From ce185970a282ebc981012cbd68bcbffbd3e151b5 Mon Sep 17 00:00:00 2001 From: Ben Thompson Date: Thu, 15 Dec 2022 13:07:20 -0500 Subject: [PATCH] Separating the database and caching infrastructure. (#180) * Starting on caching. * Successfully continuing an adagrid run from a database. * Caching configuration and null hypotheses in duckdb. * Use pottery/redis/upstash for locks and nextid. * Model data caching (e.g. rng). * birthtime, worker_id for tiles * ClickhouseStore * Why are the ids inconsistent? * Why are the ids inconsistent? * Why are the ids inconsistent? * Adding some documentation to Clickhouse and Store. * Add keyring dependency. * Mock keyring when no keyring is available. * Allow get_password to return None. --- .github/workflows/test.yml | 6 +- .pre-commit-config.yaml | 9 + .vscode/settings.json | 1 + confirm/cloud/clickhouse.py | 420 +- confirm/imprint/__init__.py | 4 +- confirm/imprint/adagrid.py | 427 +- confirm/imprint/db.py | 39 +- confirm/imprint/driver.py | 22 +- confirm/imprint/store.py | 162 + confirm/models/binom1d.py | 23 +- confirm/models/ztest.py | 2 +- poetry.lock | 4095 +++++++++-------- pyproject.toml | 7 +- .../cloud_explore/batch_cdk/run_confirm.py | 2 +- research/cloud_explore/clickhouse/test.ipynb | 8 +- research/cloud_explore/clickhouse/test.md | 4 +- research/cloud_explore/modal/bandwidth.py | 30 + research/cloud_explore/modal/main.py | 2 +- research/design/binom1d.ipynb | 2 +- research/design/binom1d.md | 2 +- research/design/cache.ipynb | 1038 +++++ research/design/cache.md | 281 ++ research/design/fisher.ipynb | 2 +- research/design/fisher.md | 2 +- research/design/ztest.ipynb | 2 +- research/design/ztest.md | 2 +- setup.cfg | 2 +- tests/end2end/__snapshot__/test_adagrid_2.csv | 80 +- tests/end2end/test_adagrid.py | 47 +- tests/end2end/test_models.py | 4 +- tests/test_clickhouse.py | 31 +- tests/test_duckdb.py | 12 +- tests/test_grid.py | 1 + tests/test_store.py | 94 + 34 files changed, 4538 insertions(+), 2327 deletions(-) create mode 100644 confirm/imprint/store.py create mode 100644 research/cloud_explore/modal/bandwidth.py create mode 100644 research/design/cache.ipynb create mode 100644 research/design/cache.md create mode 100644 tests/test_store.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e0635d49..40351784 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -51,14 +51,14 @@ jobs: - name: Update environment run: | - poetry env use ${{ matrix.python-version }} && poetry install --with=test + poetry env use ${{ matrix.python-version }} && poetry install --with=test,cloud - name: Get changed files id: changed-files uses: tj-actions/changed-files@v34.5.3 if: github.ref != 'refs/heads/main' - - name: Run all pre-commit checks on change files. + - name: Run all pre-commit checks on changed files. run: poetry run pre-commit run --color=always --files ${{ steps.changed-files.outputs.all_changed_files }} if: github.ref != 'refs/heads/main' @@ -67,7 +67,7 @@ jobs: if: github.ref == 'refs/heads/main' - name: Pytest - run: poetry run pytest -n auto + run: poetry run pytest -n auto -s # TODO: this doesn't currently work because only the imprint repo has the SLACK_BOT_TOKEN # - name: Report failures on Slack diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index f7096384..4a498a06 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,11 +1,20 @@ fail_fast: false repos: + # We have black here twice because the first black block runs black on + # Jupyter notebooks. And we don't want to run black on notebooks in the + # research folder because 1) it's slow. 2) it can overwrite a notebook in + # current use causing the session to be lost. 3) there's no reason, those + # notebooks are exploration anyway. - repo: https://github.com/psf/black rev: 22.10.0 hooks: - id: black-jupyter language_version: python3 exclude: research/ + - repo: https://github.com/psf/black + rev: 22.10.0 + hooks: + - id: black - repo: https://github.com/pycqa/flake8 rev: 5.0.4 hooks: diff --git a/.vscode/settings.json b/.vscode/settings.json index f1da396f..8606f8ac 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -3,6 +3,7 @@ "python.testing.pytestEnabled": true, "python.linting.pylintEnabled": false, "python.linting.flake8Enabled": true, + "python.linting.flake8Args": ["--ignore=E1,E2,E3,E4,E5,W1,W2,W3,W4,W5"], "python.linting.enabled": true, "python.formatting.provider": "black", "autoDocstring.docstringFormat": "google-notypes", diff --git a/confirm/cloud/clickhouse.py b/confirm/cloud/clickhouse.py index 4d28e683..6fd7a43b 100644 --- a/confirm/cloud/clickhouse.py +++ b/confirm/cloud/clickhouse.py @@ -5,15 +5,156 @@ from typing import List import clickhouse_connect -import keyring +import pottery + +try: + import keyring + + assert keyring.get_keyring().priority +except (ImportError, AssertionError): + # No suitable keyring is available, so mock the interface + # to simulate no pw. + # https://github.com/jeffwidman/bitbucket-issue-migration/commit/f4a2e18b1a8e54ee8e265bf71d0808c5a99f66f9 + class keyring: + get_password = staticmethod(lambda system, username: None) + + import pyarrow +import redis + +from confirm.imprint.store import is_table_name +from confirm.imprint.store import Store + +type_map = { + "uint32": "UInt32", + "uint64": "UInt64", + "float32": "Float32", + "float64": "Float64", + "int32": "Int32", + "int64": "Int64", + "bool": "Boolean", + "string": "String", + "object": "String", +} + + +def get_create_table_cols(df): + """ + Map from Pandas dtypes to Clickhouse types. + + Args: + df: The dataframe + + Returns: + A list of strings of the form "column_name type" + """ + return [f"{c} {type_map[dt.name]}" for c, dt in zip(df.columns, df.dtypes)] + + +def _query_df(client, query): + # Loading via Arrow and then converting to Pandas is faster than using + # query_df directly to load a pandas dataframe. I'm guessing that loading + # through arrow is a little less flexible or something, but for our + # purposes, this is great. + return client.query_arrow(query).to_pandas() + + +def _insert_df(client, table, df): + # Save as _query_df, inserting through arrow is faster! + client.insert_arrow(table, pyarrow.Table.from_pandas(df, preserve_index=False)) + + +@dataclass +class ClickhouseStore(Store): + """ + A store using Clickhouse as the backend. + """ + + client: clickhouse_connect.driver.httpclient.HttpClient + + def __post_init__(self): + self.client.command( + """ + create table if not exists store_tables + (key String, table_name String) + order by key + """ + ) + + def get(self, key): + exists, table_name = self._exists(key) + print(exists, table_name) + if exists: + return _query_df(self.client, f"select * from {table_name}") + else: + raise KeyError(f"Key {key} not found in store") + + def set(self, key, df, nickname=None): + exists, table_name = self._exists(key) + if exists: + self.client.command(f"drop table {table_name}") + else: + table_id = uuid.uuid4().hex + if is_table_name(key): + table_name = key + else: + table_name = ( + f"_store_{nickname}_{table_id}" + if nickname is not None + else f"_store_{table_id}" + ) + self.client.command( + "insert into store_tables values (%s, %s)", (key, table_name) + ) + cols = get_create_table_cols(df) + # if "id" not in df.columns: + # df["id"] = df.index + self.client.command( + f""" + create table {table_name} ({",".join(cols)}) + engine = MergeTree() order by tuple() + """ + ) + _insert_df(self.client, table_name, df) + + def set_or_append(self, key, df): + exists, table_name = self._exists(key) + if exists: + print("exists", table_name, df) + _insert_df(self.client, table_name, df) + else: + self.set(key, df) + + def _exists(self, key): + table_name = self.client.query( + "select table_name from store_tables where key = %s", (key,) + ).result_set + if len(table_name) == 0: + return False, None + else: + return True, table_name[0][0] + + def exists(self, key): + return self._exists(key)[0] @dataclass class Clickhouse: """ A tile database built on top of Clickhouse. This should be very fast and - robust and is preferred for large runs. + robust and is preferred for large runs. Latency will be worse than with + DuckDB because a network request will be required for each query. + + See the DuckDBTiles or PandasTiles implementations for details on the + Adagrid tile database interface. + + Internally, this also uses a Redis server for distributed locks and for + robustly incrementing the worker_id. We don't use Clickhouse for these + tasks because Clickhouse does not have any strong consistency tools or + tools for locking a table. + + The active and eligible tile columns are stored across two places in order + to construct an append-only table structure. active: True if the tile is a leaf in the tile tree. Specified in two ways - the "active" column in the tiles table @@ -29,91 +170,114 @@ class Clickhouse: The reason for these dual sources of information is the desire to only rarely update rows that have already been written. Updating data is painful - in Clickhouse. It's currently unimplemented, but we could periodically - flush the tiles_ineligible and tiles_inactive tables to corresponding - columns in the tiles table. This would reduce the size of the subqueries - and probably improve performance for very large jobs. + in Clickhouse. We could periodically flush the tiles_ineligible and + tiles_inactive tables to corresponding columns in the tiles table. This + would reduce the size of the subqueries and probably improve performance + for very large jobs. But, this flushing would probably only need to happen + once every few hours. NOTE: A tile may be active but ineligible if it was selected for work but the criterion decided it was not worth refining or deepening. + + NOTE: We could explore a Clickhouse projection or a Clickhouse materialized + view as alternative approaches to the dual sources of information for + active and eligible tiles. """ client: clickhouse_connect.driver.httpclient.HttpClient + redis_con: redis.Redis + host: str job_id: str worker_id: int _columns: List[str] = None + _d: int = None - def _query_df(self, query): - return self.client.query_arrow(query).to_pandas() + @property + def store(self): + return ClickhouseStore(self.client) - def _insert_df(self, table, df): - self.client.insert_arrow( - table, pyarrow.Table.from_pandas(df, preserve_index=False) - ) + def dimension(self): + if self._d is None: + self._d = ( + max([int(c[5:]) for c in self.columns() if c.startswith("theta")]) + 1 + ) + return self._d def columns(self): if self._columns is None: - self._columns = self._query_df("select * from tiles limit 1").columns + self._columns = _query_df( + self.client, "select * from tiles limit 1" + ).columns return self._columns def get_all(self): cols = ",".join([c for c in self.columns() if c not in ["active", "eligible"]]) - return self._query_df( + return _query_df( + self.client, f""" select {cols}, and(active=true, (id not in (select id from tiles_inactive))) as active, and(eligible=true, (id not in (select id from work))) as eligible from tiles - """ + """, ) def write(self, df): - self._insert_df("tiles", df) + _insert_df(self.client, "tiles", df) def worst_tile(self, order_col): - return self._query_df( + return _query_df( + self.client, f""" select * from tiles where active=true and id not in (select * from tiles_inactive) order by {order_col} limit 1 - """ + """, ) def next(self, n, order_col): - out = self._query_df( - f""" - select * from tiles where - eligible=true - and id not in (select id from work) - order by {order_col} asc limit {n} - """ + lock = pottery.Redlock( + key=f"{self.host}/{self.job_id}/lock", masters={self.redis_con} ) - T = time.time() - out["time"] = T - out["worker_id"] = self.worker_id - self._insert_df("work", out[["id", "time", "worker_id"]]) - - conflicts = self._query_df( - f""" - with mine as ( - select id, time from work where - worker_id={self.worker_id} - and time={T} + with lock: + out = _query_df( + self.client, + f""" + select * from tiles where + eligible=true + and id not in (select id from work) + order by {order_col} asc limit {n} + """, ) - select worker_id, time from work - join mine on ( - work.id=mine.id - and work.worker_id != {self.worker_id} - ) - where work.time <= mine.time - """ - ) - assert conflicts.shape[0] == 0 + T = time.time() + out["time"] = T + out["worker_id"] = self.worker_id + _insert_df(self.client, "work", out[["id", "time", "worker_id"]]) + + # No need to check for conflicts because we are using a lock but if we + # were to check for conflicts, it would look something like this + # conflicts = _query_df( + # self.client, + # f""" + # with mine as ( + # select id, time from work where + # worker_id={self.worker_id} + # and time={T} + # ) + # select worker_id, time from work + # join mine on ( + # work.id=mine.id + # and work.worker_id != {self.worker_id} + # ) + # where work.time <= mine.time + # """ + # ) + # assert conflicts.shape[0] == 0 return out def finish(self, which): - self._insert_df("tiles_inactive", which.loc[~which["active"]][["id"]]) + _insert_df(self.client, "tiles_inactive", which.loc[~which["active"]][["id"]]) def bootstrap_lamss(self): # Get the number of bootstrap lambda* columns @@ -135,28 +299,16 @@ def close(self): self.client.close() def init_tiles(self, df): - types = { - "uint32": "UInt32", - "uint64": "UInt64", - "float32": "Float32", - "float64": "Float64", - "int32": "Int32", - "int64": "Int64", - "bool": "Boolean", - "string": "String", - "bytes640": "FixedString(80)", - } - cols = [f"{c} {types[dt.name]}" for c, dt in zip(df.columns, df.dtypes)] - id_type = types[df["id"].dtype.name] - + cols = get_create_table_cols(df) orderby = "orderer" if "orderer" in df.columns else "id" - self.client.command( f""" create table tiles ({",".join(cols)}) engine = MergeTree() order by {orderby} """ ) + + id_type = type_map[df["id"].dtype.name] self.client.command( f""" create table tiles_inactive (id {id_type}) @@ -174,35 +326,135 @@ def init_tiles(self, df): def connect( job_id: int = None, - worker_id: int = 0, host=None, port=None, username=None, password=None, + redis_host=None, + redis_port=None, + redis_password=None, ): + """ + Connect to a Clickhouse server and to our Redis server. + + Each job_id corresponds to a Clickhouse database on the Clickhouse + cluster. If job_id is None, we will find + + For Clickhouse, we will use the following environment variables: + CLICKHOUSE_HOST: The hostname for the Clickhouse server. + CLICKHOUSE_PORT: The Clickhouse server port. + CLICKHOUSE_USERNAME: The Clickhouse username. + CLICKHOUSE_PASSWORD: The Clickhouse username. + + If the environment variables are not set, the defaults will be: + host: keyring entry "clickhouse-confirm-test-host" + port: 8443 + username: "default" + password: keyring entry "clickhouse-confirm-test-password" + + For Redis, we will use the following environment variables: + REDIS_HOST: The hostname for the Redis server. + REDIS_PORT: The Redis server port. + REDIS_PASSWORD: The Redis password. + + If the environment variables are not set, the defaults will be: + host: keyring entry "upstash-confirm-coordinator-host" + port: 37085 + password: keyring entry "upstash-confirm-coordinator-password" + + Args: + job_id: The job_id. Defaults to None. + host: The hostname for the Clickhouse server. Defaults to None. + port: The Clickhouse server port. Defaults to None. + username: The Clickhouse username. Defaults to None. + password: The Clickhouse password. Defaults to None. + redis_host: The Redis hostname. Defaults to None. + redis_port: The Redis port. Defaults to None. + redis_password: The Redis password. Defaults to None. + + Returns: + A Clickhouse tile database object. + """ if job_id is None: - client = get_client(host, port, username, password) + config = get_ch_config(host, port, username, password) + test_host = keyring.get_password( + "clickhouse-confirm-test-host", os.environ["USER"] + ) + if not ( + (test_host is not None and test_host in config["host"]) + or "localhost" in config["host"] + ): + raise RuntimeError( + "To run a production job, please choose an explicit unique job_id." + ) + client = clickhouse_connect.get_client(**config) job_id = find_unique_job_id(client) - client = get_client(host, port, username, password, job_id) - return Clickhouse(client, job_id, worker_id) + connection_details = get_ch_config(host, port, username, password, job_id) + host = connection_details["host"] + client = clickhouse_connect.get_client(**connection_details) + redis_con = redis.Redis( + **get_redis_config(redis_host, redis_port, redis_password) + ) + worker_id = next( + pottery.NextId( + key=f"{host}/{job_id}/worker_id", + masters={redis_con}, + ) + ) + print(f"Connected to job {job_id} as worker {worker_id}") + return Clickhouse(client, redis_con, host, job_id, worker_id) + + +def get_redis_config(host=None, port=None, password=None): + if host is None: + if "REDIS_HOST" in os.environ: + host = os.environ["REDIS_HOST"] + else: + host = keyring.get_password( + "upstash-confirm-coordinator-host", os.environ["USER"] + ) + if port is None: + if "REDIS_PORT" in os.environ: + port = os.environ["REDIS_PORT"] + else: + port = 37085 + if password is None: + if "REDIS_PASSWORD" in os.environ: + password = os.environ["REDIS_PASSWORD"] + else: + password = keyring.get_password( + "upstash-confirm-coordinator-password", os.environ["USER"] + ) + return dict(host=host, port=port, password=password) -def get_client(host=None, port=None, username=None, password=None, database=None): +def get_ch_config(host=None, port=None, username=None, password=None, database=None): if host is None: if "CLICKHOUSE_HOST" in os.environ: host = os.environ["CLICKHOUSE_HOST"] - host = keyring.get_password("clickhouse-confirm-test-host", os.environ["USER"]) + else: + host = keyring.get_password( + "clickhouse-confirm-test-host", os.environ["USER"] + ) if port is None: - port = 8443 + if "CLICKHOUSE_PORT" in os.environ: + port = os.environ["CLICKHOUSE_PORT"] + else: + port = 8443 if username is None: - username = "default" + if "CLICKHOUSE_USERNAME" in os.environ: + username = os.environ["CLICKHOUSE_USERNAME"] + else: + username = "default" if password is None: if "CLICKHOUSE_PASSWORD" in os.environ: password = os.environ["CLICKHOUSE_PASSWORD"] - password = keyring.get_password( - "clickhouse-confirm-test-password", os.environ["USER"] - ) - return clickhouse_connect.get_client( + else: + password = keyring.get_password( + "clickhouse-confirm-test-password", os.environ["USER"] + ) + print(f"Connecting to {host}:{port}/{database} as {username}.") + return dict( host=host, port=port, username=username, password=password, database=database ) @@ -228,6 +480,24 @@ def find_unique_job_id(client, attempts=3): def clear_dbs(client): + """ + DANGER, WARNING, ACHTUNG, PELIGRO: + Don't run this function for our production Clickhouse server. That + would be bad. There's a built-in safeguard to prevent this, but it's + not foolproof. + + Clear all databases (and database tables) from the Clickhouse server. This + should only work for our test database or for localhost. + + Args: + client: _description_ + """ + test_host = keyring.get_password("clickhouse-confirm-test-host", os.environ["USER"]) + if not ( + (test_host is not None and test_host in client.url) or "localhost" in client.url + ): + raise RuntimeError("This function is only for localhost or test databases.") + to_drop = [] for db in client.query_df("show databases")["name"]: if db not in ["default", "INFORMATION_SCHEMA", "information_schema", "system"]: @@ -241,4 +511,6 @@ def clear_dbs(client): print("Are you sure? [yN]") if input() == "y": for db in to_drop: - client.command(f"drop database {db}") + cmd = f"drop database {db}" + print(cmd) + client.command(cmd) diff --git a/confirm/imprint/__init__.py b/confirm/imprint/__init__.py index 24d4083e..7b042375 100644 --- a/confirm/imprint/__init__.py +++ b/confirm/imprint/__init__.py @@ -1,8 +1,8 @@ -from .adagrid import ada_tune +from .adagrid import ada_calibrate from .adagrid import ada_validate from .batching import batch from .batching import batch_all -from .driver import tune +from .driver import calibrate from .driver import validate from .grid import cartesian_grid from .grid import Grid diff --git a/confirm/imprint/adagrid.py b/confirm/imprint/adagrid.py index 6325bf83..0161c744 100644 --- a/confirm/imprint/adagrid.py +++ b/confirm/imprint/adagrid.py @@ -44,7 +44,11 @@ - worst tile: the tile for which lams is smallest. `lams[worst_tile] == lamss` """ import copy +import json +import platform +import subprocess import time +from dataclasses import dataclass from pprint import pprint import jax @@ -55,52 +59,56 @@ from . import batching from . import driver from . import grid -from .db import DuckDB +from .db import DuckDBTiles -class AdagridDriver: +class AdaCalibrationDriver: """ Driver classes are the layer of imprint that is directly responsible for asking the Model for simulations. This driver has two entrypoints: - 1. `bootstrap_tune(...)`: tunes (calculating lambda*) for every tile in a + 1. `bootstrap_calibrate(...)`: calibrates (calculating lambda*) for every tile in a grid. The calibration is performed for many bootstrap resamplings of the simulated test statistics. This bootstrap gives a picture of the distribution of lambda*. 2. `many_rej(...)`: calculates the number of rejections for many different values values of lambda*. - For the basic `validate` and `tune` drivers, see `driver.py`. + For the basic `validate` and `calibrate` drivers, see `driver.py`. """ - def __init__( - self, model, *, init_K, n_K_double, nB, bootstrap_seed, tile_batch_size - ): + def __init__(self, db, model, null_hypos, config): + self.db = db self.model = model + self.null_hypos = null_hypos + self.c = config self.forward_boundv, self.backward_boundv = driver.get_bound( model.family, model.family_params if hasattr(model, "family_params") else {} ) - self.tunevv = jax.jit( + self.calibratevv = jax.jit( jax.vmap( jax.vmap(driver.calc_tuning_threshold, in_axes=(None, 0, None)), in_axes=(0, None, 0), ) ) - bootstrap_key = jax.random.PRNGKey(bootstrap_seed) - self.init_K = init_K - self.Ks = init_K * 2 ** np.arange(n_K_double + 1) + bootstrap_key = jax.random.PRNGKey(self.c.bootstrap_seed) + self.init_K = self.c.init_K + self.Ks = self.c.init_K * 2 ** np.arange(self.c.n_K_double + 1) self.max_K = self.Ks[-1] - self.nB = nB + self.nB = self.c.nB self.bootstrap_idxs = { K: jnp.concatenate( ( jnp.arange(K)[None, :], jnp.sort( jax.random.choice( - bootstrap_key, K, shape=(nB + nB, K), replace=True + bootstrap_key, + K, + shape=(self.c.nB + self.c.nB, K), + replace=True, ), axis=-1, ), @@ -109,19 +117,26 @@ def __init__( for K in self.Ks } - self.tile_batch_size = tile_batch_size + self.tile_batch_size = self.c.tile_batch_size - def _batched_bootstrap_tune(self, K, theta, vertices, null_truth, alpha): + self.alpha = self.c.alpha + self.calibration_min_idx = self.c.calibration_min_idx + self.bias_target = self.c.bias_target + self.grid_target = self.c.grid_target + self.std_target = self.c.std_target + self.iter_size = self.c.iter_size + + def _batched_bootstrap_calibrate(self, K, theta, vertices, null_truth, alpha): # NOTE: sort of a todo. having the simulations loop as the outer batch # is faster than the other way around. But, we need all simulations in - # order to tune. So, the likely fastest solution is to have multiple + # order to calibrate. So, the likely fastest solution is to have multiple # layers of batching. This is not currently implemented. stats = self.model.sim_batch(0, K, theta, null_truth) sorted_stats = jnp.sort(stats, axis=-1) alpha0 = self.backward_boundv(alpha, theta, vertices) - return self.tunevv(sorted_stats, self.bootstrap_idxs[K], alpha0), alpha0 + return self.calibratevv(sorted_stats, self.bootstrap_idxs[K], alpha0), alpha0 - def _bootstrap_tune(self, K_df, alpha, tile_batch_size=None): + def _bootstrap_calibrate(self, K_df, alpha, tile_batch_size=None): if tile_batch_size is None: tile_batch_size = self.tile_batch_size @@ -131,7 +146,7 @@ def _bootstrap_tune(self, K_df, alpha, tile_batch_size=None): theta, vertices = K_g.get_theta_and_vertices() bootstrap_lams, alpha0 = batching.batch( - self._batched_bootstrap_tune, + self._batched_bootstrap_calibrate, tile_batch_size, in_axes=(None, 0, 0, 0, None), )(K, theta, vertices, K_g.get_null_truth(), alpha) @@ -149,9 +164,9 @@ def _bootstrap_tune(self, K_df, alpha, tile_batch_size=None): lams_df.insert(0, "alpha0", alpha0) return lams_df - def bootstrap_tune(self, df, alpha, tile_batch_size=None): + def bootstrap_calibrate(self, df, alpha, tile_batch_size=None): return df.groupby("K", group_keys=False).apply( - lambda K_df: self._bootstrap_tune(K_df, alpha, tile_batch_size) + lambda K_df: self._bootstrap_calibrate(K_df, alpha, tile_batch_size) ) def _many_rej(self, K_df, lams_arr): @@ -169,58 +184,13 @@ def many_rej(self, df, lams_arr): lambda K_df: self._many_rej(K_df, lams_arr) ) - -class AdaCalibration: - def __init__( - self, - ada_driver, - *, - g=None, - db=None, - grid_target, - bias_target, - std_target, - iter_size, - alpha, - calibration_min_idx, - ): - self.ada_driver = ada_driver - self.alpha = alpha - self.calibration_min_idx = calibration_min_idx - self.bias_target = bias_target - self.grid_target = grid_target - self.std_target = std_target - self.iter_size = iter_size - - if db is None: - if g is None: - raise ValueError( - "Must provide either an initial grid or an existing" - " database! Set either g or db." - ) - self.db = DuckDB.connect() - else: - self.db = db - - if g is not None: - self.null_hypos = g.null_hypos - # Copy the input grid so that the caller is not surprised by any changes. - g = copy.deepcopy(g) - g.df["K"] = self.ada_driver.init_K - - # We process the tiles before adding them to the database so that the - # database will be initialized with the correct set of columns. - g_tuned = self._process_tiles(g, 0) - self.db.init_tiles(g_tuned.df) - # self.db.init_null_hypos(self.null_hypos) - def _process_tiles(self, g, i): # This method actually runs the calibration and bootstrapping. # It is called once per iteration. # Several auxiliary fields are calculated because they are needed for # selecting the next iteration's tiles: impossible and orderer - lams_df = self.ada_driver.bootstrap_tune(g.df, self.alpha) + lams_df = self.bootstrap_calibrate(g.df, self.alpha) # we use insert here to order columns nicely for reading raw data lams_df.insert(1, "grid_cost", self.alpha - lams_df["alpha0"]) lams_df.insert( @@ -240,9 +210,11 @@ def _process_tiles(self, g, i): np.where(lams_df["impossible"], -np.inf, np.inf), ), ) - g_tuned = g.add_cols(lams_df) - g_tuned.df["birthday"] = i - return g_tuned + g_calibrated = g.add_cols(lams_df) + g_calibrated.df["worker_id"] = self.db.worker_id + g_calibrated.df["birthiter"] = i + g_calibrated.df["birthtime"] = time.time() + return g_calibrated def step(self, i): """ @@ -266,7 +238,7 @@ def step(self, i): # We determine the bias by comparing the Type I error at the worst # tile for each lambda**_B: B_lamss = self.db.bootstrap_lamss() - worst_tile_tie_sum = self.ada_driver.many_rej( + worst_tile_tie_sum = self.many_rej( worst_tile, np.array([lamss] + list(B_lamss)) ).iloc[0][0] worst_tile_tie_est = worst_tile_tie_sum / worst_tile["K"].iloc[0] @@ -344,8 +316,8 @@ def step(self, i): for col in twb_worst_tile.columns: if col.startswith("radii"): twb_worst_tile[col] = 1e-6 - twb_worst_tile["K"] = self.ada_driver.max_K - twb_worst_tile_lams = self.ada_driver.bootstrap_tune( + twb_worst_tile["K"] = self.max_K + twb_worst_tile_lams = self.bootstrap_calibrate( twb_worst_tile, self.alpha, tile_batch_size=1 ) twb_worst_tile_mean_lams = twb_worst_tile_lams["twb_mean_lams"].iloc[0] @@ -355,7 +327,7 @@ def step(self, i): # Step 5: Decide whether to refine or deepen # THIS IS IT! This is where we decide whether to refine or deepen. ######################################## - at_max_K = work["K"] == self.ada_driver.max_K + at_max_K = work["K"] == self.max_K work["refine"] = at_max_K work["refine"] |= (grid_cost > self.grid_target) & (~deepen_likely_to_work) work["deepen"] = ~work["refine"] @@ -372,14 +344,14 @@ def step(self, i): report["runtime_refine_deepen"] = time.time() - start_refine_deepen start_processing = time.time() - g_tuned_new = self._process_tiles(g_new, i) + g_calibrated_new = self._process_tiles(g_new, i) report["runtime_processing"] = time.time() - start_processing start_cleanup = time.time() - self.db.write(g_tuned_new.df) + self.db.write(g_calibrated_new.df) report.update( dict( - n_processed=g_tuned_new.n_tiles, - K_distribution=g_tuned_new.df["K"].value_counts().to_dict(), + n_processed=g_calibrated_new.n_tiles, + K_distribution=g_calibrated_new.df["K"].value_counts().to_dict(), ) ) @@ -438,26 +410,181 @@ def print_report(_iter, report, _ada): pprint(ready) -def ada_tune( - modeltype, - *, - g=None, - db=None, +def _run(cmd): + try: + return ( + subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True) + .decode("utf-8") + .strip() + ) + except subprocess.CalledProcessError as exc: + return f"ERROR: {exc.returncode} {exc.output}" + + +def _get_git_revision_hash() -> str: + return _run(["git", "rev-parse", "HEAD"]) + + +def _get_git_diff() -> str: + return _run(["git", "diff", "HEAD"]) + + +def _get_nvidia_smi() -> str: + return _run(["nvidia-smi"]) + + +def _get_pip_freeze() -> str: + return _run(["pip", "freeze"]) + + +def _get_conda_list() -> str: + return _run(["conda", "list"]) + + +calibration_defaults = dict( + model_name="invalid", model_seed=0, + model_kwargs=None, alpha=0.025, init_K=2**13, n_K_double=4, bootstrap_seed=0, nB=50, - tile_batch_size=64, + tile_batch_size=None, grid_target=0.001, bias_target=0.001, std_target=0.002, calibration_min_idx=40, iter_size=2**10, n_iter=100, - callback=print_report, + worker_id=None, + git_hash=None, + git_diff=None, + nvidia_smi=None, +) + + +@dataclass +class CalibrationConfig: + """ + CalibrationConfig is a dataclass that holds all the configuration for a + calibration run. For each worker, the data here will be written to the + database so that we can keep track of what was run and how. + """ + + modeltype: type + model_seed: int + model_kwargs: dict + alpha: float + init_K: int + n_K_double: int + bootstrap_seed: int + nB: int + tile_batch_size: int + grid_target: float + bias_target: float + std_target: float + calibration_min_idx: int + iter_size: int + n_iter: int + worker_id: int + git_hash: str = None + git_diff: str = None + nvidia_smi: str = None + pip_freeze: str = None + conda_list: str = None + platform: str = None + defaults: dict = None + + def __post_init__(self): + self.git_hash = _get_git_revision_hash() + self.git_diff = _get_git_diff() + self.platform = platform.platform() + self.nvidia_smi = _get_nvidia_smi() + self.pip_freeze = _get_pip_freeze() + self.conda_list = _get_conda_list() + + self.tile_batch_size = self.tile_batch_size or ( + 64 if jax.lib.xla_bridge.get_backend().platform == "gpu" else 4 + ) + self.model_name = self.modeltype.__name__ # noqa + if self.model_kwargs is None: + self.model_kwargs = {} + # TODO: is json suitable for all models? are there models that are going to + # want to have large objects as parameters? + self.model_kwargs = json.dumps(self.model_kwargs) + + continuation = True + if self.defaults is None: + self.defaults = calibration_defaults + continuation = False + + for k in self.defaults: + if self.__dict__[k] is None: + self.__dict__[k] = self.defaults[k] + + # If we're continuing a calibration, make sure that fixed parameters + # are the same across all workers. + if continuation: + for k in [ + "model_seed", + "model_kwargs", + "alpha", + "init_K", + "n_K_double", + "bootstrap_seed", + "nB", + "model_name", + ]: + if self.__dict__[k] != self.defaults[k]: + raise ValueError( + f"Fixed parameter {k} has different values across workers." + ) + + config_dict = {k: self.__dict__[k] for k in self.defaults} + self.config_df = pd.DataFrame([config_dict]) + + +def _load_null_hypos(db): + d = db.dimension() + null_hypos_df = db.store.get("null_hypos") + null_hypos = [] + for i in range(null_hypos_df.shape[0]): + n = np.array([null_hypos_df[f"n{i}"].iloc[i] for i in range(d)]) + c = null_hypos_df["c"].iloc[i] + null_hypos.append(grid.HyperPlane(n, c)) + return null_hypos + + +def _store_null_hypos(db, null_hypos): + d = db.dimension() + n_hypos = len(null_hypos) + cols = {f"n{i}": [null_hypos[j].n[i] for j in range(n_hypos)] for i in range(d)} + cols["c"] = [null_hypos[j].c for j in range(n_hypos)] + null_hypos_df = pd.DataFrame(cols) + db.store.set("null_hypos", null_hypos_df) + + +def ada_calibrate( + modeltype, + *, + g=None, + db=None, + model_seed: int = None, model_kwargs=None, + alpha: float = None, + init_K: int = None, + n_K_double: int = None, + bootstrap_seed: int = None, + nB: int = None, + tile_batch_size: int = None, + grid_target: float = None, + bias_target: float = None, + std_target: float = None, + calibration_min_idx: int = None, + iter_size: int = None, + n_iter: int = None, + callback=print_report, ): """ The main entrypoint for the adaptive calibration algorithm. @@ -465,7 +592,10 @@ def ada_tune( Args: modeltype: The model class to use. g: The initial grid. + db: The database backend to use. Defaults to `db.DuckDB.connect()`. model_seed: The random seed for the model. Defaults to 0. + model_kwargs: Additional keyword arguments for constructing the Model + object. Defaults to None. alpha: The target type I error control level. Defaults to 0.025. init_K: Initial K for the first tiles. Defaults to 2**13. n_K_double: The number of doublings of K. The maximum K will be @@ -473,6 +603,7 @@ def ada_tune( bootstrap_seed: The random seed for bootstrapping. Defaults to 0. nB: The number of bootstrap samples. Defaults to 50. tile_batch_size: The number of tiles to simulate in a single batch. + Defaults to 64 on GPU and 4 on CPU. grid_target: Part of the stopping criterion: the target slack from CSE. Defaults to 0.001. bias_target: Part of the stopping criterion: the target bias as @@ -488,69 +619,103 @@ def ada_tune( alpha0 will need to be larger. Defaults to 40. n_iter: The number of adagrid iterations to run. Defaults to 100. - db_type: The database backend to use. Defaults to db.DuckDBTiles. callback: A function accepting three arguments (ada_iter, report, ada) that can perform some reporting or printing at each iteration. Defaults to print_report. - model_kwargs: Additional keyword arguments for constructing the Model - object. Defaults to None. Returns: ada_iter: The final iteration number. reports: A list of the report dicts from each iteration. ada: The Adagrid object after the final iteration. """ + + ######################################## + # Setup the database, load + ######################################## + if g is None and db is None: + raise ValueError("Must provide either an initial grid or a database!") + + if db is None: + db = DuckDBTiles.connect() + + ######################################## + # Store config + ######################################## + + defaults = None + if g is None: + defaults = db.store.get("config").iloc[0].to_dict() + + c = CalibrationConfig( + modeltype, + model_seed, + model_kwargs, + alpha, + init_K, + n_K_double, + bootstrap_seed, + nB, + tile_batch_size, + grid_target, + bias_target, + std_target, + calibration_min_idx, + iter_size, + n_iter, + db.worker_id, + defaults=defaults, + ) + db.store.set_or_append("config", c.config_df) + + ######################################## + # Set up model, driver and grid + ######################################## + # Why initialize the model here rather than relying on the user to pass an # already constructed model object? # 1. We can pass the seed here. # 2. We can pass the correct max_K and avoid a class of errors. - if model_kwargs is None: - model_kwargs = {} - model = modeltype(seed=model_seed, max_K=init_K * 2**n_K_double, **model_kwargs) - - ada_driver = AdagridDriver( - model, - init_K=init_K, - n_K_double=n_K_double, - nB=nB, - bootstrap_seed=bootstrap_seed, - tile_batch_size=tile_batch_size, + model = modeltype( + seed=c.model_seed, + max_K=c.init_K * 2**c.n_K_double, + **json.loads(c.model_kwargs), ) + null_hypos = _load_null_hypos(db) if g is None else g.null_hypos + ada_driver = AdaCalibrationDriver(db, model, null_hypos, c) - ada = AdaCalibration( - ada_driver, - g=g, - db=db, - grid_target=grid_target, - bias_target=bias_target, - std_target=std_target, - iter_size=iter_size, - alpha=alpha, - calibration_min_idx=calibration_min_idx, - ) + if g is not None: + # Copy the input grid so that the caller is not surprised by any changes. + g = copy.deepcopy(g) + g.df["K"] = c.init_K - try: - reports = [] - # We start at iteration 1 because the initial grid is iteration 0. - for ada_iter in range(1, n_iter): - done, report = ada.step(ada_iter) + # We process the tiles before adding them to the database so that the + # database will be initialized with the correct set of columns. + g_calibrated = ada_driver._process_tiles(g, 0) + db.init_tiles(g_calibrated.df) + _store_null_hypos(db, null_hypos) + + ######################################## + # Run adagrid! + ######################################## + + reports = [] + for ada_iter in range(c.n_iter): + try: + done, report = ada_driver.step(ada_iter) if callback is not None: - callback(ada_iter, report, ada) + callback(ada_iter, report, ada_driver) reports.append(report) if done: break - except KeyboardInterrupt: - return ada_iter, reports, ada - # TODO: return db instead of ada? - return ada_iter, reports, ada + except KeyboardInterrupt: + print("KeyboardInterrupt") + return ada_iter, reports, db + return ada_iter + 1, reports, db -def _validation_process_tiles(driver, g, lam, delta, i, transformation): + +def _validation_process_tiles(db, driver, g, lam, delta, i, transformation): print("processing ", g.n_tiles) - # TODO: this is ugly - # more generally, this idea of having a "computational" grid and an "input" - # grid might not be the ideal solution for our problems. i'm not sure! - # potential problem: null hypotheses need to take into account the transformations. if transformation is None: computational_df = g.df else: @@ -574,7 +739,9 @@ def _validation_process_tiles(driver, g, lam, delta, i, transformation): rej_df["total_cost"] = rej_df["grid_cost"] + rej_df["sim_cost"] g_val = g.add_cols(rej_df) - g_val.df["birthday"] = i + g_val.df["worker_id"] = db.worker_id + g_val.df["birthiter"] = i + g_val.df["birthtime"] = time.time() return g_val @@ -614,13 +781,15 @@ def ada_validate( "Must provide either an initial grid or an existing" " database! Set either g or db." ) - db = DuckDB.connect() + db = DuckDBTiles.connect() # TODO: fix this, not right in the midterm for restarts g = copy.deepcopy(g) null_hypos = g.null_hypos g.df["K"] = init_K - g_val = _validation_process_tiles(ada_driver, g, lam, delta, 0, transformation) + g_val = _validation_process_tiles( + db, ada_driver, g, lam, delta, 0, transformation + ) db.init_tiles(g_val.df) else: db = db @@ -695,7 +864,7 @@ def ada_validate( start_processing = time.time() g_val_new = _validation_process_tiles( - ada_driver, g_new, lam, delta, ada_iter, transformation + db, ada_driver, g_new, lam, delta, ada_iter, transformation ) db.write(g_val_new.df) report.update( diff --git a/confirm/imprint/db.py b/confirm/imprint/db.py index abbf06ca..925bf199 100644 --- a/confirm/imprint/db.py +++ b/confirm/imprint/db.py @@ -1,12 +1,17 @@ from dataclasses import dataclass +from dataclasses import field +from typing import Dict from typing import List import duckdb import pandas as pd +from confirm.imprint.store import DuckDBStore +from confirm.imprint.store import PandasStore + @dataclass -class PandasDB: +class PandasTiles: """ A tile database built on top of Pandas DataFrames. @@ -16,6 +21,18 @@ class PandasDB: """ df: pd.DataFrame = None + worker_id: int = 0 + _tables: Dict[str, pd.DataFrame] = field(default_factory=dict) + + @property + def store(self): + return PandasStore(self._tables) + + def dimension(self): + return max([int(c[5:]) for c in self.columns() if c.startswith("theta")]) + 1 + + def columns(self): + return self.df.columns def get_all(self): return self.df @@ -45,7 +62,7 @@ def init_tiles(self, df): @dataclass -class DuckDB: +class DuckDBTiles: """ A tile database built on top of DuckDB. This should be very fast and robust and is the default database for confirm. @@ -55,10 +72,22 @@ class DuckDB: """ con: duckdb.DuckDBPyConnection + # TODO: despite not supporting multiple workers, it would still be good to + # distinguish between separate runs with worker_id + worker_id: int = 0 _columns: List[str] = None + _d: int = None + store: DuckDBStore = None + + def __post_init__(self): + self.store = DuckDBStore(self.con) - def __init__(self, con): - self.con = con + def dimension(self): + if self._d is None: + self._d = ( + max([int(c[5:]) for c in self.columns() if c.startswith("theta")]) + 1 + ) + return self._d def columns(self): if self._columns is None: @@ -128,4 +157,4 @@ def connect(path=":memory:"): Returns: The tile database. """ - return DuckDB(duckdb.connect(path)) + return DuckDBTiles(duckdb.connect(path)) diff --git a/confirm/imprint/driver.py b/confirm/imprint/driver.py index 735fe09c..1743fe3a 100644 --- a/confirm/imprint/driver.py +++ b/confirm/imprint/driver.py @@ -57,7 +57,7 @@ def __init__(self, model, *, tile_batch_size): model.family, model.family_params if hasattr(model, "family_params") else {} ) - self.tunev = jax.jit( + self.calibratev = jax.jit( jax.vmap( calc_tuning_threshold, in_axes=(0, None, 0), @@ -115,29 +115,29 @@ def validate(self, df, lam, *, delta=0.01): .reset_index(drop=True) ) - def _batched_tune(self, K, theta, vertices, null_truth, alpha): + def _batched_calibrate(self, K, theta, vertices, null_truth, alpha): stats = self.model.sim_batch(0, K, theta, null_truth) sorted_stats = jnp.sort(stats, axis=-1) alpha0 = self.backward_boundv(alpha, theta, vertices) - bootstrap_lams = self.tunev(sorted_stats, np.arange(K), alpha0) + bootstrap_lams = self.calibratev(sorted_stats, np.arange(K), alpha0) return bootstrap_lams - def _tune(self, K_df, alpha): + def _calibrate(self, K_df, alpha): K = K_df["K"].iloc[0] K_g = grid.Grid(K_df) theta, vertices = K_g.get_theta_and_vertices() bootstrap_lams = batching.batch( - self._batched_tune, + self._batched_calibrate, self.tile_batch_size, in_axes=(None, 0, 0, 0, None), )(K, theta, vertices, K_g.get_null_truth(), alpha) return pd.DataFrame(bootstrap_lams, columns=["lams"]) - def tune(self, df, alpha): + def calibrate(self, df, alpha): return ( df.groupby("K", group_keys=False) - .apply(lambda K_df: self._tune(K_df, alpha)) + .apply(lambda K_df: self._calibrate(K_df, alpha)) .reset_index(drop=True) ) @@ -202,7 +202,7 @@ def validate( return rej_df -def tune( +def calibrate( modeltype, g, *, @@ -213,7 +213,7 @@ def tune( model_kwargs=None ): """ - Tune the critical threshold for a given level of Type I Error control. + calibrate the critical threshold for a given level of Type I Error control. Args: modeltype: The model class. @@ -231,5 +231,5 @@ def tune( _description_ """ driver, g = _setup(modeltype, g, model_seed, K, model_kwargs, tile_batch_size) - tune_df = driver.tune(g.df, alpha) - return tune_df + calibrate_df = driver.calibrate(g.df, alpha) + return calibrate_df diff --git a/confirm/imprint/store.py b/confirm/imprint/store.py new file mode 100644 index 00000000..c8eb430b --- /dev/null +++ b/confirm/imprint/store.py @@ -0,0 +1,162 @@ +import json +import re +from dataclasses import dataclass +from dataclasses import field +from typing import Dict + +import duckdb +import pandas as pd + + +def is_table_name(s): + if not s: + return False + + if not s[0].isalpha(): + return False + + if not re.match("^[a-zA-Z0-9_]+$", s): + return False + + return True + + +class Store: + """ + A Store is a key-value store that can save and retrieve dataframes in a + database. We use this for two purposes: + 1. To cache the results of expensive computations. + 2. To store the configuration of a job in the database. + + Tile-related information is handled separately. For example, by DuckDBTiles. + """ + + def __call__(self, func): + def wrapper(*args, **kwargs): + key = json.dumps( + dict( + f=func.__module__ + "." + func.__qualname__, + args=args, + kwargs={str(k): str(v) for k, v in kwargs.items()}, + ) + ) + if self.exists(key): + return self.get(key) + else: + result = func(*args, **kwargs) + self.set(key, result, nickname=func.__name__) + return result + + return wrapper + + +@dataclass +class PandasStore(Store): + """ + A very simple reference store implementation that stores DataFrames in memory. + """ + + _store: Dict[str, pd.DataFrame] = field(default_factory=dict) + + def get(self, key): + return self._store[key] + + def set(self, key, data, nickname=None): + self._store[key] = data + + def set_or_append(self, key, data): + if self.exists(key): + self._store[key] = pd.concat([self._store[key], data], axis=0).reset_index( + drop=True + ) + else: + self.set(key, data) + + def exists(self, key): + return key in self._store + + +@dataclass +class DuckDBStore(Store): + con: duckdb.DuckDBPyConnection + + def __post_init__(self): + self.con.execute( + "create table if not exists store_tables (key VARCHAR, table_name VARCHAR)" + ) + + def _get_all_keys(self): + return self.con.execute("select * from store_tables").df() + + def get(self, key): + exists, table_name = self._exists(key) + if exists: + return self.con.execute(f"select * from {table_name}").df() + else: + raise KeyError(f"Key {key} not found in store") + + def set(self, key, df, nickname=None): + """ + Set a key. If the key already exists, it will be overwritten. + + If the key is a valid table name, the key will be stored in that table. + If the key is not a valid table name: + - and nickname is not None, a new table will be created with a name + of the form _store_{nickname}_{idx}. + - and nickname is None, a new table will be created with a name of the + form _store_{idx}. + + Args: + key: The key + df: The + nickname: _description_. Defaults to None. + """ + exists, table_name = self._exists(key) + if exists: + self.con.execute(f"drop table {table_name}") + else: + idx = self.con.execute("select count(*) from store_tables").fetchone()[0] + if is_table_name(key): + table_name = key + else: + table_name = ( + f"_store_{nickname}_{idx}" + if nickname is not None + else f"_store_{idx}" + ) + self.con.execute( + "insert into store_tables values (?, ?)", (key, table_name) + ) + self.con.execute(f"create table {table_name} as select * from df") + + def set_or_append(self, key, df): + exists, table_name = self._exists(key) + if exists: + self.con.execute(f"insert into {table_name} select * from df") + else: + self.set(key, df) + + def _exists(self, key): + table_name = self.con.execute( + "select table_name from store_tables where key = ?", (key,) + ).fetchall() + if len(table_name) == 0: + return False, None + else: + return True, table_name[0][0] + + def exists(self, key): + return self._exists(key)[0] + + @staticmethod + def connect(path=":memory:"): + """ + Load a store database from a file. + + Args: + path: The filepath to the database. + + Returns: + The database. + """ + return DuckDBStore(duckdb.connect(path)) diff --git a/confirm/models/binom1d.py b/confirm/models/binom1d.py index 60a6461f..57e80239 100644 --- a/confirm/models/binom1d.py +++ b/confirm/models/binom1d.py @@ -1,5 +1,7 @@ import jax import jax.numpy as jnp +import numpy as np +import pandas as pd @jax.jit @@ -13,18 +15,23 @@ def _sim(samples, theta, null_truth): ) +def unifs(seed, *, shape, dtype): + samples = jax.random.uniform( + jax.random.PRNGKey(seed), shape=shape, dtype=dtype + ).ravel() + return pd.DataFrame(dict(data=[samples.tobytes()])) + + class Binom1D: - def __init__(self, seed, max_K, *, n_arm_samples): + def __init__(self, seed, max_K, *, n, store=lambda x: x): self.family = "binomial" - self.family_params = {"n": n_arm_samples} + self.family_params = {"n": n} self.dtype = jnp.float32 - self.n_arm_samples = n_arm_samples - # sample normals and then compute the CDF to transform into the - # interval [0, 1] - key = jax.random.PRNGKey(seed) - self.samples = jax.random.uniform( - key, shape=(max_K, self.n_arm_samples), dtype=self.dtype + samples_bytes = store(unifs)(seed, shape=(max_K, n), dtype=self.dtype) + # NOTE: reshape before converting to jax because jax copies on reshape + self.samples = jnp.asarray( + np.frombuffer(samples_bytes["data"].iloc[0], self.dtype).reshape((max_K, n)) ) def sim_batch(self, begin_sim, end_sim, theta, null_truth, detailed=False): diff --git a/confirm/models/ztest.py b/confirm/models/ztest.py index 99c44c90..b97dacfe 100644 --- a/confirm/models/ztest.py +++ b/confirm/models/ztest.py @@ -13,7 +13,7 @@ def _sim(samples, theta, null_truth): class ZTest1D: - 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a/research/cloud_explore/batch_cdk/run_confirm.py b/research/cloud_explore/batch_cdk/run_confirm.py index 799909bb..54167460 100644 --- a/research/cloud_explore/batch_cdk/run_confirm.py +++ b/research/cloud_explore/batch_cdk/run_confirm.py @@ -19,7 +19,7 @@ def f(): g = ip.cartesian_grid(theta_min=[-1], theta_max=[1], null_hypos=[ip.hypo("x0 < 0")]) print("Created grid in {:.2f} seconds".format(time.time() - start)) start = time.time() - iter, reports, ada = ip.ada_tune(ZTest1D, g=g, nB=5) + iter, reports, ada = ip.ada_calibrate(ZTest1D, g=g, nB=5) print("Ran ada in {:.2f} seconds".format(time.time() - start)) print(pd.DataFrame(reports)) diff --git a/research/cloud_explore/clickhouse/test.ipynb b/research/cloud_explore/clickhouse/test.ipynb index 72fe31d9..a1c3d086 100644 --- a/research/cloud_explore/clickhouse/test.ipynb +++ b/research/cloud_explore/clickhouse/test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -12,8 +12,8 @@ "import pandas as pd\n", "import time\n", "\n", - "host = keyring.get_password(\"clickhouse-confirm-host\", os.environ[\"USER\"])\n", - "password = keyring.get_password(\"clickhouse-confirm-password\", os.environ[\"USER\"])\n", + "host = keyring.get_password(\"clickhouse-confirm-test-host\", os.environ[\"USER\"])\n", + "password = keyring.get_password(\"clickhouse-confirm-test-password\", os.environ[\"USER\"])\n", "client = clickhouse_connect.get_client(\n", " host=host,\n", " port=8443,\n", @@ -1868,7 +1868,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:25:29) [Clang 14.0.6 ]" + "version": "3.10.8" }, "orig_nbformat": 4, "vscode": { diff --git a/research/cloud_explore/clickhouse/test.md b/research/cloud_explore/clickhouse/test.md index 99436dfc..e769c6f8 100644 --- a/research/cloud_explore/clickhouse/test.md +++ b/research/cloud_explore/clickhouse/test.md @@ -5,8 +5,8 @@ import keyring import pandas as pd import time -host = keyring.get_password("clickhouse-confirm-host", os.environ["USER"]) -password = keyring.get_password("clickhouse-confirm-password", os.environ["USER"]) +host = keyring.get_password("clickhouse-confirm-test-host", os.environ["USER"]) +password = keyring.get_password("clickhouse-confirm-test-password", os.environ["USER"]) client = clickhouse_connect.get_client( host=host, port=8443, diff --git a/research/cloud_explore/modal/bandwidth.py b/research/cloud_explore/modal/bandwidth.py new file mode 100644 index 00000000..80e5ffc0 --- /dev/null +++ b/research/cloud_explore/modal/bandwidth.py @@ -0,0 +1,30 @@ +import time + +import modal +import numpy as np + +stub = modal.Stub("bandwidthtest") + +volume = modal.SharedVolume().persist("bandwidthtest_data") + + +@stub.function(shared_volumes={"/data": volume}) +def save(): + start = time.time() + data = np.random.uniform(size=(256000, 350 * 4)) + print("generate", time.time() - start) + start = time.time() + np.save("/data/data.npy", data) + print("save", time.time() - start) + + +@stub.function(shared_volumes={"/data": volume}) +def load(i): + start = time.time() + np.load("/data/data.npy") + print("load", time.time() - start) + + +with stub.run(): + # save.call() + list(load.map(range(3))) diff --git a/research/cloud_explore/modal/main.py b/research/cloud_explore/modal/main.py index 13e5766b..53ce1c7c 100644 --- a/research/cloud_explore/modal/main.py +++ b/research/cloud_explore/modal/main.py @@ -45,7 +45,7 @@ def f(n): g = ip.cartesian_grid(theta_min=[-1], theta_max=[1], null_hypos=[ip.hypo("x0 < 0")]) print("Created grid in {:.2f} seconds".format(time.time() - start)) start = time.time() - iter, reports, ada = ip.ada_tune(ZTest1D, g=g, nB=5) + iter, reports, ada = ip.ada_calibrate(ZTest1D, g=g, nB=5) print("Ran ada in {:.2f} seconds".format(time.time() - start)) print(pd.DataFrame(reports)) diff --git a/research/design/binom1d.ipynb b/research/design/binom1d.ipynb index 78fb7da6..dc2272dc 100644 --- a/research/design/binom1d.ipynb +++ b/research/design/binom1d.ipynb @@ -186,7 +186,7 @@ } ], "source": [ - "iter, reports, ada = ip.ada_tune(Binom1D, g=g, model_kwargs=dict(n_arm_samples=10))" + "iter, reports, ada = ip.ada_calibrate(Binom1D, g=g, model_kwargs=dict(n_arm_samples=10))" ] } ], diff --git a/research/design/binom1d.md b/research/design/binom1d.md index c42bb8e1..0721d837 100644 --- a/research/design/binom1d.md +++ b/research/design/binom1d.md @@ -36,5 +36,5 @@ plt.show() ``` ```python -iter, reports, ada = ip.ada_tune(Binom1D, g=g, model_kwargs=dict(n_arm_samples=10)) +iter, reports, ada = ip.ada_calibrate(Binom1D, g=g, model_kwargs=dict(n_arm_samples=10)) ``` diff --git a/research/design/cache.ipynb b/research/design/cache.ipynb new file mode 100644 index 00000000..3b71bbb3 --- /dev/null +++ b/research/design/cache.ipynb @@ -0,0 +1,1038 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from confirm.outlaw.nb_util import setup_nb\n", + "setup_nb()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3185777777777778" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "256000 * 350 * 4 * 8 / 1e9 / 9" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from confirm.models.binom1d import Binom1D\n", + "from confirm.imprint.cache import DuckDBCache\n", + "c = DuckDBCache.connect()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.27 s, sys: 1.11 s, total: 3.39 s\n", + "Wall time: 1.97 s\n" + ] + } + ], + "source": [ + "%%time\n", + "m = Binom1D(0, 256000, n=350 * 4, cache=c)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading {\"f\": \"confirm.models.binom1d.unifs\", \"args\": [0], \"kwargs\": {\"shape\": \"(256000, 1400)\", \"dtype\": \"\"}}\n", + "CPU times: user 41.9 ms, sys: 109 ms, total: 151 ms\n", + "Wall time: 150 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "m = Binom1D(0, 256000, n=350 * 4, cache=c)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 466 ms, sys: 641 ms, total: 1.11 s\n", + "Wall time: 1.11 s\n" + ] + } + ], + "source": [ + "%%time\n", + "b = m.samples.tobytes()\n", + "c.set(\"test2\", pd.DataFrame(dict(data=[b])))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 99.5 ms, sys: 247 ms, total: 346 ms\n", + "Wall time: 346 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "arr = jnp.frombuffer(c.get('test2')['data'].iloc[0], dtype=np.float32).reshape((256000, 1400))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(256000, 1400)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(256000, 1400)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr.reshape((256000, 350 * 4)).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import struct\n", + "def fast_numpy_save(array):\n", + " size=len(array.shape)\n", + " return bytes(array.dtype.byteorder.replace('=','<' if sys.byteorder == 'little' else '>')+array.dtype.kind,'utf-8')+array.dtype.itemsize.to_bytes(1,byteorder='little')+struct.pack(f'\"}}\n", + "CPU times: user 1.19 s, sys: 663 ms, total: 1.85 s\n", + "Wall time: 1.85 s\n" + ] + } + ], + "source": [ + "%%time\n", + "m = Binom1D(0, 256000, n=350 * 4, cache=c)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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keytable_name
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" + ], + "text/plain": [ + " key table_name\n", + "0 abc _cache_cool_0" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c.con.execute('select * from cache_tables').df()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from confirm.outlaw.nb_util import setup_nb\n", + "\n", + "setup_nb()\n", + "\n", + "import io\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import confirm.imprint as ip" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'[0, {\"a\": [1, 2]}]'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "json.dumps((0, {'a': (1,2)}))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "\n", + "@jax.jit\n", + "def _sim(samples, theta, null_truth):\n", + " p = jax.scipy.special.expit(theta)\n", + " stats = jnp.sum(samples[None, :] < p[:, None], axis=2) / samples.shape[1]\n", + " return jnp.where(\n", + " null_truth[:, None, 0],\n", + " 1 - stats,\n", + " jnp.inf,\n", + " )\n", + "\n", + "\n", + "\n", + "\n", + "class Binom1D:\n", + "\n", + " @staticmethod\n", + " def unifs(seed, *, shape, dtype):\n", + " return jax.random.uniform(jax.random.PRNGKey(seed), shape=shape, dtype=dtype)\n", + "\n", + " def __init__(self, cache, seed, max_K, *, n):\n", + " self.family = \"binomial\"\n", + " self.family_params = {\"n\": n}\n", + " self.dtype = jnp.float32\n", + "\n", + " # cache_key = f'samples-{seed}-{max_K}-{n}-{self.dtype}'\n", + " # if cache_key in cache:\n", + " # self.samples = cache[cache_key]\n", + " # else:\n", + " # key = jax.random.PRNGKey(seed)\n", + " # self.samples = jax.random.uniform(key, shape=(max_K, n), dtype=self.dtype)\n", + " # cache.update({cache_key: self.samples})\n", + " #\n", + " self.samples = cache(Binom1D.unifs)(seed, shape=(max_K, n), dtype=self.dtype)\n", + "\n", + " def sim_batch(self, begin_sim, end_sim, theta, null_truth, detailed=False):\n", + " return _sim(self.samples[begin_sim:end_sim], theta, null_truth)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "class Cache:\n", + " def __init__(self):\n", + " self._cache = {}\n", + "\n", + " def __call__(self, func):\n", + " def wrapper(*args, **kwargs):\n", + " key = json.dumps(dict(\n", + " f=func.__module__ + '.' + func.__qualname__,\n", + " args=args, \n", + " kwargs={str(k):str(v) for k,v in kwargs.items()}\n", + " ))\n", + " if key in self._cache:\n", + " return self._cache[key]\n", + " else:\n", + " result = func(*args, **kwargs)\n", + " self._cache[key] = result\n", + " return result\n", + "\n", + " return wrapper" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import confirm.models.binom1d\n", + "c = Cache()\n", + "confirm.models.binom1d.Binom1D(c, 0, 100, n=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'f': 'confirm.models.binom1d.Binom1D.unifs',\n", + " 'args': [0],\n", + " 'kwargs': {'shape': '(100, 100)', 'dtype': \"\"}}" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pprint import pprint\n", + "json.loads(list(c._cache.keys())[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'{\"f\": \"confirm.models.binom1d.Binom1D.unifs\", \"args\": [0], \"kwargs\": {\"shape\": \"(100, 100)\", \"dtype\": \"\"}}'" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(c._cache.keys())[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import hashlib\n", + "import confirm\n", + "\n", + "\n", + "def hash_confirm_code():\n", + " confirm_path = os.path.dirname(confirm.__file__)\n", + " hashes = []\n", + " hash_md5 = hashlib.md5()\n", + " for path, subdirs, files in os.walk(confirm_path):\n", + " for fn in files:\n", + " if not fn.endswith(\".py\"):\n", + " continue\n", + " with open(os.path.join(path, fn), \"rb\") as f:\n", + " for chunk in iter(lambda: f.read(4096), b\"\"):\n", + " hash_md5.update(chunk)\n", + " hash_md5.hexdigest()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['../../confirm/lewislib/grid.py',\n", + " '../../confirm/lewislib/__init__.py',\n", + " '../../confirm/lewislib/lewis.py',\n", + " '../../confirm/lewislib/jax_wrappers.py',\n", + " '../../confirm/lewislib/table.py',\n", + " '../../confirm/berrylib/util.py',\n", + " '../../confirm/berrylib/quadrature.py',\n", + " '../../confirm/berrylib/constants.py',\n", + " '../../confirm/berrylib/__init__.py',\n", + " '../../confirm/berrylib/fast_inla.py',\n", + " '../../confirm/berrylib/dirty_bayes.py',\n", + " '../../confirm/berrylib/imprint.py',\n", + " '../../confirm/berrylib/mcmc.py',\n", + " '../../confirm/berrylib/batch_run.py',\n", + " '../../confirm/berrylib/fast_math.py',\n", + " '../../confirm/berrylib/binomial.py',\n", + " '../../confirm/models/ztest.py',\n", + " '../../confirm/models/__init__.py',\n", + " '../../confirm/models/fisher_exact.py',\n", + " '../../confirm/models/binom1d.py',\n", + " '../../confirm/outlaw/quad.py',\n", + " '../../confirm/outlaw/numpyro_interface.py',\n", + " '../../confirm/outlaw/inla.py',\n", + " '../../confirm/outlaw/nb_util.py',\n", + " '../../confirm/outlaw/__init__.py',\n", + " '../../confirm/outlaw/berry.py',\n", + " '../../confirm/outlaw/smalljax.py',\n", + " '../../confirm/outlaw/interp.py',\n", + " '../../confirm/cloud/clickhouse.py',\n", + " '../../confirm/cloud/__init__.py',\n", + " '../../confirm/cloud/awsbatch.py',\n", + " '../../confirm/cloud/modal_util.py',\n", + " '../../confirm/bound/multivariate_normal.py',\n", + " '../../confirm/bound/normal.py',\n", + " '../../confirm/bound/__init__.py',\n", + " '../../confirm/bound/binomial.py',\n", + " '../../confirm/imprint/db.py',\n", + " '../../confirm/imprint/grid.py',\n", + " '../../confirm/imprint/batching.py',\n", + " '../../confirm/imprint/checkpoint.py',\n", + " '../../confirm/imprint/adagrid.py',\n", + " '../../confirm/imprint/__init__.py',\n", + " '../../confirm/imprint/summary.py',\n", + " '../../confirm/imprint/testing.py',\n", + " '../../confirm/imprint/driver.py']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import glob\n", + "\n", + "glob.glob(\"../../confirm/**/*.py\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "cache = Cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 97.6 ms, sys: 6.93 ms, total: 105 ms\n", + "Wall time: 103 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "DeviceArray([[0.02379167, 0.8527204 , 0.8132185 , 0.5140263 , 0.17172801, 0.8026866 , 0.5124631 ,\n", + " 0.34838438, 0.50526905, 0.3370521 ],\n", + " [0.10868239, 0.10520637, 0.83827364, 0.78986526, 0.34059846, 0.8349273 , 0.24575627,\n", + " 0.21387374, 0.02423227, 0.5617423 ],\n", + " [0.28066766, 0.94366455, 0.61214995, 0.7383388 , 0.52419806, 0.65466726, 0.41012764,\n", + " 0.24028647, 0.74443066, 0.03544927],\n", + " [0.851014 , 0.02434528, 0.47239733, 0.72706807, 0.35055435, 0.6274171 , 0.61077535,\n", + " 0.06525731, 0.8091929 , 0.21307838],\n", + " [0.6465323 , 0.3245015 , 0.5538883 , 0.8849807 , 0.9591211 , 0.83856845, 0.48919427,\n", + " 0.11810577, 0.16933143, 0.83657074],\n", + " [0.587505 , 0.6867087 , 0.95522237, 0.5797727 , 0.28024232, 0.34749162, 0.5199702 ,\n", + " 0.9811766 , 0.5645981 , 0.2446456 ],\n", + " [0.68722725, 0.9616587 , 0.480047 , 0.88953114, 0.7083205 , 0.948612 , 0.67764974,\n", + " 0.44131804, 0.36789334, 0.95148766],\n", + " [0.30909216, 0.70908046, 0.8749926 , 0.60973287, 0.60751855, 0.22647333, 0.5363518 ,\n", + " 0.96195626, 0.08158326, 0.5266887 ],\n", + " [0.85922587, 0.648262 , 0.4657668 , 0.31623375, 0.43507564, 0.48351157, 0.41285944,\n", + " 0.73501325, 0.15267539, 0.67055714],\n", + " [0.08459568, 0.04527426, 0.21078384, 0.4654404 , 0.7363906 , 0.23245859, 0.22119188,\n", + " 0.99092937, 0.878675 , 0.4102913 ]], dtype=float32)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "unifs(0, shape=(10, 10), dtype=jnp.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "197 ms ± 4.66 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "model = Binom1D(cache, 0, 1000000, n=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{(,\n", + " (0,),\n", + " (('shape', (100, 1000)),\n", + " ('dtype',\n", + " jax.numpy.float32))): DeviceArray([[0.28537035, 0.32794476, 0.7018368 , 0.99795973, 0.80373716, 0.8437431 , 0.18751788,\n", + " 0.5537597 , 0.35716057, 0.633845 , ..., 0.9791453 , 0.98088837, 0.15622127,\n", + " 0.6792512 , 0.9664891 , 0.65000224, 0.5286546 , 0.06554615, 0.6344644 , 0.16242659],\n", + " [0.60463357, 0.6501305 , 0.34219182, 0.62337744, 0.5855551 , 0.823779 , 0.17784536,\n", + " 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0.451293 , 0.7998103 , 0.03633654, 0.92831016, 0.50967216]], dtype=float32)}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cache._cache" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## old" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Binom1D.__init__() missing 1 required positional argument: 'max_K'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn [3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m g \u001b[39m=\u001b[39m ip\u001b[39m.\u001b[39mcartesian_grid(theta_min\u001b[39m=\u001b[39m[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m], theta_max\u001b[39m=\u001b[39m[\u001b[39m1\u001b[39m], null_hypos\u001b[39m=\u001b[39m[ip\u001b[39m.\u001b[39mhypo(\u001b[39m\"\u001b[39m\u001b[39mx0 < 0\u001b[39m\u001b[39m\"\u001b[39m)])\n\u001b[0;32m----> 2\u001b[0m rej_df \u001b[39m=\u001b[39m ip\u001b[39m.\u001b[39;49mvalidate(Binom1D, g, \u001b[39m0.5\u001b[39;49m, K\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m\u001b[39m10\u001b[39;49m, model_kwargs\u001b[39m=\u001b[39;49m{\u001b[39m\"\u001b[39;49m\u001b[39mn\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m100\u001b[39;49m})\n\u001b[1;32m 3\u001b[0m rej_df\n", + "File \u001b[0;32m~/confirm/confirmasaurus/confirm/imprint/driver.py:196\u001b[0m, in \u001b[0;36mvalidate\u001b[0;34m(modeltype, g, lam, delta, model_seed, K, tile_batch_size, model_kwargs)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mvalidate\u001b[39m(\n\u001b[1;32m 160\u001b[0m modeltype,\n\u001b[1;32m 161\u001b[0m g,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 168\u001b[0m model_kwargs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m\n\u001b[1;32m 169\u001b[0m ):\n\u001b[1;32m 170\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[39m Calculate the Type I Error bound.\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[39m - tie_bound: The bound on the Type I error over the whole tile.\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 196\u001b[0m driver, g \u001b[39m=\u001b[39m _setup(modeltype, g, model_seed, K, model_kwargs, tile_batch_size)\n\u001b[1;32m 197\u001b[0m rej_df \u001b[39m=\u001b[39m driver\u001b[39m.\u001b[39mvalidate(g\u001b[39m.\u001b[39mdf, lam, delta\u001b[39m=\u001b[39mdelta)\n\u001b[1;32m 198\u001b[0m \u001b[39mreturn\u001b[39;00m rej_df\n", + "File \u001b[0;32m~/confirm/confirmasaurus/confirm/imprint/driver.py:155\u001b[0m, in \u001b[0;36m_setup\u001b[0;34m(modeltype, g, model_seed, K, model_kwargs, tile_batch_size)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[39mif\u001b[39;00m model_kwargs \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 154\u001b[0m model_kwargs \u001b[39m=\u001b[39m {}\n\u001b[0;32m--> 155\u001b[0m model \u001b[39m=\u001b[39m modeltype(model_seed, g\u001b[39m.\u001b[39;49mdf[\u001b[39m\"\u001b[39;49m\u001b[39mK\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mmax(), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mmodel_kwargs)\n\u001b[1;32m 156\u001b[0m \u001b[39mreturn\u001b[39;00m Driver(model, tile_batch_size\u001b[39m=\u001b[39mtile_batch_size), g\n", + "\u001b[0;31mTypeError\u001b[0m: Binom1D.__init__() missing 1 required positional argument: 'max_K'" + ] + } + ], + "source": [ + "g = ip.cartesian_grid(theta_min=[-1], theta_max=[1], null_hypos=[ip.hypo(\"x0 < 0\")])\n", + "rej_df = ip.validate(Binom1D, g, 0.5, K=2**10, model_kwargs={\"n\": 100})\n", + "rej_df" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "model = Binom1D(0, 2**18, n=100)\n", + "db = ip.db.DuckDB.connect()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 106 µs, sys: 11 µs, total: 117 µs\n", + "Wall time: 116 µs\n" + ] + } + ], + "source": [ + "%%time\n", + "samples = pd.DataFrame(model.samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14.3 ms ± 169 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "memfile = io.BytesIO()\n", + "np.save(memfile, samples.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.5 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "memfile.seek(0)\n", + "s2 = np.load(memfile)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all(s2 == samples.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "125 ms ± 1.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "db.con.execute(\"drop table samples\")\n", + "db.con.execute(\"create table samples as select * from samples\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.6 ('confirm')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "b4c6ec5b2d6c7b38df115d547b82cd53ca25eea58d87299956d35a9dc79f19f1" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/research/design/cache.md b/research/design/cache.md new file mode 100644 index 00000000..5d8e4d46 --- /dev/null +++ b/research/design/cache.md @@ -0,0 +1,281 @@ +```python +from confirm.outlaw.nb_util import setup_nb +setup_nb() +``` + +```python +256000 * 350 * 4 * 8 / 1e9 / 9 +``` + +```python +from confirm.models.binom1d import Binom1D +from confirm.imprint.cache import DuckDBCache +c = DuckDBCache.connect() +``` + +```python +%%time +m = Binom1D(0, 256000, n=350 * 4, cache=c) +``` + +```python +%%time +m = Binom1D(0, 256000, n=350 * 4, cache=c) +``` + +```python +import pandas as pd +import numpy as np +``` + +```python +%%time +b = m.samples.tobytes() +c.set("test2", pd.DataFrame(dict(data=[b]))) +``` + +```python +import jax.numpy as jnp +``` + +```python +%%time +arr = jnp.frombuffer(c.get('test2')['data'].iloc[0], dtype=np.float32).reshape((256000, 1400)) +``` + +```python +arr.shape +``` + +```python +arr.reshape((256000, 350 * 4)).shape +``` + +```python +import numpy as np +``` + +```python +import sys +import struct +def fast_numpy_save(array): + size=len(array.shape) + return bytes(array.dtype.byteorder.replace('=','<' if sys.byteorder == 'little' else '>')+array.dtype.kind,'utf-8')+array.dtype.itemsize.to_bytes(1,byteorder='little')+struct.pack(f'