...</td>\n <td>you this you you want 1 \"This is my jungle. I ...</td>\n <td>0.112222</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"# Calculate the error between the two transcriptions - populate error column\n",
"for i in range(merged_df.shape[0]):\n",
" merged_df.loc[i, 'error'] = calc_error(merged_df.loc[i, 'human transcription'], merged_df.loc[i, 'api transcription'])\n",
"\n",
"merged_df.head(10)"
]
},
{
"source": [
"### Calculate confidence and add to df\n",
"- Final method we used; calculate the confidence of each character in the entire submission, then return the average confidence\n",
"- `confidence_flag.py` in the app uses the `image_confidence` method and calculates the confidence level at the character level and raises a flag is the confidence for a page is lower than 0.85."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [],
"source": [
"def image_confidence(image_path):\n",
" '''\n",
" Detects text in images and calculates the confidence level for each\n",
" character. Returns a True boolean if the overall confidence for the\n",
" page is less than 0.85. Otherwise, returns False\n",
"\n",
" Input: Path to file where image is stored\n",
" One image per call: run function on each image in a submission\n",
" Output: Boolean; True if confidence level for page is less than 0.85\n",
" False if confidence is 0.85 or greater\n",
" '''\n",
" # Imports needed specificially for this function\n",
" from google.cloud import vision\n",
" import io\n",
" from google.oauth2 import service_account\n",
"\n",
" # If image_path is local\n",
" with io.open(image_path, 'rb') as image_file:\n",
" content = image_file.read()\n",
" image = vision.types.Image(content=content)\n",
"\n",
" # # If image_path is a uri\n",
" # image = vision.types.Image()\n",
" # image.source.image_uri = uri\n",
"\n",
" # Set language to english only\n",
" language = vision.types.ImageContext(language_hints=['en-t-i0-handwrit'])\n",
"\n",
" # Connect to Google API client\n",
" creds = service_account.Credentials.from_service_account_file('/Users/stevenchase/Desktop/Steven/Computer_Science/Lambda/labs/story_sqaud/Story Squad-6122da7459cf.json')\n",
" client = vision.ImageAnnotatorClient(credentials=creds)\n",
" response = client.document_text_detection(image=image, image_context=language)\n",
"\n",
" # List of confidence levels of each word\n",
" symbol_confidences = []\n",
"\n",
" for page in response.full_text_annotation.pages:\n",
" for block in page.blocks:\n",
" for paragraph in block.paragraphs:\n",
" for word in paragraph.words:\n",
" for symbol in word.symbols:\n",
" symbol_confidences.append(symbol.confidence)\n",
" \n",
" # If there is no text on the page, needs to return a list\n",
" if len(symbol_confidences) == 0:\n",
" return [-1]\n",
" else:\n",
" # Return list of all of the confidences for each character\n",
" return symbol_confidences"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {},
"outputs": [],
"source": [
"def multi_page(directory):\n",
" '''\n",
" Function calls image_confidence function for each page in a single submission\n",
" Input: file path containing images of user's written stories\n",
" Output: Float; Confidence in the transcription of the entire submission\n",
" '''\n",
"\n",
" paths = []\n",
"\n",
" for item in os.listdir(directory):\n",
" if item[-3:] == 'jpg':\n",
" path = os.path.join(directory, item)\n",
" paths.append(path)\n",
"\n",
" submission_confidence = []\n",
"\n",
" for image_path in paths:\n",
" submission_confidence.extend(image_confidence(image_path))\n",
" \n",
" # Drop any '-1' (meaning no text detected) responses from confidence function\n",
" submission_confidence = [x for x in submission_confidence if x != -1]\n",
"\n",
" # Check to make sure that submission_confidence isn't empty\n",
" if len(submission_confidence) == 0:\n",
" return 0\n",
" else:\n",
" confidence_level = sum(submission_confidence) / len(submission_confidence)\n",
" return confidence_level"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
"# Add a confidence column to be able to refer to the index\n",
"merged_df['confidence'] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n.. ... ... \n162 5132 One day, in the middle of August, there was a ... \n163 5103 => "Hey[]" exclaimed Tommy, "thats my phone, w... \n164 5104 One day a kid named Steven was at school he ha... \n165 5105 []The wind was howeling against my window. [] ... \n166 5102 There I was flying over a cliff. My name is Tr... \n\n api transcription error confidence \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 0.0 \n1 D she was very, a berenang The pony that didn'... 0.364450 0.0 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 0.0 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 0.0 \n4 The secret fifth grade E am Anella I am starti... 0.136770 0.0 \n.. ... ... ... \n162 up One day, in the middle of August, there was... 0.089368 0.0 \n163 mom the S103 Indent Heytilen exclaimed Tommy, ... 0.251528 0.0 \n164 he from la so as -5164 No Hone Work One day a ... 0.598857 0.0 \n165 S AT was 2 2 The wind howeling against my wind... 0.302930 0.0 \n166 over was is Trey, and aro am a free ouns Ibuil... 0.440937 0.0 \n\n[167 rows x 5 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>162</th>\n <td>5132</td>\n <td>One day, in the middle of August, there was a ...</td>\n <td>up One day, in the middle of August, there was...</td>\n <td>0.089368</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>163</th>\n <td>5103</td>\n <td>=> \"Hey[]\" exclaimed Tommy, \"thats my phone, w...</td>\n <td>mom the S103 Indent Heytilen exclaimed Tommy, ...</td>\n <td>0.251528</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>164</th>\n <td>5104</td>\n <td>One day a kid named Steven was at school he ha...</td>\n <td>he from la so as -5164 No Hone Work One day a ...</td>\n <td>0.598857</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>165</th>\n <td>5105</td>\n <td>[]The wind was howeling against my window. [] ...</td>\n <td>S AT was 2 2 The wind howeling against my wind...</td>\n <td>0.302930</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>166</th>\n <td>5102</td>\n <td>There I was flying over a cliff. My name is Tr...</td>\n <td>over was is Trey, and aro am a free ouns Ibuil...</td>\n <td>0.440937</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>167 rows × 5 columns</p>\n</div>"
},
"metadata": {},
"execution_count": 189
}
],
"source": [
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"# Root directory\n",
"root_directory = os.path.join('/Users/stevenchase/Desktop/Steven/Computer_Science/Lambda/labs/story_sqaud', 'Stories Dataset', \"Transcribed Stories\")"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n\n api transcription error confidence \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 0.959946 \n1 D she was very, a berenang The pony that didn'... 0.364450 0.894102 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 0.946982 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 0.895751 \n4 The secret fifth grade E am Anella I am starti... 0.136770 0.916551 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n <td>0.959946</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n <td>0.894102</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n <td>0.946982</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n <td>0.895751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n <td>0.916551</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 197
}
],
"source": [
"# Calculate the confidence for each transcription\n",
"for i in range(merged_df.shape[0]):\n",
" # Build the directory path based off of the story_id\n",
" directory = os.path.join(root_directory, str(merged_df['story_id'][i])[:2] + '--', str(merged_df['story_id'][i]))\n",
"\n",
" # Populate confidence column with call of multi_page function on each row\n",
" merged_df.loc[i, 'confidence'] = multi_page(directory)\n",
"\n",
"merged_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"merged_df.to_csv('error_confidence_metrics.csv')"
]
},
{
"source": [
"### Explore confidence metrics and relationship with calculated error\n",
"- First Correlation metric: -0.25\n",
"- Correlation after dropping the missing images: -0.46\n",
"\n",
"Final method of getting confidence level (by character for the entire submission)\n",
"- First Correlation metric: -0.29\n",
"- After dropping the missing images: -0.49"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "count 167.000000\nmean 0.905906\nstd 0.044304\nmin 0.728981\n25% 0.895665\n50% 0.916216\n75% 0.935226\nmax 0.961698\nName: confidence, dtype: float64"
},
"metadata": {},
"execution_count": 80
}
],
"source": [
"# Explore distribution of confidence \n",
"merged_df['confidence'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"# Scatter plot to compare relationship between error and confidence level\n",
"sns.regplot(x=\"error\", y=\"confidence\", data=merged_df);\n"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"ax = sns.distplot(merged_df['confidence'])\n",
"plt.title(\"Distribution of Confidence of API Transcription\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\nstory_id 1.000000 -0.050345 0.023507\nerror -0.050345 1.000000 -0.299523\nconfidence 0.023507 -0.299523 1.000000",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>story_id</th>\n <td>1.000000</td>\n <td>-0.050345</td>\n <td>0.023507</td>\n </tr>\n <tr>\n <th>error</th>\n <td>-0.050345</td>\n <td>1.000000</td>\n <td>-0.299523</td>\n </tr>\n <tr>\n <th>confidence</th>\n <td>0.023507</td>\n <td>-0.299523</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 199
}
],
"source": [
"merged_df.corr()"
]
},
{
"source": [
"### Explore outliers and reasons for bad correlation"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\n0 3132 0.055957 0.959946\n1 3104 0.364450 0.894102\n2 3103 0.383740 0.946982\n3 3117 0.270862 0.895751\n4 3102 0.136770 0.916551",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>0.055957</td>\n <td>0.959946</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>0.364450</td>\n <td>0.894102</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>0.383740</td>\n <td>0.946982</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>0.270862</td>\n <td>0.895751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>0.136770</td>\n <td>0.916551</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 200
}
],
"source": [
"# Copy of df to manipulate\n",
"\n",
"confidence_df = merged_df[['story_id', 'error', 'confidence']]\n",
"confidence_df.head()"
]
},
{
"source": [
"### Some stories had missing pages for the human transcription which resulted in inaccurate error calculations. Those were dropped below.\n",
"### Additionally, we found the following human transcription errors. We believe this was a small enough sample size that it didn't affect the correlation too much. But they are important to keep in mind so I will note them here for future iterations.\n",
"- Story 5202: There is text added to a sentence by the child using a ^. It is placed out of order. There are also a few '[]' throughout the transcription. One is for something that is crossed out. The others are smudges on the paper. Typo 'Jlack' instead of 'Jack' on the second page.\n",
"- Story 3109: Text added by ^ is out of place. Includes some text from an illustration. [] brackets where words are crossd out. \n",
"- Story 3114: Carrot out of place\n",
"\n",
"The convention seems to be the text associated with a carrot (^) where the student is adding to a sentence is out of order. Words that are crossed out are indicated by []. Sometimes text in illustrations are included but most times they are not. The human transcriptions errors do not seem too drastic to affect the calculated errors. \n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [],
"source": [
"# Drop stories that had known human error in the the transcriptions - missing pages\n",
"to_drop = [3213, 3215, 3240, 5104, 5109, 5262]"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "55\n64\n66\n80\n145\n164\n"
}
],
"source": [
"# Drop outliers to see the relationship better\n",
"\n",
"for i in range(confidence_df.shape[0]):\n",
" if confidence_df['story_id'][i] in to_drop:\n",
" confidence_df = confidence_df.drop(i)\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.regplot(x=\"error\", y=\"confidence\", data=confidence_df);"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\nstory_id 1.000000 -0.028041 0.030630\nerror -0.028041 1.000000 -0.492487\nconfidence 0.030630 -0.492487 1.000000",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>story_id</th>\n <td>1.000000</td>\n <td>-0.028041</td>\n <td>0.030630</td>\n </tr>\n <tr>\n <th>error</th>\n <td>-0.028041</td>\n <td>1.000000</td>\n <td>-0.492487</td>\n </tr>\n <tr>\n <th>confidence</th>\n <td>0.030630</td>\n <td>-0.492487</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 204
}
],
"source": [
"confidence_df.corr()"
]
},
{
"source": [
"### Explore values that have a high confidence but also have a high error metric"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\n16 3128 0.610937 0.895146\n33 3216 0.454545 0.951636\n34 3229 0.408889 0.918442\n60 3246 0.427553 0.940928\n62 3222 0.474832 0.949171\n93 5208 0.415114 0.903336\n99 5241 0.411483 0.902887\n141 5122 0.459770 0.895210\n144 5114 0.631939 0.911629\n158 5119 0.546148 0.932757\n161 5110 0.401645 0.900754\n166 5102 0.440937 0.930952",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>16</th>\n <td>3128</td>\n <td>0.610937</td>\n <td>0.895146</td>\n </tr>\n <tr>\n <th>33</th>\n <td>3216</td>\n <td>0.454545</td>\n <td>0.951636</td>\n </tr>\n <tr>\n <th>34</th>\n <td>3229</td>\n <td>0.408889</td>\n <td>0.918442</td>\n </tr>\n <tr>\n <th>60</th>\n <td>3246</td>\n <td>0.427553</td>\n <td>0.940928</td>\n </tr>\n <tr>\n <th>62</th>\n <td>3222</td>\n <td>0.474832</td>\n <td>0.949171</td>\n </tr>\n <tr>\n <th>93</th>\n <td>5208</td>\n <td>0.415114</td>\n <td>0.903336</td>\n </tr>\n <tr>\n <th>99</th>\n <td>5241</td>\n <td>0.411483</td>\n <td>0.902887</td>\n </tr>\n <tr>\n <th>141</th>\n <td>5122</td>\n <td>0.459770</td>\n <td>0.895210</td>\n </tr>\n <tr>\n <th>144</th>\n <td>5114</td>\n <td>0.631939</td>\n <td>0.911629</td>\n </tr>\n <tr>\n <th>158</th>\n <td>5119</td>\n <td>0.546148</td>\n <td>0.932757</td>\n </tr>\n <tr>\n <th>161</th>\n <td>5110</td>\n <td>0.401645</td>\n <td>0.900754</td>\n </tr>\n <tr>\n <th>166</th>\n <td>5102</td>\n <td>0.440937</td>\n <td>0.930952</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 205
}
],
"source": [
"# Create sub_df to explore the worst correlation data points\n",
"condition_1 = confidence_df['error'] >= 0.4\n",
"condition_2 = confidence_df['confidence'] >= 0.85\n",
"\n",
"outlier_df = confidence_df[condition_1 & condition_2]\n",
"outlier_df"
]
},
{
"source": [
"Notes on the top 12 worst correlations:\n",
"\n",
"3128: Handwriting seemed fine. API transcription had a lot of words out of order which is what lead to the higher error metric. Could have been because some of it was faint handwriting.\n",
"\n",
"3216: Handwriting was fine, it was written front and back so you could faitly make out some of the words written on the back. Wonder if that threw google off. Might be able to clean that up\n",
"\n",
"3229: Transcription wasn't actually that bad. But it was a very short piece of writing so the error metric was heavily influenced by a few mistakes.\n",
"\n",
"3246: Some light writing\n",
"\n",
"3222: Faint handwriting\n",
"\n",
"5208: Page is crowded, the words are all pushed together and the letters are tall on the lines\n",
"\n",
"5241: IDK\n",
"\n",
"5122: Crossed out the first paragraph, google still tried to transcribe that\n",
"\n",
"5114: Some erase marks in few paragraphs, other than that good handwriting\n",
"\n",
"5119: IDK\n",
"\n",
"5110: Messy, different colors, crossed out material\n",
"\n",
"5102: Crossed out words"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 216,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\n24 3112 0.177743 0.844136\n27 3124 0.751310 0.777286\n86 5264 0.616956 0.801282\n116 5202 0.248390 0.818169\n143 5113 0.161404 0.787450",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>24</th>\n <td>3112</td>\n <td>0.177743</td>\n <td>0.844136</td>\n </tr>\n <tr>\n <th>27</th>\n <td>3124</td>\n <td>0.751310</td>\n <td>0.777286</td>\n </tr>\n <tr>\n <th>86</th>\n <td>5264</td>\n <td>0.616956</td>\n <td>0.801282</td>\n </tr>\n <tr>\n <th>116</th>\n <td>5202</td>\n <td>0.248390</td>\n <td>0.818169</td>\n </tr>\n <tr>\n <th>143</th>\n <td>5113</td>\n <td>0.161404</td>\n <td>0.787450</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 216
}
],
"source": [
"# Low confidence subset to explore any commonalities\n",
"low_confidence_df = confidence_df[confidence_df['confidence'] < 0.85]\n",
"low_confidence_df"
]
},
{
"source": [
"Notes on confidence below 0.85\n",
"\n",
"3112: IDK\n",
"\n",
"3124, 5264, 5202: words pushed together\n",
"\n",
"5113: small writing, words are spaced but letters are crowded"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# Experimental work\n",
"## Explore different ways of calculating confidence levels, see if that effects the correlation\n",
"### Look at the first 25 samples\n",
"- confidence by word per page\n",
"- confidence by word entire submission at once\n",
"- confidence by character entrie submission at once\n",
"\n",
"We ended up getting the best correlation for the first 25 samlpes by retrieving the confidence level of every character across the entire submission before aggregating a total confidence level - will use that method on the entire dataset of stories"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "55\n64\n66\n80\n145\n164\n"
}
],
"source": [
"# Drop the stories that have missing data\n",
"cleaned_df = merged_df.copy()\n",
"\n",
"for i in range(cleaned_df.shape[0]):\n",
" if cleaned_df['story_id'][i] in to_drop:\n",
" cleanded_df = cleaned_df.drop(i)\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "(25, 5)"
},
"metadata": {},
"execution_count": 166
}
],
"source": [
"# Subset of first 25 rows to experiment with\n",
"df_25 = cleaned_df[:25]\n",
"df_25.shape"
]
},
{
"source": [
"### Confidence by word per page metrics"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.regplot(x=\"error\", y=\"confidence\", data=confidence_df[:25]);"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\nstory_id 1.000000 -0.131402 0.151173\nerror -0.131402 1.000000 -0.273108\nconfidence 0.151173 -0.273108 1.000000",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>story_id</th>\n <td>1.000000</td>\n <td>-0.131402</td>\n <td>0.151173</td>\n </tr>\n <tr>\n <th>error</th>\n <td>-0.131402</td>\n <td>1.000000</td>\n <td>-0.273108</td>\n </tr>\n <tr>\n <th>confidence</th>\n <td>0.151173</td>\n <td>-0.273108</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 176
}
],
"source": [
"confidence_df[:25].corr()"
]
},
{
"source": [
"### Confidence by character by page"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [],
"source": [
"# Function to get submission confidence by character per page\n",
"def character_page_confidence(image_path):\n",
" '''\n",
" Detects document features in images and returns overall confidence level of transcribed text\n",
" Input: Path to file where images are stored\n",
" - Assuming 1 image per image_path - run this on each individual image in one submission\n",
" - Code for both local image_path and remote image_path, comment out\n",
" the apporopriate one\n",
" Output: Float. Decimal format of confidence level of the transcription for the page\n",
" '''\n",
"\n",
" from google.cloud import vision\n",
" import io\n",
" from google.oauth2 import service_account\n",
"\n",
" # If image_path is local\n",
" with io.open(image_path, 'rb') as image_file:\n",
" content = image_file.read()\n",
" image = vision.types.Image(content=content)\n",
"\n",
" # # If image_path is a uri\n",
" # image = vision.types.Image()\n",
" # image.source.image_uri = uri\n",
"\n",
" # Set language to english only\n",
" language = vision.types.ImageContext(language_hints=['en-t-i0-handwrit'])\n",
"\n",
" # Connect to Google API client\n",
" creds = service_account.Credentials.from_service_account_file('/Users/stevenchase/Desktop/Steven/Computer_Science/Lambda/labs/story_sqaud/Story Squad-6122da7459cf.json')\n",
" client = vision.ImageAnnotatorClient(credentials=creds)\n",
" response = client.document_text_detection(image=image, image_context=language)\n",
"\n",
" # List of confidence levels of each word\n",
" symbol_confidences = []\n",
"\n",
" for page in response.full_text_annotation.pages:\n",
" for block in page.blocks:\n",
" for paragraph in block.paragraphs:\n",
" for word in paragraph.words:\n",
" # word_confidences.append(word.confidence)\n",
" for symbol in word.symbols:\n",
" symbol_confidences.append(symbol.confidence)\n",
" # If there is no text on the page\n",
" if len(symbol_confidences) == 0:\n",
" return None\n",
" else:\n",
" # Calculate and return the overall confidence for the page\n",
" page_confidence = sum(symbol_confidences) / len(symbol_confidences)\n",
"\n",
" return float(page_confidence)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [],
"source": [
"def character_multi_page(directory):\n",
" '''\n",
" Function calls page_confidence function for each page in a single submission\n",
" Input: file path containing images of user's written stories\n",
" Output: Float; Confidence in the transcription of the entire submission\n",
" '''\n",
" \n",
" paths = []\n",
"\n",
" for item in os.listdir(directory):\n",
" if item[-3:] == 'jpg':\n",
" path = os.path.join(directory, item)\n",
" paths.append(path)\n",
"\n",
" submission_confidence = []\n",
"\n",
" for image_path in paths:\n",
" submission_confidence.append(character_page_confidence(image_path))\n",
" \n",
" # Drop any 'None' responses from confidence function\n",
" submission_confidence = [x for x in submission_confidence if x is not None]\n",
"\n",
" # Check to make sure that submission_confidence isn't empty\n",
" if submission_confidence == 0:\n",
" return 0\n",
" else:\n",
" confidence_level = sum(submission_confidence) / len(submission_confidence)\n",
" return confidence_level"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [],
"source": [
"df_25['confidence'] = 0.0"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [],
"source": [
"# Root directory\n",
"root_directory = os.path.join('/Users/stevenchase/Desktop/Steven/Computer_Science/Lambda/labs/story_sqaud', 'Stories Dataset', \"Transcribed Stories\")"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n\n api transcription error confidence \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 0.958198 \n1 D she was very, a berenang The pony that didn'... 0.364450 0.917615 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 0.959991 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 0.895751 \n4 The secret fifth grade E am Anella I am starti... 0.136770 0.917641 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n <td>0.958198</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n <td>0.917615</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n <td>0.959991</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n <td>0.895751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n <td>0.917641</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 172
}
],
"source": [
"# Calculate the confidence for each transcription\n",
"for i in range(df_25.shape[0]):\n",
" # Build the directory path based off of the story_id\n",
" directory = os.path.join(root_directory, str(df_25['story_id'][i])[:2] + '--', str(df_25['story_id'][i]))\n",
"\n",
" # Populate confidence column with call of multi_page function on each row\n",
" df_25.loc[i, 'confidence'] = character_multi_page(directory)\n",
"\n",
"df_25.head()"
]
},
{
"source": [
"#### Confidence by character per page metrics"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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VG9n/M13s6m6nq6N52Q7V5PcRj0VsWgD+8Nt2VfT/OGJBEDGbuh2LGE2xIBfSFJ3ZEykuMli8wE/7ZkgjqkuS2t0zZnY38AjBNNcvuftRMzsA9Lr7QWA/cK+ZOcEQ04dq1Z5GVqsS4mZGV0czXR3NvPGqDYXjFyfSHA/zGl8/0s9IMlNY4Jd1J5txHn12kEefDUqKdCZiRaVEOti1qZ0tyyQZvth9xHPu5LKX/hKVz39ELAgkn3vsOBGD5liUnOd/ZviTQ8/zll0btQ5Elo2aroNw94eBh0uO3VN0+yHgoTle48vAl2vQvCWR7x2UDm/UU2dLnNduW8drt63jW0+eYMfGVtxhMpNjMp0jmckynspiBumsczGZ4YmXz/PEy+cLr5GIR7iqq316MnxDW90rwJZain3Ep/Ifgf7hcToTMTLZqYR6LGK8dG6Ml8+Nh/enhrFi0QjRSDBLKxpRfkQah2ox1VDx8EZnIsbZsUnu+/YxPkxlwxtLoThJnv8zkY6wc2M7n3nvz/LSufFpC/yeHxxlLJUlmc5x9ORFjpYmwze0hT2NNnZ1d3BVd9u0Ynv1vL68Wu8jXsl7ZnI5LrWdePHQlpkRsTCoRIxo1LCSc6JmhR5d/jkii6UAUUOLHd5YCpdKkseiQS/hqq523nldcH7OnVMXkmH9qZFC8BgeD5PhQ0F5EcJMkwGb17VMq3a7q7udta1Ndb++Rn7P8kNb2bLnlmNmRC0IJrHIVO4kn5iP2FTvRWQ2qsU0i2okqW/7wg/oTMSwohm/jjOSzPAXv/mGxTaxaqqRJD87Olk07Tb4c+pCctbzN7Y3Tat2e/WmdjbVKBlej33El8Pe5RAEkljEiESCXoqR77VM3Y7Y1GORSP5+2HMJg01EvZZlS8X6FqAaAeKjX39qxlDDRDrLhrZm/uhXX73YJja8kWR6Rg2ql8+NM0vBWzoTMa7qbufqrvZCWZEt61obbitUKS8fbKKRYCZcPpBEDCJhDyZSdI4CSmOoW7G+1a4ewxuNpCMR5zXb1vGabesKx5LpLC8UVbw9NjjKC2fGSGVyXExmePLl8zxZnAyPRdjZ1cbV3R3Ttn9ttGS4BNOq01knnQXSc59fGOqKhsNhkTJ/TMGkntSDmEW11kEsl6GGesrmnJfPjRetCg8Cx9hk+TH3aMS4cn0ruzZN5TWu7mqnrVnfd1aqfE6leIjLcXIOuZwHORsPglTxEFgkDDKRomR/PuBEbOY6l/z91URDTAuwlAvlZCYvToYXDVOdG0vN+pzNa1tmbP+6vm1pkuGysiw2yBgsaqpyLhyHXYrpzhpikmXHzLhibQtXrG1h3zVdhePnxlIcGxwJSokMBSVF8snwE+cnOHF+gu88N1Q4f0Nb07SexjXdHWzqXL4rw2VpBD2S8Mtz5ZPHZrAwWOST/oS38z/zfw9zOce95H2LXiMfiMzgijUtS7ZOpuIAEW7us8vd/8nMWoCYu4/UrmkiM61va+L1Ozbw+h1TK8NHJzM8XzKD6sWzY+Qczo6lONt3jh/0nSuc35GITdv+9erudratVzJcqs/dgwqlU/9Z0GtkHbLh85dyzKeiAGFmv0lQVns9cBVB4b0/5RKluJe7tqYYifXRMPobmVyObM7J5BwvWuDkBJHfCcbSPRwLTWeDhPRKGcJrZO3NMV69dS2v3rq2cGwyneWFs9O3f+0bGmMyk2MkmeGH/ef5Yf9UMrwpFmFnccXbTe3s3NiuZLisapX2ID5EsAHQvwC4+zEzW9FVxyIRI1K0fiEaWVhp7Ew2F8zsyOVIZ4KgkcooeNRaczzK7ss62X1ZZ+FYPhleOvV2dDJDKpPj2VdGePaVqU5xxChaGR6WSe9up70KyfBGLMEiUqrSv+mT7p7Kj5eFlVr16VaBYM9laGH2AJPLBduN5nsoOQ96KVl3MtkcqWzQe8nOtoBAKhKNGDs2trFjYxvv2LMJCLrvr1xMThueOjY4ytnRFDmHvjNj9J0Z4x+eOV14ncvXJKb1NHZ1d8wrGb5UJVgUhGSxKg0Q3zGz/wNoMbN3EGzo8ze1a9bqku+txOfopLgHASQbBpF80EjleymZ3IwEl1yamXH5mhYuX9PCvl3Tk+GlPY0T54MCf6cuJDl1IcnhY2cK569va5oxg+qKNYmyyfClKMGyHOqASeOrNEB8Avgg8DRwF0GF1i/WqlFS3tS+zLOfk0xnmUznmMxkSWVzZLIzZ0XI3Na3NbF3x3r27pj6MB2bzBS2f80v8nvp7DjZnHNuLMXjL5zj8RemkuFtzVGuLql4e+WGtiWpMLsc6oBJ46s0QLQQ7OfwBQAzi4bHxmvVMFmYRDxKIh4F4oVjmTBhnskFQ1b5nke66L7Mra05xvVb1nL9lqlkeCqT48WzY4Ud/I4NjvB8mAwfm8zy1MAFnhq4UDi/KRYhZsZoMkNbc6ywd/ZkproVZpciCMnKV2mAeBT4BSC/Z2UL8A/Amy71JDO7EbiPYMOgL7r775c8fiXwJaALOAfc7u4DZvZzwJ8AnQSzkH/P3b9eYVulRD4PMpsg9zE1SyuT9UJQKczcUi+krKZYhGs2dXDNpo7CsWzO6R8Oy6SH6zWOD44ykgyS4fmlfmOpqQn2sYixsb2Zb/b2F4aoOhJxFqoeZc5l5ak0QCTcvbChsbuPmlnrpZ4Q9jI+D7yDYH/qI2Z20N2fKTrts8BX3f0rZvY24F7g/QQ9k/8pnC11BfCEmT3i7ueRqgvq3lw6AZILcx3jqSzjqeCDTsqLhvtibN/Qxi9cO5UMPz0yyfGwp3HkxXMcHxolHZb0zuScJ/vP82TR1NvLOhOFRX75IaoNbU0VLfJb7XXApDoqDRBjZvZad/9XADN7HTBXX3UvcNzd+8LnPAjcDBQHiD3AR8LbjwF/DeDuz+VPcPeTZjZI0MtQgKiTSMRIRILhq/VtTbhPJcfzM62CnkcwI0s9junMjMs6E1zWmeAtuzZyx5u3A3B+PDVjBtXAcPBP65WLSV65mOS/FyXD17XGp2//2t3O5WsTM7Z/3btzPR9ml+qAyaJUGiD+HfBNMzsZ3r8c+NU5nrMZ6C+6PwC8vuScp4BbCIah3g10mNkGdz+bP8HM9gJNwPMVtlWWgJnRHIsy25KAXH64KhcOVYXJ8mDqrpNW7gOAta1N3LB9PTdsn/rgHk9leH5wbMbK8EzOGR5P8/iLwzz+4nDh/Nam6LSV4bs2tXPl+taa7XUuq0dFAcLdj5jZbuBnCDYJe9bd5yroW64fXPqJ8HHgc2Z2B3AYOAFkCi9gdjnwNeAD7j5jTMPM7iRY4c22bdsquRQBDj07yP2H++gfHmfrulbu2reT/buru+4xEjGaIkYTs69ELk6eZ7NBjySVzZHK5FZ1D6S1KcbPblnDz25ZUziWyuR4KVwZ/ly+t3F6tDDs9/SJCzx9YioZHo8Gaz6KN2Xa2dUWTmAQqUzF1VzN7E3AdoqCirt/9RLnvxH4tLu/M7z/yfA5985yfjtB4NkS3u8EDgH3uvs352pftau5rlSHnh3knoNHiUetMPUxnXUO3HRd1YPEYuSDR76nkc6GvZGskuYQrHP4fx4tjMQyNpllMpsjFjHGU+Wry0UMtq5rLfQy8mXSO1sWngyXpXflhraq1g1bdDVXM/saQQ2mHzJV29CBWQMEcATYZWY7CHoGtwLvK3ndjcC5sHfwSYIZTZhZE/AtggT2nMFBKnf/4T7iUaO1KfjVtzbFGE9luP9wX0MFiPzMq9m+8WayOZKZHOOpDMlUjkxudSXNHzzSTzwaKcxS6kzEmUhnWd/axCfftXtaifRjp0cZGp0k5/DSuXFeOjfOo88OFl5rU2fztET4ru4ONrZXlgyXla3SHEQPsMfn8bXN3TNmdjfwCME01y+5+1EzOwD0uvtBYD9wr5k5wRDTh8Kn/wqwD9gQDj8B3OHuP6z0/aW8/uFx1pZ8Y2yJRxkYXl5LWmLRCO3RSKEuUn6WVb7W1WQmSyqzcvMcs61zOD2SpLszQXdngjdfvbHw2IXxdFgefaQQPAaGJ3Dg9MVJTl+c5HvHC6k/1rbEp9Wg2rWpnSvWtsxIhsvKVmmA+DFwGXBqPi/u7g8TrLouPnZP0e2HgIfKPO/PgT+fz3tJZbaua2VwJFnoQUCwT/aWdZectdzw8rOsAGieOp4vlpgPGhPp7IoIGvNd57CmNc7rrlzH666c2v51IpUtrAzP9zZeOBMkw89PpOl9aZjel0qT4VPbv17T3c6VG1qJRVXxdqWqNEBsBJ4xs8eBwjZr7n5TTVolNXPXvp3cc/Ao46nMtBzEXft21rtpNVEoltg0tbq8uIeRyuaYTC+/GlbVWOfQ0hTlVZvX8KrNU8nwdDbHS2fHi2ZQjXB8cIyJdDZMhl/k6RMXC+fHo8Gaj+IaVFd1t08LXLJ8VZSkNrO3ljvu7t+peosWSEnqyuVnMQ0Mj7OlRrOYlpvJTJZkWMNqMp0jnW38nMZS7Xeec+fE8MS0GlTHB0e5MFF+IqMBW9e3Thuiurq7nTVKhlfFUiap5zOLqXhHuVYg2kg7yilASDXlcs5kUU9jMrM8gsZScXfOjAbbvxYnwwcvsY97d0fz9LxGdztdHdr+db4acRZT6Y5ym1nhO8rJ6haJGC1N0XBoKpDN+bSAkVrFQcPM6OpopqujmTddVZQMn0jP2P61f3icnMPgyCSDI5P88/NTyfDORGz6yvBN7WxZp2R4o9COciIVikaC6cGtRXsDpbM5JtJZJlLBGP1qX5+xpiXOa69cx2uLk+HpLC8MjYXDU0GP44UzY6SzzsVkhidePs8TL09V0UnEI1zVNb2nsX1jG3Elw5ecdpQTWYR4NEI8GqEzEcfdw2KGWZLp7KrtXZRqiUfZc0Une66Y2v41k83xUrj9a3546vmh0fD/XY6jJy9y9ORUMjwWFkCcvv1r27TZeFJ92lFOpErMjLbmGG1FazMm0lnGJjOMp7LLbqZULcWiQS/hqq523nldcCznzqnz+e1fp3Ibw+NpMjkPyqYPjcLR4HwDNq9rmTaDald3O2tbK9/+VS6t0llMEYId5X6R4PfyCMH+Dg3zN15Jamlk7kGwyE+tTWW0218l3J2z4favx05P5TZeuZic9Tld7dNXhl+9qZ1NKygZ3pCzmBqdAoQsR/nE99hksM/GSljEtxRGkukZe4a/fC5IhpfTmYhxVVh76pqwDtWWda1V/aBdKg0zi8nMnuYSuQZ3v36RbRNZ1aYS3zGgmYlUltHJjILFHDoScV6zbR2v2TaVDE+ms7xwZmxaHaq+cFOmi8kMT758nieLk+GxCDu72tgVrgzftamd7RvaaIopGZ43Vw7ifwx/5mskfS38+WtoP2qRqstPrXVvIpnOKVjMQyIe5drLO7n28unJ8JeLkuHHB4M8xthklmQmxzOnRnjm1NRyrmjEuHJD67S8xtVd7YW80mpTaQ7ie+7+5rmO1ZOGmGSlyucvRiczjE8q2b1Y7s6pC8kZK8PPjaVmfc7mtS3T8xrd7axvq08yvGGGmIq0mdlb3P274Qu+CWirVgNFZHZmU8NQ3j41lVY9i4UxM65Y28IVa1vYd01X4fi5sWBl+LHTU0NUpy4EyfAT5yc4cX6C7zw3VDh/Q3vTtBLpu7rb2dS5cpLhUHkP4nUEezXkq3qdB34jv0d1I1APQlajZHoqWKQyWndRbaOTmcLK8HxP46WzY7MmwzsSscL2r/lNmbZWORnesLOYwl3ezN0vzHnyElOAkNUunc0xNplhdFLBopYm01leCLd/zQeN54fGZv1/3hwmw4uHqHZubF9wMrxhhpjM7HZ3/3Mz+2jJcQDc/Y+q1koRWZR4NMLa1ibWtjaRykwFC63orq7meJTdl3Wy+7KpZHg250XJ8JFwCu4Yo5MZJjM5fnJqhJ8UJcMjFnzQF5cTuaq7vbABVqOYqzX5XWQ6FvLiZnYjcB/BjnJfdPffL3n8SoKhqy7gHHC7uw+Ej30A+FR46v/t7l9ZSBtEVqOmWISmWBPr2poK6yzGFCxqJhoxdmxsY8fGNt6xZxMQJMNfuZicVrjw2OAoZ0dT5BxeODPGC2fG+MdnThde54q1iRnbv9YrGQ5zDDGZ2X9y998xs1+e797QZg+Ul+UAABMmSURBVBYFngPeAQwQ7FF9m7s/U3TON4G/dfevmNnbgF939/eb2Xqgl2CrUweeAF7n7sOl75OnISaRuaUyuaCwYDpDMp1b9cUF6+FcuDK8eIjqxPmJWc9f31acDG/nrT/TzfYNrVVLhi84BxEulHst8C/u/tp5vukbgU+7+zvD+58EcPd7i845CrzT3QcsuNoL7t5pZrcB+939rvC8+4FD7v6Xs72fAoTI/Lg7yXQu3C1OeYt6GpvMBLWmigLHS2fHZ52l9l8+0MPbr91UlfdezDTXvwfOEExzvVh03AB3987yTwOCPSP6i+4PAK8vOecp4BaCYah3Ax1mtmGW526eo60iMg9mU3terG9rKiS5x1JZJtPZejdvVWlrjvHqLWt59Za1hWOpTI4Xz44V1aAa4fmhMSYzuWmVcWvpkgHC3f898O/N7L+5+83zfO1y/Z/ScPhx4HNmdgdwGDgBZCp8LmZ2J8FGRmzbtm2ezRORYlNJ7mAF8lgq2OcimdbivHpoikW4ZlMH12yaSgFnc447XNaZWJI2VJQyX0BwgOBbf/EO6luAkyWvexJ4D4CZtQO3uPsFMxsA9pc891CZdj0APADBENMC2igiZcSiEda0RFjTEuxzMZnJkUwHezUoYNRPUAqkbckW41W65eh7gP8EdBN8u69kiOkIsMvMdhD0DG4F3lfyuhuBc+6eAz5JMKMJgnLi/9HM8pW4fjF8XKSqDj07yP2H++gfHmfrulbu2reT/bu1WWIxMyMRj5KIB9uv5nMXY6mg9Ecmp9zFSlXppNvPAP/G3X9S6Qu7e8bM7ib4sI8CX3L3o2Z2AOh194MEvYR7zcwJhpg+FD73nJn9LkGQATjg7ucqfW+RShx6dpB7Dh4lHjXWtsQZHElyz8GjHAAFiUsozl3QPrWaW9Noa+vxvnM8eKSfodEk29a3LcmXmQUX62s0msVUHavpG/VtD/yAwZHktG0rx1MZujsS/OWdb6hjy5avZLiD3ph6FlX1eN857vv2MWIRozMRI5nJkc46B266btH/Pi81i6nStd69ZvZ1M7vNzN6T/7OoVknDyX+jHhxJTvtGfejZwXo3rSb6h8dpCYdN8lriUQaGVcl+oRLxKBvam9m2oZXL17TQnogRWUHF6+rlwSP9xCJGSzxaKN4Yjxr3H+6r6ftWOsTUSbD/wy8WHXPgv1a9RVI39x/uIx61wjfq1qYY46kM9x/uW5G9iK3rWmf0ICbSWbasa73Es6RShb0t2p2xVJbRZIaJdFaL8xbg1MUJOhPTP66X4stMpbOYfr2mrZAlV24oqX94nLUt8WnnreRv1Hft28k9B48ynsrQEo8ykc6Szjp37dtZ76atKGZGe3OM9uYY2ZwzGtaI0lqLyl3e2cLZsclpPd6l+DJT0RCTmW0xs2+Z2aCZnTazvzKzLTVtmdTMbENJHc0xJkr+0a7kb9T7d3dz4Kbr6O5IcGEiTXdHoipjujK7aMRY0xJn89oWtq5vZX1bE/Gotvicy603bCWT80IPbDyVWZIvM5UOMf0Z8BfAL4f3bw+PvaMWjZLamm0oyd1JZ31VfaPev7tbAaFOiqvPTmayjE9mVX12Fnt3rufD7OLBI/2cGU2ydYlmMVUaILrc/c+K7n/ZzP5dLRoktTfbUNKFiTS/e/OruP9wHwPD42xZ4bOYpHE0x6I0x6KsawtKlY+ntK9Fqb0717N35/qq7wdxKZUGiDNmdjuQL5Z3G3C2Nk2SWrtUclbfqKXe8qXK17YG9aHGJ7OMppSzqIdKB/9+A/gV4BXgFPBeQInrZequfTsLQ0lLOZ4pMl/xaIQ1rUHOYtv6Vja0NwcL9GRJVNqD+F3gA/n9GML9Gj5LEDhkmdm/u5sDoKEkWVaK60Nlc8EXm2A/bk2drZVKA8T1xZv1hKUwXlOjNskS0FCSLGfRiNGRiNORiE/b12IinSWV0UZI1VJpgIiY2bqSHkRjbZ4qIqvStNpQQC7nJDNBqfJ8wJCFqfRD/g+BfzazhwhWUP8K8Hs1a5WIyAJFIsEU7vwkjGzOCwUFJ1KqETUfla6k/qqZ9QJvIyj1/Z7ivaVFRBpVNGK0Ncdoaw4+7vJrLsa0zeqcKh4mCgOCgoKILGvFay7y02jHUhmSmkY7g/IIIrJqBdNog6m02ZwXNkFSUcGAAoSICMFQVGciTmciTi7njKezjE8GU2lX6xarNa2SZWY3mtlPzey4mX2izOPbzOwxM3vSzH5kZu8Kj8fN7Ctm9rSZ/cTMtN2oiCyZSCSoQNvdmeDKDa1ctiZBRyJOLLK6CgvWrAdhZlHg8wQF/QaAI2Z2sCS5/SngG+7+J2a2B3gY2E5QFLDZ3X/WzFqBZ8zsL939xVq1V0SknPwGPflZUfld88ZT2RVfWLCWQ0x7gePu3gdgZg8CNzM90e0EmxEBrAFOFh1vM7MY0AKkgIs1bKuISEUS8Wiwcx7BjKixyZW7H3ctA8RmoL/o/gDw+pJzPg38g5n9NtAG/EJ4/CGCYHIKaAU+4u7nSt/AzO4E7gTYtm1bNdsuIjKn/Iyo9UVVaMdS2RVTWLCWA2rl6tGWZnpuA77s7luAdwFfM7MIQe8jC1wB7AA+ZmYzKsm5+wPu3uPuPV1dXdVtvYjIPDTFgv0tCoUF25pJxJd3YcFa9iAGgK1F97cwNYSU90HgRgB3/76ZJYCNwPuAv3f3NDBoZt8DeoDa7tAtIlIFsZLps8u1sGAtexBHgF1mtsPMmoBbgYMl57wMvB3AzK4FEsBQePxtFmgD3gA8W8O2iojURL6w4KbOBNs3tLKpM0F7IkbElmbTn8WoWQ/C3TNmdjfwCBAFvuTuR83sANDr7geBjwFfMLOPEAw/3eHubmafJ9jS9McEQ1V/5u4/qlVbRUSWgtlU2Q9vD6rQjk5mGrZGlC2n7s6l9PT0eG9vb72bISKyIJVOn632lqNm9oS795R7TCupRUQaQPH02Xz12dFkpq49CwUIEZEGkw8W69uaSKazjCQzjKcyZHNLO+KjACEi0sDywcK9ifFUliqOLs1JAUJEZBnIJ7iX0uqqPCUiIhVTgBARkbIUIEREpCwFCBERKUsBQkREylKAEBGRshQgRESkLAUIEREpSwFCRETKUoAQEZGyFCBERKQsBQgRESmrpgHCzG40s5+a2XEz+0SZx7eZ2WNm9qSZ/cjM3lX02PVm9n0zO2pmT4f7VYuIyBKpWWlAM4sCnwfeAQwAR8zsoLs/U3Tap4BvuPufmNke4GFgu5nFgD8H3u/uT5nZBiBdq7aKiMhMtexB7AWOu3ufu6eAB4GbS85xoDO8vQY4Gd7+ReBH7v4UgLufdfdsDdsqIiIlahkgNgP9RfcHwmPFPg3cbmYDBL2H3w6PXwO4mT1iZv9qZv97uTcwszvNrNfMeoeGhqrbehGRVa6WAaLcvkel++XdBnzZ3bcA7wK+ZmYRgqGvtwC/Fv58t5m9fcaLuT/g7j3u3tPV1VXd1ouIrHK1DBADwNai+1uYGkLK+yDwDQB3/z6QADaGz/2Ou59x93GC3sVra9hWEREpUcsAcQTYZWY7zKwJuBU4WHLOy8DbAczsWoIAMQQ8AlxvZq1hwvqtwDOIiMiSqdksJnfPmNndBB/2UeBL7n7UzA4Ave5+EPgY8AUz+wjB8NMd7u7AsJn9EUGQceBhd///atVWERGZyYLP4+Wvp6fHe3t7690MEZFlxcyecPeeco9pJbWIiJSlACEiImUpQIiISFkKECIiUpYChIiIlKUAISIiZSlAiIhIWQoQIiJSlgKEiIiUpQAhIiJlKUCIiEhZNSvWJ6vHoWcHuf9wH/3D42xd18pd+3ayf3d3vZslIoukHoQsyqFnB7nn4FEGR5KsbYkzOJLknoNHOfTsYL2bJiKLpAAhi3L/4T7iUaO1KYZZ8DMeNe4/3FfvponIIilAyKL0D4/TEo9OO9YSjzIwPF6nFolItShAyKJsXdfKRDo77dhEOsuWda11apGIVEtNA4SZ3WhmPzWz42b2iTKPbzOzx8zsSTP7kZm9q8zjo2b28Vq2Uxburn07SWed8VQG9+BnOuvctW9nvZsmIotUswBhZlHg88AvAXuA28xsT8lpnwK+4e6vIdiz+j+XPP7HwN/Vqo2yePt3d3Pgpuvo7khwYSJNd0eCAzddp1lMIitALae57gWOu3sfgJk9CNwMPFN0jgOd4e01wMn8A2b2b4E+YKyGbZQq2L+7WwFBZAWq5RDTZqC/6P5AeKzYp4HbzWwAeBj4bQAzawN+B/i/LvUGZnanmfWaWe/Q0FC12i0iItQ2QFiZY15y/zbgy+6+BXgX8DUzixAEhj9299FLvYG7P+DuPe7e09XVVZVGi4hIoJZDTAPA1qL7WygaQgp9ELgRwN2/b2YJYCPweuC9ZvYZYC2QM7Oku3+uhu0VEZEitQwQR4BdZrYDOEGQhH5fyTkvA28Hvmxm1wIJYMjdfz5/gpl9GhhVcBARWVo1G2Jy9wxwN/AI8BOC2UpHzeyAmd0UnvYx4DfN7CngL4E73L10GEpEROrAVsrncU9Pj/f29ta7GSIiy4qZPeHuPeUe00pqEREpSwFCRETKUoAQEZGyFCBERKQsBQgRESlLAUJERMpSgBARkbIUIEREpCwFCBERKUsBQkREylKAEBGRshQgRESkLAUIEREpSwFCRETKUoAQEZGyFCBERKQsBQgRESlLAUJERMpaMVuOmtkQ8NIlTtkInFmi5tSSrqPxrJRr0XU0nqW4livdvavcAysmQMzFzHpn23d1OdF1NJ6Vci26jsZT72vREJOIiJSlACEiImWtpgDxQL0bUCW6jsazUq5F19F46notqyYHISIi87OaehAiIjIPChAiIlLWigoQZnajmf3UzI6b2SfKPN5sZl8PH/8XM9u+9K2sTAXXss/M/tXMMmb23nq0sRIVXMdHzewZM/uRmT1qZlfWo51zqeA6/hcze9rMfmhm3zWzPfVoZyXmupai895rZm5mDTlltILfyR1mNhT+Tn5oZv9zPdo5l0p+H2b2K+G/k6Nm9hdL1jh3XxF/gCjwPLATaAKeAvaUnPNbwJ+Gt28Fvl7vdi/iWrYD1wNfBd5b7zYv4jr+B6A1vP2/NuLvpMLr6Cy6fRPw9/Vu90KvJTyvAzgM/ADoqXe7F/g7uQP4XL3bWoXr2AU8CawL73cvVftWUg9iL3Dc3fvcPQU8CNxccs7NwFfC2w8BbzczW8I2VmrOa3H3F939R0CuHg2sUCXX8Zi7j4d3fwBsWeI2VqKS67hYdLcNaNTZH5X8OwH4XeAzQHIpGzcPlV5Ho6vkOn4T+Ly7DwO4++BSNW4lBYjNQH/R/YHwWNlz3D0DXAA2LEnr5qeSa1kO5nsdHwT+rqYtWpiKrsPMPmRmzxN8sP5vS9S2+ZrzWszsNcBWd//bpWzYPFX6d+uWcPjyITPbujRNm5dKruMa4Boz+56Z/cDMblyqxq2kAFGuJ1D6La6ScxrBcmnnXCq+DjO7HegB/qCmLVqYiq7D3T/v7lcBvwN8quatWphLXouZRYA/Bj62ZC1amEp+J38DbHf364F/Ymr0oJFUch0xgmGm/cBtwBfNbG2N2wWsrAAxABR/Q9gCnJztHDOLAWuAc0vSuvmp5FqWg4quw8x+AfgPwE3uPrlEbZuP+f4+HgT+bU1btHBzXUsH8CrgkJm9CLwBONiAieo5fyfufrbo79MXgNctUdvmo9LPrf/m7ml3fwH4KUHAqL16J2mqmOyJAX3ADqaSPdeVnPMhpiepv1Hvdi/0WorO/TKNm6Su5HfyGoIk3a56t3eR17Gr6Pa/AXrr3e7F/t0Kzz9EYyapK/mdXF50+93AD+rd7gVex43AV8LbGwmGpDYsSfvq/T+oyv+z3wU8F37g/Ifw2AGCb6YACeCbwHHgcWBnvdu8iGu5geCbxRhwFjha7zYv8Dr+CTgN/DD8c7DebV7gddwHHA2v4bFLfejW+89c11JybkMGiAp/J/eGv5Onwt/J7nq3eYHXYcAfAc8ATwO3LlXbVGpDRETKWkk5CBERqSIFCBERKUsBQkREylKAEBGRshQgRESkLAUIEREpSwFCpEbMLHqp+5d4Xqw2LRKZHwUIkQUys9vN7PFwr4H7zSxqZqNmdsDM/gV4o5m9aGb3mNl3gV82s58LC679yMy+ZWbrwtc6ZGb/0cy+A3y4rhcmElKAEFkAM7sW+FXgze7+c0AW+DWCUt8/dvfXu/t3w9OT7v4Wd3+QYP+O3/GggNzTwP9Z9LJr3f2t7v6HS3clIrNTV1ZkYd5OUPztSLilSAswSBAo/qrk3K8DmNkagiDwnfD4VwhKv0w7T6RRKECILIwRFFD75LSDZh9392zJuWMVvmal54ksCQ0xiSzMo8B7zawbwMzWz7WftrtfAIbN7OfDQ+8HvnOJp4jUlXoQIgvg7s+Y2aeAfwg32UkTlJOfyweAPzWzVoIyz79ew2aKLIqquYqISFkaYhIRkbIUIEREpCwFCBERKUsBQkREylKAEBGRshQgRESkLAUIEREp6/8HcjiA3MwRoyYAAAAASUVORK5CYII=\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.regplot(x=\"error\", y=\"confidence\", data=df_25);"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\nstory_id 1.000000 -0.131402 0.099005\nerror -0.131402 1.000000 -0.358734\nconfidence 0.099005 -0.358734 1.000000",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>story_id</th>\n <td>1.000000</td>\n <td>-0.131402</td>\n <td>0.099005</td>\n </tr>\n <tr>\n <th>error</th>\n <td>-0.131402</td>\n <td>1.000000</td>\n <td>-0.358734</td>\n </tr>\n <tr>\n <th>confidence</th>\n <td>0.099005</td>\n <td>-0.358734</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 175
}
],
"source": [
"df_25.corr()"
]
},
{
"source": [
"### Confidence by character for the entire submission"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [],
"source": [
"# Function to get submission confidence by character per page\n",
"def character_submission_confidence(image_path):\n",
" '''\n",
" Detects document features in images and returns overall confidence level of transcribed text\n",
" Input: Path to file where images are stored\n",
" - Assuming 1 image per image_path - run this on each individual image in one submission\n",
" - Code for both local image_path and remote image_path, comment out\n",
" the apporopriate one\n",
" Output: Float. Decimal format of confidence level of the transcription for the page\n",
" '''\n",
"\n",
" from google.cloud import vision\n",
" import io\n",
" from google.oauth2 import service_account\n",
"\n",
" # If image_path is local\n",
" with io.open(image_path, 'rb') as image_file:\n",
" content = image_file.read()\n",
" image = vision.types.Image(content=content)\n",
"\n",
" # # If image_path is a uri\n",
" # image = vision.types.Image()\n",
" # image.source.image_uri = uri\n",
"\n",
" # Set language to english only\n",
" language = vision.types.ImageContext(language_hints=['en-t-i0-handwrit'])\n",
"\n",
" # Connect to Google API client\n",
" creds = service_account.Credentials.from_service_account_file('/Users/stevenchase/Desktop/Steven/Computer_Science/Lambda/labs/story_sqaud/Story Squad-6122da7459cf.json')\n",
" client = vision.ImageAnnotatorClient(credentials=creds)\n",
" response = client.document_text_detection(image=image, image_context=language)\n",
"\n",
" # List of confidence levels of each word\n",
" symbol_confidences = []\n",
"\n",
" for page in response.full_text_annotation.pages:\n",
" for block in page.blocks:\n",
" for paragraph in block.paragraphs:\n",
" for word in paragraph.words:\n",
" # word_confidences.append(word.confidence)\n",
" for symbol in word.symbols:\n",
" symbol_confidences.append(symbol.confidence)\n",
" # If there is no text on the page\n",
" if len(symbol_confidences) == 0:\n",
" return None\n",
" else:\n",
" # Return list of all of the confidences for each character\n",
" return symbol_confidences"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [],
"source": [
"def character_multi_page_submission(directory):\n",
" '''\n",
" Function calls page_confidence function for each page in a single submission\n",
" Input: file path containing images of user's written stories\n",
" Output: Float; Confidence in the transcription of the entire submission\n",
" '''\n",
" \n",
" paths = []\n",
"\n",
" for item in os.listdir(directory):\n",
" if item[-3:] == 'jpg':\n",
" path = os.path.join(directory, item)\n",
" paths.append(path)\n",
"\n",
" submission_confidence = []\n",
"\n",
" for image_path in paths:\n",
" submission_confidence.extend(character_submission_confidence(image_path))\n",
" \n",
" # Drop any 'None' responses from confidence function\n",
" submission_confidence = [x for x in submission_confidence if x is not None]\n",
"\n",
" # Check to make sure that submission_confidence isn't empty\n",
" if len(submission_confidence) == 0:\n",
" return 0\n",
" else:\n",
" confidence_level = sum(submission_confidence) / len(submission_confidence)\n",
" return confidence_level"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n\n api transcription error confidence \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 0.0 \n1 D she was very, a berenang The pony that didn'... 0.364450 0.0 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 0.0 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 0.0 \n4 The secret fifth grade E am Anella I am starti... 0.136770 0.0 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 181
}
],
"source": [
"# Create new df\n",
"df_25_character_submission = df_25.copy()\n",
"df_25_character_submission['confidence'] = 0.0\n",
"df_25_character_submission.head()"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id human transcription \\\n0 3132 Page 1 Once there was a little cheatah and the... \n1 3104 The pony that didn't know. Once there was a po... \n2 3103 Rainbow the Unica Once there was a unicorn nam... \n3 3117 gumdrop land 1 gumdrop land is a place were gu... \n4 3102 The secret fifth grade I am Amelia I am starti... \n\n api transcription error confidence \n0 Page. I 3132 Once there was a little cheatah a... 0.055957 0.959946 \n1 D she was very, a berenang The pony that didn'... 0.364450 0.894102 \n2 Rainbow the Unica unicom named some een Pearl ... 0.383740 0.946982 \n3 O gum drop land gumdrop. land is prace Were gu... 0.270862 0.895751 \n4 The secret fifth grade E am Anella I am starti... 0.136770 0.916551 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>human transcription</th>\n <th>api transcription</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>3132</td>\n <td>Page 1 Once there was a little cheatah and the...</td>\n <td>Page. I 3132 Once there was a little cheatah a...</td>\n <td>0.055957</td>\n <td>0.959946</td>\n </tr>\n <tr>\n <th>1</th>\n <td>3104</td>\n <td>The pony that didn't know. Once there was a po...</td>\n <td>D she was very, a berenang The pony that didn'...</td>\n <td>0.364450</td>\n <td>0.894102</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3103</td>\n <td>Rainbow the Unica Once there was a unicorn nam...</td>\n <td>Rainbow the Unica unicom named some een Pearl ...</td>\n <td>0.383740</td>\n <td>0.946982</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3117</td>\n <td>gumdrop land 1 gumdrop land is a place were gu...</td>\n <td>O gum drop land gumdrop. land is prace Were gu...</td>\n <td>0.270862</td>\n <td>0.895751</td>\n </tr>\n <tr>\n <th>4</th>\n <td>3102</td>\n <td>The secret fifth grade I am Amelia I am starti...</td>\n <td>The secret fifth grade E am Anella I am starti...</td>\n <td>0.136770</td>\n <td>0.916551</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 183
}
],
"source": [
"# Calculate the confidence for each transcription\n",
"for i in range(df_25_character_submission.shape[0]):\n",
" # Build the directory path based off of the story_id\n",
" directory = os.path.join(root_directory, str(df_25_character_submission['story_id'][i])[:2] + '--', str(df_25_character_submission['story_id'][i]))\n",
"\n",
" # Populate confidence column with call of multi_page function on each row\n",
" df_25_character_submission.loc[i, 'confidence'] = character_multi_page_submission(directory)\n",
"\n",
"df_25_character_submission.head()"
]
},
{
"source": [
"#### Confidence by character for entire submission at once"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.regplot(x=\"error\", y=\"confidence\", data=df_25_character_submission[:25]);"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " story_id error confidence\nstory_id 1.000000 -0.131402 0.151808\nerror -0.131402 1.000000 -0.394979\nconfidence 0.151808 -0.394979 1.000000",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>story_id</th>\n <th>error</th>\n <th>confidence</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>story_id</th>\n <td>1.000000</td>\n <td>-0.131402</td>\n <td>0.151808</td>\n </tr>\n <tr>\n <th>error</th>\n <td>-0.131402</td>\n <td>1.000000</td>\n <td>-0.394979</td>\n </tr>\n <tr>\n <th>confidence</th>\n <td>0.151808</td>\n <td>-0.394979</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 185
}
],
"source": [
"df_25_character_submission.corr()"
]
}
]
}