[2]:
+[1]:
from lomas_client.client import Client
@@ -215,7 +215,7 @@ Step 2: Initialise the client
-[3]:
+[2]:
APP_URL = "http://lomas_server"
@@ -231,7 +231,7 @@ Step 3: Understand the functionnalities of the library
Getting dataset metadata
-[4]:
+[3]:
titanic_metadata = client.get_dataset_metadata()
@@ -240,7 +240,7 @@ Getting dataset metadata
-[4]:
+[3]:
@@ -340,7 +340,7 @@ Getting dataset metadata
Get a dummy dataset
-[5]:
+[4]:
NB_ROWS = 200
@@ -349,7 +349,7 @@ Get a dummy dataset
-[6]:
+[5]:
df_dummy = client.get_dummy_dataset(
@@ -371,7 +371,7 @@ Get a dummy dataset
-[6]:
+[5]:
@@ -487,7 +487,7 @@ Query on dummy dataset
Average and number of rows with smartnoise-sql library on remote dummy
-[7]:
+[6]:
# Average Age
@@ -498,7 +498,7 @@ Average and number of rows with smartnoise-sql library on remote dummy
-[8]:
+[7]:
# On the remote server dummy dataframe
@@ -514,7 +514,7 @@ Average and number of rows with smartnoise-sql library on remote dummy
-[9]:
+[8]:
print(f"Average age in remote dummy: {np.round(dummy_res.result.df['avg_age'][0], 2)} years old.")
@@ -536,7 +536,7 @@ Average and number of rows with smartnoise-sql library on remote dummy
Get current budget
-[10]:
+[9]:
client.get_initial_budget()
@@ -544,16 +544,16 @@ Get current budget
-[10]:
+[9]:
-{'initial_epsilon': 45.0, 'initial_delta': 0.2}
+InitialBudgetResponse(initial_epsilon=45.0, initial_delta=0.2)
-[11]:
+[10]:
client.get_total_spent_budget()
@@ -561,17 +561,17 @@ Get current budget
-[11]:
+[10]:
-{'total_spent_epsilon': 0.0, 'total_spent_delta': 0.0}
+SpentBudgetResponse(total_spent_epsilon=0.0, total_spent_delta=0.0)
It will also be useful to know what the remaining budget is. Therefore, we call the function get_remaining_budget. It just substarcts the total spent budget from the initial budget.
-[12]:
+[11]:
client.get_remaining_budget()
@@ -579,12 +579,12 @@ Get current budget
-[12]:
+[11]:
-{'remaining_epsilon': 45.0, 'remaining_delta': 0.2}
+RemainingBudgetResponse(remaining_epsilon=45.0, remaining_delta=0.2)
As expected, for now the remaining budget is equal to the inital budget.
@@ -594,7 +594,7 @@ Estimate cost of a queryestimate cost function returns the estimated real cost of any query.
Again, of course, this will not impact the user’s budget.
-[13]:
+[12]:
EPSILON = 0.5
@@ -602,8 +602,8 @@ Estimate cost of a query
-[ ]:
+
+[13]:
client.smartnoise_sql.cost(
@@ -614,19 +614,37 @@ Estimate cost of a query
+[13]:
+
+
+
+
+CostResponse(epsilon=1.5, delta=0.00014999500000001387)
+
+
Query on real private dataset with smartnoise-sql.
-
-[ ]:
+
+[14]:
client.get_remaining_budget()
+
+[14]:
+
+
+
+
+RemainingBudgetResponse(remaining_epsilon=45.0, remaining_delta=0.2)
+
+
-[ ]:
+[15]:
-
-[ ]:
+
+[16]:
+
+
+
+
+
+Number of passengers in real data: 887.
+Average age in real data: 29.34.
+
+
After each query on the real dataset, the budget informations are also returned to the researcher. It is possible possible to check the remaining budget again afterwards:
-
-[ ]:
+
+[17]:
client.get_remaining_budget()
+
+[17]:
+
+
+
+
+RemainingBudgetResponse(remaining_epsilon=43.5, remaining_delta=0.199850005)
+
+
As can be seen in get_total_spent_budget(), it is the budget estimated with estimate_smartnoise_sql_cost() that was spent.
-
-[ ]:
+
+[18]:
client.get_total_spent_budget()
+
+[18]:
+
+
+
+
+SpentBudgetResponse(total_spent_epsilon=1.5, total_spent_delta=0.00014999500000001387)
+
+
Step 4: Titanic statistics with opendp
-[ ]:
+[19]:
import opendp as dp
@@ -692,24 +737,121 @@ Step 4: Titanic statistics with opendp
Confidence intervals for age over the whole population
-
-[ ]:
+
+[20]:
titanic_metadata
+
+[20]:
+
+
+
+
+{'max_ids': 1,
+ 'rows': 887,
+ 'row_privacy': True,
+ 'censor_dims': False,
+ 'columns': {'Pclass': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'int',
+ 'precision': 32,
+ 'lower': 1,
+ 'upper': 3},
+ 'Name': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'string'},
+ 'Sex': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'string',
+ 'cardinality': 2,
+ 'categories': ['male', 'female']},
+ 'Age': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'float',
+ 'precision': 64,
+ 'lower': 0.1,
+ 'upper': 100.0},
+ 'SibSp': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'int',
+ 'precision': 32,
+ 'lower': 0,
+ 'upper': 10},
+ 'Parch': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'int',
+ 'precision': 32,
+ 'lower': 0,
+ 'upper': 10},
+ 'Ticket': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'string'},
+ 'Fare': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'float',
+ 'precision': 64,
+ 'lower': 0.0,
+ 'upper': 1000.0},
+ 'Cabin': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'string'},
+ 'Embarked': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'string',
+ 'cardinality': 3,
+ 'categories': ['C', 'Q', 'S']},
+ 'Survived': {'private_id': False,
+ 'nullable': False,
+ 'max_partition_length': None,
+ 'max_influenced_partitions': None,
+ 'max_partition_contributions': None,
+ 'type': 'boolean'}}}
+
+
-[ ]:
+[21]:
columns = ["PassengerId", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch"]
-
-[ ]:
+
+[22]:
+
+[22]:
+
+
+
+
+(0.1, 100.0)
+
+
-[ ]:
+[23]:
-
-[ ]:
+
+[24]:
+
+
+
+
+
+---------------------------------------------------------------------------
+InvalidQueryException Traceback (most recent call last)
+Cell In[24], line 2
+ 1 # Expect to fail !!!
+----> 2 client.opendp.query(
+ 3 opendp_pipeline = age_transformation_pipeline,
+ 4 dummy=True
+ 5 )
+
+File /code/lomas_client/libraries/opendp.py:105, in OpenDPClient.query(self, opendp_pipeline, fixed_delta, dummy, nb_rows, seed)
+ 102 body = request_model.model_validate(body_dict)
+ 103 res = self.http_client.post(endpoint, body)
+--> 105 return validate_model_response(res, QueryResponse)
+
+File /code/lomas_client/utils.py:83, in validate_model_response(response, response_model)
+ 80 r_model = response_model.model_validate_json(data)
+ 81 return r_model
+---> 83 raise_error(response)
+ 84 return None
+
+File /code/lomas_client/utils.py:26, in raise_error(response)
+ 24 error_message = response.json()
+ 25 if response.status_code == status.HTTP_400_BAD_REQUEST:
+---> 26 raise InvalidQueryException(error_message["InvalidQueryException"])
+ 27 if response.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY:
+ 28 raise ExternalLibraryException(
+ 29 error_message["library"], error_message["ExternalLibraryException"]
+ 30 )
+
+InvalidQueryException: The pipeline provided is not a measurement. It cannot be processed in this server.
+
+
This is because the server will only allow measurement pipeline with differentially private results. We add Laplacian noise to the pipeline and should be able to instantiate the pipeline.
-[ ]:
+[26]:
var_age_transformation_pipeline = (
@@ -758,8 +946,8 @@ Confidence intervals for age over the whole population
Now that there is a measurement, one is able to apply the pipeline on the dummy dataset of the server.
-
-[ ]:
+
+[27]:
+
+
+
+
+
+Dummy result for variance: 59.75
+
+
With opendp, the function estimate_opendp_cost is particularly useful to estimate the used epsilon and delta based on the scale value.
-
-[ ]:
+
+[28]:
+
+[28]:
+
+
+
+
+CostResponse(epsilon=2.2502841037292076, delta=0.0)
+
+
One can now execute the query on the real dataset.
-[ ]:
+[29]:
-
-[ ]:
+
+[30]:
-
-[ ]:
+
+
+
+
+
+Number of passengers: 887 (from previous smartnoise-sql query).
+Average age: 29.34 (from previous smartnoise-sql query).
+Variance of age: 182.132 (from opendp query).
+
+
+
+[31]:
-
-[ ]:
+
+
+
+
+
+Standard error of age: 0.45.
+
+
+
+[32]:
-
-[ ]:
-
-
-
-
+
+
+
+
+The 95% confidence interval of the age of all passengers is [28.45, 30.23].
+
[ ]:
diff --git a/html/develop/en/notebooks/s3_example_notebook.ipynb b/html/develop/en/notebooks/s3_example_notebook.ipynb
index 9739b53e..482193e8 100644
--- a/html/develop/en/notebooks/s3_example_notebook.ipynb
+++ b/html/develop/en/notebooks/s3_example_notebook.ipynb
@@ -94,7 +94,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"id": "6fb569fc",
"metadata": {},
"outputs": [],
@@ -120,7 +120,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"id": "941991f7",
"metadata": {},
"outputs": [],
@@ -149,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"id": "d15cbe39",
"metadata": {},
"outputs": [
@@ -247,7 +247,7 @@
" 'type': 'boolean'}}}"
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -267,7 +267,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"id": "01f4365a",
"metadata": {},
"outputs": [],
@@ -278,7 +278,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"id": "3f553b29",
"metadata": {},
"outputs": [
@@ -414,7 +414,7 @@
"4 S True "
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -447,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"id": "3946425d",
"metadata": {},
"outputs": [],
@@ -460,7 +460,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"id": "90cf2a6d",
"metadata": {},
"outputs": [],
@@ -478,7 +478,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"id": "a30f277e",
"metadata": {},
"outputs": [
@@ -506,17 +506,17 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "61a467f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'initial_epsilon': 45.0, 'initial_delta': 0.2}"
+ "InitialBudgetResponse(initial_epsilon=45.0, initial_delta=0.2)"
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -527,17 +527,17 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"id": "afd22f84",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'total_spent_epsilon': 0.0, 'total_spent_delta': 0.0}"
+ "SpentBudgetResponse(total_spent_epsilon=0.0, total_spent_delta=0.0)"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -556,17 +556,17 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"id": "6260cf54",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'remaining_epsilon': 45.0, 'remaining_delta': 0.2}"
+ "RemainingBudgetResponse(remaining_epsilon=45.0, remaining_delta=0.2)"
]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -596,7 +596,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"id": "fd5ed08a",
"metadata": {},
"outputs": [],
@@ -607,10 +607,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "133020c6",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "CostResponse(epsilon=1.5, delta=0.00014999500000001387)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"client.smartnoise_sql.cost(\n",
" query = QUERY, \n",
@@ -629,17 +640,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "19e60263",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RemainingBudgetResponse(remaining_epsilon=45.0, remaining_delta=0.2)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"client.get_remaining_budget()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "69767fac",
"metadata": {},
"outputs": [],
@@ -662,10 +684,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "6dbbdf93",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of passengers in real data: 887.\n",
+ "Average age in real data: 29.34.\n"
+ ]
+ }
+ ],
"source": [
"nb_passengers = response.result.df['nb_passengers'].iloc[0]\n",
"print(f\"Number of passengers in real data: {nb_passengers}.\")\n",
@@ -684,10 +715,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "39701fe5",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RemainingBudgetResponse(remaining_epsilon=43.5, remaining_delta=0.199850005)"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"client.get_remaining_budget()"
]
@@ -702,10 +744,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "487f835f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SpentBudgetResponse(total_spent_epsilon=1.5, total_spent_delta=0.00014999500000001387)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"client.get_total_spent_budget()"
]
@@ -720,7 +773,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "b9685226",
"metadata": {},
"outputs": [],
@@ -740,17 +793,116 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "4331d86f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'max_ids': 1,\n",
+ " 'rows': 887,\n",
+ " 'row_privacy': True,\n",
+ " 'censor_dims': False,\n",
+ " 'columns': {'Pclass': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'int',\n",
+ " 'precision': 32,\n",
+ " 'lower': 1,\n",
+ " 'upper': 3},\n",
+ " 'Name': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'string'},\n",
+ " 'Sex': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'string',\n",
+ " 'cardinality': 2,\n",
+ " 'categories': ['male', 'female']},\n",
+ " 'Age': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'float',\n",
+ " 'precision': 64,\n",
+ " 'lower': 0.1,\n",
+ " 'upper': 100.0},\n",
+ " 'SibSp': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'int',\n",
+ " 'precision': 32,\n",
+ " 'lower': 0,\n",
+ " 'upper': 10},\n",
+ " 'Parch': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'int',\n",
+ " 'precision': 32,\n",
+ " 'lower': 0,\n",
+ " 'upper': 10},\n",
+ " 'Ticket': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'string'},\n",
+ " 'Fare': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'float',\n",
+ " 'precision': 64,\n",
+ " 'lower': 0.0,\n",
+ " 'upper': 1000.0},\n",
+ " 'Cabin': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'string'},\n",
+ " 'Embarked': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'string',\n",
+ " 'cardinality': 3,\n",
+ " 'categories': ['C', 'Q', 'S']},\n",
+ " 'Survived': {'private_id': False,\n",
+ " 'nullable': False,\n",
+ " 'max_partition_length': None,\n",
+ " 'max_influenced_partitions': None,\n",
+ " 'max_partition_contributions': None,\n",
+ " 'type': 'boolean'}}}"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"titanic_metadata"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "ff8cb7b6",
"metadata": {},
"outputs": [],
@@ -760,10 +912,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "70b2bdb1",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.1, 100.0)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"age_min = titanic_metadata['columns']['Age']['lower']\n",
"age_max = titanic_metadata['columns']['Age']['upper']\n",
@@ -772,7 +935,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "75e4933b",
"metadata": {},
"outputs": [],
@@ -789,10 +952,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"id": "8041a647",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "InvalidQueryException",
+ "evalue": "The pipeline provided is not a measurement. It cannot be processed in this server.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mInvalidQueryException\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[24], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Expect to fail !!!\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopendp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquery\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mopendp_pipeline\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mage_transformation_pipeline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mdummy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m 5\u001b[0m \u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/code/lomas_client/libraries/opendp.py:105\u001b[0m, in \u001b[0;36mOpenDPClient.query\u001b[0;34m(self, opendp_pipeline, fixed_delta, dummy, nb_rows, seed)\u001b[0m\n\u001b[1;32m 102\u001b[0m body \u001b[38;5;241m=\u001b[39m request_model\u001b[38;5;241m.\u001b[39mmodel_validate(body_dict)\n\u001b[1;32m 103\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhttp_client\u001b[38;5;241m.\u001b[39mpost(endpoint, body)\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvalidate_model_response\u001b[49m\u001b[43m(\u001b[49m\u001b[43mres\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mQueryResponse\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/code/lomas_client/utils.py:83\u001b[0m, in \u001b[0;36mvalidate_model_response\u001b[0;34m(response, response_model)\u001b[0m\n\u001b[1;32m 80\u001b[0m r_model \u001b[38;5;241m=\u001b[39m response_model\u001b[38;5;241m.\u001b[39mmodel_validate_json(data)\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r_model\n\u001b[0;32m---> 83\u001b[0m \u001b[43mraise_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+ "File \u001b[0;32m/code/lomas_client/utils.py:26\u001b[0m, in \u001b[0;36mraise_error\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m 24\u001b[0m error_message \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_400_BAD_REQUEST:\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidQueryException(error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalidQueryException\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m status\u001b[38;5;241m.\u001b[39mHTTP_422_UNPROCESSABLE_ENTITY:\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ExternalLibraryException(\n\u001b[1;32m 29\u001b[0m error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibrary\u001b[39m\u001b[38;5;124m\"\u001b[39m], error_message[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExternalLibraryException\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 30\u001b[0m )\n",
+ "\u001b[0;31mInvalidQueryException\u001b[0m: The pipeline provided is not a measurement. It cannot be processed in this server."
+ ]
+ }
+ ],
"source": [
"# Expect to fail !!!\n",
"client.opendp.query(\n",
@@ -811,7 +989,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"id": "b8162859",
"metadata": {},
"outputs": [],
@@ -832,10 +1010,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"id": "df61bce0",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dummy result for variance: 59.75\n"
+ ]
+ }
+ ],
"source": [
"dummy_var_res = client.opendp.query(\n",
" opendp_pipeline = var_age_transformation_pipeline, \n",
@@ -854,10 +1040,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"id": "7ae7f735",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "CostResponse(epsilon=2.2502841037292076, delta=0.0)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"cost_res = client.opendp.cost(\n",
" opendp_pipeline = var_age_transformation_pipeline\n",
@@ -875,7 +1072,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"id": "085555a5",
"metadata": {},
"outputs": [],
@@ -887,10 +1084,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"id": "674332e7",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of passengers: 887 (from previous smartnoise-sql query).\n",
+ "Average age: 29.34 (from previous smartnoise-sql query).\n",
+ "Variance of age: 182.132 (from opendp query).\n"
+ ]
+ }
+ ],
"source": [
"print(f\"Number of passengers: {nb_passengers} (from previous smartnoise-sql query).\")\n",
"\n",
@@ -902,10 +1109,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"id": "f72b19d0",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Standard error of age: 0.45.\n"
+ ]
+ }
+ ],
"source": [
"# Get standard error\n",
"standard_error = np.sqrt(var_age/nb_passengers)\n",
@@ -914,10 +1129,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 32,
"id": "62630a03",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The 95% confidence interval of the age of all passengers is [28.45, 30.23].\n"
+ ]
+ }
+ ],
"source": [
" # Compute the 95% confidence interval\n",
"ZSCORE = 1.96\n",
@@ -933,14 +1156,6 @@
"metadata": {},
"outputs": [],
"source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3850a191-7e1b-4e44-a1dc-0621171360c9",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/html/develop/en/searchindex.js b/html/develop/en/searchindex.js
index 818bac2c..ebc193b0 100644
--- a/html/develop/en/searchindex.js
+++ b/html/develop/en/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"AIM: Adaptive Iterative Mechanism": [[26, "AIM:-Adaptive-Iterative-Mechanism"]], "API Documentation": [[3, null]], "Access the server to administrate the mongoDB": [[27, "Access-the-server-to-administrate-the-mongoDB"]], "Access via Kubernetes": [[33, "access-via-kubernetes"]], "Access via Onyxia Platform": [[33, "access-via-onyxia-platform"]], "Accessing the Helm Chart": [[35, "accessing-the-helm-chart"]], "Add user": [[27, "Add-user"]], "Adding a DP Library": [[0, "adding-a-dp-library"]], "Adding a Data Connector (for private dataset in various databases)": [[0, "adding-a-data-connector-for-private-dataset-in-various-databases"]], "Administration": [[30, null]], "Archives of queries": [[27, "Archives-of-queries"]], "Average and number of rows with smartnoise-sql library on remote dummy": [[28, "Average-and-number-of-rows-with-smartnoise-sql-library-on-remote-dummy"]], "CLI": [[32, null]], "Change budget": [[27, "Change-budget"]], "Classification: Gaussian Naive Bayes": [[24, "Classification:-Gaussian-Naive-Bayes"]], "Classification: Logistic Regression": [[24, "Classification:-Logistic-Regression"]], "Client": [[8, "client"], [9, "client"]], "Client API": [[4, null]], "Clustering: K-Means": [[24, "Clustering:-K-Means"]], "Collections": [[32, "collections"]], "Confidence intervals for age over the whole population": [[28, "Confidence-intervals-for-age-over-the-whole-population"]], "Confidence intervals for flipper length over the whole population": [[25, "Confidence-intervals-for-flipper-length-over-the-whole-population"]], "Create a docker volume": [[27, "Create-a-docker-volume"]], "DPCTGAN: Differentially Private Conditional Tabular GAN": [[26, "DPCTGAN:-Differentially-Private-Conditional-Tabular-GAN"]], "DPGAN: DIfferentially Private GAN": [[26, "DPGAN:-DIfferentially-Private-GAN"]], "Dashboard": [[33, null]], "Datasets": [[32, "datasets"]], "Datasets (add and drop)": [[27, "Datasets-(add-and-drop)"]], "Decision Tree Classifier": [[24, "Decision-Tree-Classifier"]], "Deploying Lomas on Onyxia": [[38, "deploying-lomas-on-onyxia"]], "Deploying the Service on Kubernetes": [[35, "deploying-the-service-on-kubernetes"]], "Deployment": [[34, null]], "Detailed description:": [[9, "detailed-description"]], "Dimensionality Reduction: PCA": [[24, "Dimensionality-Reduction:-PCA"]], "Errors": [[5, null]], "Estimate budget of Linear Regression with DiffPrivLib": [[23, "Estimate-budget-of-Linear-Regression-with-DiffPrivLib"]], "Estimate cost of a query": [[25, "Estimate-cost-of-a-query"], [28, "Estimate-cost-of-a-query"]], "Estimate cost of a query with smartnoise-sql": [[23, "Estimate-cost-of-a-query-with-smartnoise-sql"]], "Estimate cost of training patectgan synthesizer with Smartnoise-Synth": [[23, "Estimate-cost-of-training-patectgan-synthesizer-with-Smartnoise-Synth"]], "Estimate cost with opendp": [[23, "Estimate-cost-with-opendp"]], "Examples": [[6, null], [32, "examples"]], "Execute pipeline on real dataset with opendp": [[23, "Execute-pipeline-on-real-dataset-with-opendp"]], "External Loggers": [[0, "external-loggers"]], "ExternalLibraryException": [[5, "externallibraryexception"]], "Finally all can be loaded fom a file direcly": [[27, "Finally-all-can-be-loaded-fom-a-file-direcly"]], "First steps": [[8, "first-steps"]], "Get a dummy dataset": [[28, "Get-a-dummy-dataset"]], "Get current budget": [[28, "Get-current-budget"]], "Getting dataset metadata": [[24, "Getting-dataset-metadata"], [26, "Getting-dataset-metadata"], [28, "Getting-dataset-metadata"]], "Git Branches": [[0, "git-branches"]], "GitHub Workflows": [[0, "github-workflows"]], "History": [[9, "history"]], "Hypothesis testing": [[25, "Hypothesis-testing"]], "Indices and tables": [[9, "indices-and-tables"]], "Installation": [[8, "installation"]], "Installing the Helm Chart": [[35, "installing-the-helm-chart"]], "InternalServerException": [[5, "internalserverexception"]], "Introduction": [[38, "introduction"]], "InvalidQueryException": [[5, "invalidqueryexception"]], "Kubernetes": [[35, null]], "Linting and Other Checks": [[0, "linting-and-other-checks"]], "Local": [[36, null]], "Local Access via Docker": [[33, "local-access-via-docker"]], "Lomas Client Side: Using DiffPrivlib": [[24, null]], "Lomas Client Side: Using Smartnoise-SQL": [[25, null]], "Lomas Client Side: Using Smartnoise-Synth": [[26, null]], "Lomas-server: CLI administration": [[27, null]], "Lomas: Client demo": [[23, null]], "Lomas: The Data Oases Hidden Behind the Mist.": [[9, null]], "MST: Maximum Spanning Tree": [[26, "MST:-Maximum-Spanning-Tree"]], "MWEM: Multiplicative Weights Exponential Mechanism": [[26, "MWEM:-Multiplicative-Weights-Exponential-Mechanism"]], "Modifying the ingress Section": [[35, "modifying-the-ingress-section"]], "Modifying values.yaml": [[35, "modifying-values-yaml"]], "Module contents": [[11, "module-contents"], [12, "module-contents"], [13, "module-lomas_server"], [14, "module-lomas_server.admin_database"], [15, "module-lomas_server.administration"], [16, "module-lomas_server.administration.tests"], [17, "module-lomas_server.data_connector"], [18, "module-lomas_server.dp_queries"], [19, "module-lomas_server.dp_queries.dp_libraries"], [20, "module-lomas_server.routes"], [21, "module-lomas_server.tests"], [22, "module-lomas_server.utils"]], "MongoDB Administration": [[32, "mongodb-administration"]], "MongoDB Connection": [[32, "mongodb-connection"]], "Note on budget with Smartnoise-SQL (Advanced)": [[25, "Note-on-budget-with-Smartnoise-SQL-(Advanced)"]], "Notes for Client Contributors": [[1, null]], "Notes for Contributors": [[0, null]], "Notes for Server Contributors": [[2, null]], "Onyxia": [[38, null]], "Overide DP mechanism": [[25, "Overide-DP-mechanism"]], "Overview": [[32, "overview"]], "PATECTGAN: Conditional tabular GAN using Private Aggregation of Teacher Ensembles": [[26, "PATECTGAN:-Conditional-tabular-GAN-using-Private-Aggregation-of-Teacher-Ensembles"]], "PATEGAN: Private Aggregation of Teacher Ensembles": [[26, "PATEGAN:-Private-Aggregation-of-Teacher-Ensembles"]], "Poster": [[29, null]], "Postprocess": [[25, "Postprocess"]], "Postprocessing: no additional privacy risk with DP": [[23, "Postprocessing:-no-additional-privacy-risk-with-DP"]], "Prepare Random Forest Classifier pipeline on dummy with DiffPrivLib": [[23, "Prepare-Random-Forest-Classifier-pipeline-on-dummy-with-DiffPrivLib"]], "Prepare opendp pipeline and verify on dummy": [[23, "Prepare-opendp-pipeline-and-verify-on-dummy"]], "Prepare the database": [[27, "Prepare-the-database"]], "Prerequisites": [[35, "prerequisites"], [36, "prerequisites"]], "Pushing a new version to Pypi": [[1, "pushing-a-new-version-to-pypi"]], "Query average bill length on dummy dataset with smartnoise-sql": [[23, "Query-average-bill-length-on-dummy-dataset-with-smartnoise-sql"]], "Query average bill length on private dataset with smartnoise-sql": [[23, "Query-average-bill-length-on-private-dataset-with-smartnoise-sql"]], "Query dummy dataset": [[25, "Query-dummy-dataset"]], "Query on dummy dataset": [[28, "Query-on-dummy-dataset"]], "Query on real private dataset with smartnoise-sql.": [[28, "Query-on-real-private-dataset-with-smartnoise-sql."]], "Query real dataset": [[25, "Query-real-dataset"]], "Quickstart": [[8, null]], "Random Forest": [[24, "Random-Forest"]], "Regression: Linear Regression": [[24, "Regression:-Linear-Regression"]], "Release Workflow": [[0, "release-workflow"]], "Remove user": [[27, "Remove-user"]], "S3 example": [[28, null]], "Server": [[9, "server"]], "Server API": [[31, null]], "She can now estimated the cost of this pipeline": [[24, "She-can-now-estimated-the-cost-of-this-pipeline"]], "She starts to write the associated DiffPrivLib pipeline and tries it on the dummy.": [[24, "She-starts-to-write-the-associated-DiffPrivLib-pipeline-and-tries-it-on-the-dummy."]], "Start server": [[27, "Start-server"]], "Start the server": [[27, "Start-the-server"]], "Step 1: Install the library": [[23, "Step-1:-Install-the-library"], [24, "Step-1:-Install-the-library"], [25, "Step-1:-Install-the-library"], [26, "Step-1:-Install-the-library"], [28, "Step-1:-Install-the-library"]], "Step 2: Initialise the client": [[23, "Step-2:-Initialise-the-client"], [24, "Step-2:-Initialise-the-client"], [25, "Step-2:-Initialise-the-client"], [26, "Step-2:-Initialise-the-client"], [28, "Step-2:-Initialise-the-client"]], "Step 3: Create a Synthetic Dataset keeping all default parameters": [[26, "Step-3:-Create-a-Synthetic-Dataset-keeping-all-default-parameters"]], "Step 3: Getting dataset metadata": [[25, "Step-3:-Getting-dataset-metadata"]], "Step 3: Metadata and dummy dataset": [[24, "Step-3:-Metadata-and-dummy-dataset"], [26, "Step-3:-Metadata-and-dummy-dataset"]], "Step 3: Understand the functionnalities of the library": [[23, "Step-3:-Understand-the-functionnalities-of-the-library"], [28, "Step-3:-Understand-the-functionnalities-of-the-library"]], "Step 4: Average bill length with Smartnoise-SQL": [[25, "Step-4:-Average-bill-length-with-Smartnoise-SQL"]], "Step 4: Penguin statistics": [[25, "Step-4:-Penguin-statistics"]], "Step 4: See archives of queries": [[23, "Step-4:-See-archives-of-queries"]], "Step 4: Titanic statistics with opendp": [[28, "Step-4:-Titanic-statistics-with-opendp"]], "Step 4: Train Logistic Regression model with DiffPrivLib": [[24, "Step-4:-Train-Logistic-Regression-model-with-DiffPrivLib"]], "Step 4: Use DP libraries to analyse the dataset": [[23, "Step-4:-Use-DP-libraries-to-analyse-the-dataset"]], "Step 5: Train other models with DiffPrivLib": [[24, "Step-5:-Train-other-models-with-DiffPrivLib"]], "Step 6: See archives of queries": [[24, "Step-6:-See-archives-of-queries"], [26, "Step-6:-See-archives-of-queries"]], "Steps to Deploy Locally": [[36, "steps-to-deploy-locally"]], "Stop the server: do not do it now !": [[27, "Stop-the-server:-do-not-do-it-now-!"]], "Submodules": [[11, "submodules"], [12, "submodules"], [13, "submodules"], [14, "submodules"], [16, "submodules"], [17, "submodules"], [18, "submodules"], [19, "submodules"], [20, "submodules"], [21, "submodules"], [22, "submodules"]], "Subpackages": [[13, "subpackages"], [18, "subpackages"]], "Technical Overview:": [[9, "technical-overview"]], "Tests": [[2, "tests"]], "Train patectgan synthesizer on dummy data with Smartnoise-Synth": [[23, "Train-patectgan-synthesizer-on-dummy-data-with-Smartnoise-Synth"]], "Train patectgan synthesizer on private data with Smartnoise-Synth": [[23, "Train-patectgan-synthesizer-on-private-data-with-Smartnoise-Synth"]], "Train random forest classifier on sensitive data with DiffPrivLib": [[23, "Train-random-forest-classifier-on-sensitive-data-with-DiffPrivLib"]], "UnauthorizedAccessException": [[5, "unauthorizedaccessexception"]], "Users": [[27, "Users"], [32, "users"]], "Visualise all options": [[27, "Visualise-all-options"]], "Welcome to Lomas documentation": [[10, null]], "a. Compute average bill length with Smartnoise-SQL": [[23, "a.-Compute-average-bill-length-with-Smartnoise-SQL"]], "a. Getting dataset metadata": [[23, "a.-Getting-dataset-metadata"]], "b. Compute confidence interval with opendp": [[23, "b.-Compute-confidence-interval-with-opendp"]], "b. Get a dummy dataset": [[23, "b.-Get-a-dummy-dataset"]], "c. Check privacy loss budget \u03b5, \u03b4 (initial, current, remaining)": [[23, "c.-Check-privacy-loss-budget-\u03b5,-\u03b4-(initial,-current,-remaining)"]], "c. Train a DP Machine Learning model with DiffPrivLib": [[23, "c.-Train-a-DP-Machine-Learning-model-with-DiffPrivLib"]], "d. Get a Synthetic Dataset with Smartnoise-Synth": [[23, "d.-Get-a-Synthetic-Dataset-with-Smartnoise-Synth"]], "lomas_client": [[7, null]], "lomas_client package": [[11, null]], "lomas_client.client module": [[11, "lomas-client-client-module"]], "lomas_client.libraries package": [[12, null]], "lomas_client.libraries.diffprivlib module": [[12, "lomas-client-libraries-diffprivlib-module"]], "lomas_client.libraries.opendp module": [[12, "lomas-client-libraries-opendp-module"]], "lomas_client.libraries.smartnoise_sql module": [[12, "lomas-client-libraries-smartnoise-sql-module"]], "lomas_client.libraries.smartnoise_synth module": [[12, "lomas-client-libraries-smartnoise-synth-module"]], "lomas_client.utils module": [[11, "lomas-client-utils-module"]], "lomas_server": [[37, null]], "lomas_server package": [[13, null]], "lomas_server.admin_database package": [[14, null]], "lomas_server.admin_database.admin_database module": [[14, "lomas-server-admin-database-admin-database-module"]], 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\ No newline at end of file
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